Boole Microcap Fund

September 25, 2022

Nassim Nicholas Taleb  is the author of several books, including Fooled by Randomness, The Black Swan, and Antifragile.  I wrote about Fooled by Randomness here: https://boolefund.com/fooled-by-randomness/

Today’s blog post is a summary of Taleb’s The Black Swan.  If you’re an investor, or if you have any interest in predictions or in history, then this is a MUST-READ book.  One of Taleb’s main points is that Black Swans, which are unpredictable, can be either positive or negative.  It’s crucial to try to be prepared for negative Black Swans and to try to benefit from positive Black Swans.  However, many measurements of risk in finance assume a statistical distribution that is normal when they should assume a distribution that is fat-tailed.  These standard measures of risk won’t prepare you for a Black Swan.

That said, Taleb is an option trader, whereas I am a value investor.  For me, if you buy a stock far below liquidation value, then usually you have a margin of safety.  A group of such stocks will outperform the market over time while carrying low risk.  Furthermore, if you’re a long-term investor, then you can either adopt a value investing approach or you can simply invest in low-cost index funds.  Either way, given a long enough period of time, you should get good results.  The market has recovered from every crash and has eventually gone on to new highs.  Yet Taleb misses this point.

Nonetheless, although Taleb overlooks value investing and index funds, his views on predictions and on history are very insightful and should be studied by every thinking person.

Here’s the outline:

• • Prologue

PART ONE: UMBERTO ECO’S ANTILIBRARY, OR HOW WE SEEK VALIDATION

• • Chapter 1: The Apprenticeship of an Empirical Skeptic
  • Chapter 2: Yevgenia’s Black Swan
  • Chapter 3: The Speculator and the Prostitute
  • Chapter 4: One Thousand and One Days, or How Not to Be a Sucker
  • Chapter 5: Confirmation Schmonfirmation!
  • Chapter 6: The Narrative Fallacy
  • Chapter 7: Living in the Antechamber of Hope
  • Chapter 8: Giacomo Casanova’s Unfailing Luck: The Problem of Silent Evidence
  • Chapter 9: The Ludic Fallacy, or the Uncertainty of the Nerd

PART TWO: WE JUST CAN’T PREDICT

• • Chapter 10: The Scandal of Prediction
  • Chapter 11: How to Look for Bird Poop
  • Chapter 12: Epistemocracy, a Dream
  • Chapter 13: Apelles the Painter, or What Do You Do if You Cannot Predict?

PART THREE: THOSE GRAY SWANS OF EXTREMISTAN

• • Chapter 14: From Mediocristan to Extremistan, and Back
  • Chapter 15: The Bell Curve, That Great Intellectual Fraud
  • Chapter 16: The Aesthetics of Randomness
  • Chapter 17: Locke’s Madmen, or Bell Curves in the Wrong Places
  • Chapter 18: The Uncertainty of the Phony

PART FOUR: THE END

• • Chapter 19: Half and Half, or How to Get Even with the Black Swan

 

PROLOGUE

Taleb writes:

Before the discovery of Australia, people in the Old World were convinced that all swans were white, an unassailable belief as it seemed completely confirmed by empirical evidence… It illustrates a severe limitation to our learning from observations or experience and the fragility of our knowledge.  One single observation can invalidate a general statement derived from millenia of confirmatory sightings of millions of white swans.

Taleb defines a black swan as having three attributes:

• • First, it is an outlier, as it lies outside the realm of regular expectations, because nothing in the past can convincingly point to its possibility.
  • Second, it carries an extreme impact.
  • Third, in spite of its outlier status, human nature makes us concoct explanations for its occurrence after the fact, making it explainable and predictable.

Taleb notes that the effect of Black Swans has been increasing in recent centuries.  Furthermore, social scientists still assume that risks can be modeled using the normal distribution, i.e., the bell curve.  Social scientists have not incorporated “Fat Tails” into their assumptions about risk.  (A fat-tailed statistical distribution, as compared to a normal distribution, carries higher probabilities for extreme outliers.)

(Illustration by Peter Hermes Furian:  The red curve is a normal distribution, whereas the orange curve has fat tails.)

Taleb continues:

Black Swan logic makes what you don’t know far more relevant than what you do know.  Consider that many Black Swans can be caused and exacerbated by their being unexpected.

Taleb mentions the Sept. 11, 2001 terrorist attack on the twin towers.  If such an attack had been expected, then it would have been prevented.  Taleb:

Isn’t it strange to see an event happening precisely because it was not supposed to happen?  What kind of defense do we have against that? … It may be odd that, in such a strategic game, what you know can be truly inconsequential.

Taleb argues that Black Swan logic applies to many areas in business and also to scientific theories.  Taleb makes a general point about history:

The inability to predict outliers implies the inability to predict the course of history, given the share of these events in the dynamics of events.

Indeed, people, especially experts, have a terrible record in forecasting political and economic events.  Taleb advises:

Black Swans being unpredictable, we need to adjust to their existence (rather than naively try to predict them).  There are so many things we can do if we focus on antiknowledge, or what we do not know.  Among many other benefits, you can set yourself up to collect serendipitous Black Swans (of the positive kind) by maximizing your exposure to them.  Indeed, in some domains””such as scientific discovery and venture capital investments””there is a disproportionate payoff from the unknown, since you typically have little to lose and plenty to gain from a rare event… The strategy is, then, to tinker as much as possible and try to collect as many Black Swan opportunities as you can.

Taleb introduces the terms Platonicity and the Platonic fold:

Platonicity is what makes us think that we understand more than we actually do.  But this does not happen everywhere.  I am not saying that Platonic forms don’t exist.  Models and constructions, these intellectual maps of reality, are not always wrong; they are wrong only in some specific applications.  The difficulty is that a) you do not know before hand (only after the fact) where the map will be wrong, and b) the mistakes can lead to severe consequences…

The Platonic fold is the explosive boundary where the Platonic mindset enters in contact with messy reality, where the gap between what you know and what you think you know becomes dangerously wide.  It is here that the Black Swan is produced.

 

PART ONE: UMBERTO ECO’S ANTILIBRARY, OR HOW WE SEEK VALIDATION

Umberto Eco’s personal library contains thirty thousand books.  But what’s important are the books he has not yet read.  Taleb:

Read books are far less valuable than unread books.  The library should contain as much of what you do not know as your financial means, mortgage rates, and the currently tight real-estate market allow you to put there… Indeed, the more you know, the larger the rows of unread books.  Let us call this collection of unread books an antilibrary.

Taleb adds:

Let us call an antischolar””someone who focuses on the unread books, and makes an attempt not to treat his knowledge as a treasure, or even a possession, or even a self-esteem enhancement device””a skeptical empiricist.

(Photo by Pp1)

 

CHAPTER 1: THE APPRENTICESHIP OF AN EMPIRICAL SKEPTIC

Taleb says his family is from “the Greco-Syrian community, the last Byzantine outpost in northern Syria, which included what is now called Lebanon.”  Taleb writes:

People felt connected to everything they felt was worth connecting to; the place was exceedingly open to the world, with a vastly sophisticated lifestyle, a prosperous economy, and temperate weather just like California, with snow-covered mountains jutting above the Mediterranean.  It attracted a collection of spies (both Soviet and Western), prostitutes (blondes), writers, poets, drug dealers, adventurers, compulsive gamblers, tennis players, apres-skiers, and merchants””all professions that complement one another.

Taleb writes about when he was a teenager.  He was a “rebellious idealist” with an “ascetic taste.”  Taleb:

As a teenager, I could not wait to go settle in a metropolis with fewer James Bond types around.  Yet I recall something that felt special in the intellectual air.  I attended the French lycee that had one of the highest success rates for the French baccalaureat (the high school degree), even in the subject of the French language.  French was spoken there with some purity: as in prerevolutionary Russia, the Levantine Christian and Jewish patrician class (from Istanbul to Alexandria) spoke and wrote formal French as a language of distinction.  The most privileged were sent to school in France, as both my grandfathers were… Two thousand years earlier, by the same instinct of linguistic distinction, the snobbish Levantine patricians wrote in Greek, not the vernacular Aramaic… And, after Hellenism declined, they took up Arabic.  So in addition to being called a “paradise,” the place was also said to be a miraculous crossroads of what are superficially tagged “Eastern” and “Western” cultures.

Then a Black Swan hit:

The Lebanese “paradise” suddenly evaporated, after a few bullets and mortar shells… after close to thirteen centuries of remarkable ethnic coexistence, a Black Swan, coming out of nowhere, transformed the place from heaven to hell.  A fierce civil war began between Christians and Moslems, including the Palastinian refugees who took the Moslem side.  It was brutal, since the combat zones were in the center of town and most of the fighting took place in residential areas (my high school was only a few hundred feet from the war zone).  The conflict lasted more than a decade and a half.

Taleb makes a general point about history:

The human mind suffers from three ailments as it comes into contact with history, what I call the triplet of opacity.  They are:

• • the illusion of understanding, or how everyone thinks he knows what is going on in a world that is more complicated (or random) than they realize;
  • the retrospective distortion, or how we can assess matters only after the fact, as if they were in a rearview mirror (history seems clearer and more organized in history books than in empirical reality); and
  • the overvaluation of factual information and the handicap of authoritative and learned people, particularly when they create categories””when they “Platonify.”

Taleb points out that a diary is a good way to record events as they are happening.  This can help later to put events in their context.

Taleb writes about the danger of oversimplification:

Any reduction of the world around us can have explosive consequences since it rules out some sources of uncertainty; it drives us to a misunderstanding of the fabric of the world.  For instance, you may think that radical Islam (and its values) are your allies against the threat of Communism, and so you may help them develop, until they send two planes into downtown Manhattan.

 

CHAPTER 2: YEVGENIA’S BLACK SWAN

Taleb:

Five years ago, Yevgenia Nikolayevna Krasnova was an obscure and unpublished novelist, with an unusual background.  She was a neuroscientist with an interest in philosophy (her first three husbands had been philosophers), and she got it into her stubborn Franco-Russian head to express her research and ideas in literary form.

Most publishers largely ignored Yevgenia.  Publishers who did look at Yevnegia’s book were confused because she couldn’t seem to answer the most basic questions.  “Is this fiction or nonfiction?”  “Who is this book written for?”  (Five years ago, Yevgenia attended a famous writing workshop.  The instructor told her that her case was hopeless.)

Eventually the owner of a small unknown publishing house agreed to publish Yevgenia’s book.  Taleb:

It took five years for Yevnegia to graduate from the “egomaniac without anything to justify it, stubborn and difficult to deal with” category to “persevering, resolute, painstaking, and fiercely independent.”  For her book slowly caught fire, becoming one of the great and strange successes in literary history, selling millions of copies and drawing so-called critical acclaim…

Yevgenia’s book is a Black Swan.

 

 

CHAPTER 3: THE SPECULATOR AND THE PROSTITUTE

Taleb introduces Mediocristan and Extremistan:

Mediocristan	Extremistan
Nonscalable	Scalable
Mild or type 1 randomness	Wild (even superwild) type 2 randomness
The most typical member is mediocre	The most “typical” is either giant or dwarf, i.e., there is no typical member
Winners get a small segment of the total pie	Winner-take-almost-all effects
Example: Audience of an opera singer before the gramophone	Today’s audience for an artist
More likely to be found in our ancestral environment	More likely to be found in our modern environment
Impervious to the Black Swan	Vulnerable to the Black Swan
Subject to gravity	There are no physical constraints on what a number can be
Corresponds (generally) to physical quantities, i.e., height	Corresponds to numbers, say, wealth
As close to utopian equality as reality can spontaneously deliver	Dominated by extreme winner-take-all inequality
Total is not determined by a single instance or observation	Total will be determined by a small number of extreme events
When you observe for a while you can get to know what’s going on	It takes a long time to get to know what’s going on
Tyranny of the collective	Tyranny of the accidental
Easy to predict from what you see and extend to what you do not see	Hard to predict from past information
History crawls	History makes jumps
Events are distributed according to the “bell curve” or its variations	The distribution is either Mandelbrotian “gray” Swans (tractable scientifically) or totally intractable Black Swans

Taleb observes that Yevgenia’s rise from “the second basement to superstar” is only possible in Extremistan.

(Photo by Flavijus)

Taleb comments on knowledge and Extremistan:

What you can know from data in Mediocristan augments very rapidly with the supply of information.  But knowledge in Extremistan grows slowly and erratically with the addition of data, some of it extreme, possibly at an unknown rate.

Taleb gives many examples:

Matters that seem to belong to Mediocristan (subjected to what we call type 1 randomness): height, weight, calorie consumption, income for a baker, a small restaurant owner, a prostitute, or an orthodontist; gambling profits (in the very special case, assuming the person goes to a casino and maintains a constant betting size), car accidents, mortality rates, “IQ” (as measured).

Matters that seem to belong to Extremistan (subjected to what we call type 2 randomness): wealth, income, book sales per author, book citations per author, name recognition as a “celebrity,” number of references on Google, populations of cities, uses of words in a vocabulary, numbers of speakers per language, damage caused by earthquakes, deaths in war, deaths from terrorist incidents, sizes of planets, sizes of companies, stock ownership, height between species (consider elephants and mice), financial markets (but your investment manager does not know it), commodity prices, inflation rates, economic data.  The Extremistan list is much longer than the prior one.

Taleb concludes the chapter by introducing “gray” swans, which are rare and consequential, but somewhat predictable:

They are near-Black Swans.  They are somewhat tractable scientifically””knowing about their incidence should lower your surprise; these events are rare but expected.  I call this special case of “gray” swans Mandelbrotian randomness.  This category encompasses the randomness that produces phenomena commonly known by terms such as scalable, scale-invariant, power laws, Pareto-Zipf laws, Yule’s law, Paretian-stable processes, Levy-stable, and fractal laws, and we will leave them aside for now since they will be covered in some depth in Part Three…

You can still experience severe Black Swans in Mediocristan, though not easily.  How?  You may forget that something is random, think that it is deterministic, then have a surprise.  Or you can tunnel and miss on a source of uncertainty, whether mild or wild, owing to lack of imagination””most Black Swans result from this “tunneling” disease, which I will discuss in Chapter 9.

 

CHAPTER 4: ONE THOUSAND AND ONE DAYS, OR HOW NOT TO BE A SUCKER

Taleb introduces the Problem of Induction by using an example from the philosopher Bertrand Russell:

How can we logically go from specific instances to reach general conclusions?  How do we know what we know?  How do we know that what we have observed from given objects and events suffices to enable us to figure out their other properties?  There are traps built into any kind of knowledge gained from observation.

Consider a turkey that is fed every day.  Every single feeding will firm up the bird’s belief that it is a general rule of life to be fed every day by friendly members of the human race “looking out for its best interests,” as a politician would say.  On the afternoon of the Wednesday before Thanksgiving, something unexpected will happen to the turkey.  It will incur a revision of belief.

The rest of this chapter will outline the Black Swan problem in its original form: How can we know the future, given knowledge of the past; or, more generally, how can we figure out properties of the (infinite) unknown based on the (finite) known?

Taleb says that, as in the example of the turkey, the past may be worse than irrelevant.  The past may be “viciously misleading.”  The turkey’s feeling of safety reached its high point just when the risk was greatest.

Taleb gives the example of banking, which was seen and presented as “conservative,” based on the rarity of loans going bust.  However, you have to look at the loans over a very long period of time in order to see if a given bank is truly conservative.  Taleb:

In the summer of 1982, large American banks lost close to all their past earnings (cumulatively), about everything they ever made in the history of American banking””everything.  They had been lending to South and Central American countries that all defaulted at the same time”””an event of an exceptional nature”… They are not conservative; just phenomenally skilled at self-deception by burying the possibility of a large, devastating loss under the rug.  In fact, the travesty repeated itself a decade later, with the “risk-conscious” large banks once again under financial strain, many of them near-bankrupt, after the real-estate collapse of the early 1990s in which the now defunct savings and loan industry required a taxpayer-funded bailout of more than half a trillion dollars.

Taleb offers another example: the hedge fund Long-Term Capital Management (LTCM).  The fund calculated risk using the methods of two Nobel Prize-winning economists.  According to these calculations, risk of blowing up was infinitesimally small.  But in 1998, LTCM went bankrupt almost instantly.

A Black Swan is always relative to your expectations.  LTCM used science to create a Black Swan.

Taleb writes:

In general, positive Black Swans take time to show their effect while negative ones happen very quickly””it is much easier and much faster to destroy than to build.

Although the problem of induction is often called “Hume’s problem,” after the Scottish philosopher and skeptic David Hume, Taleb holds that the problem is older:

The violently antiacademic writer, and antidogma activist, Sextus Empiricus operated close to a millenium and a half before Hume, and formulated the turkey problem with great precision… We surmise that he lived in Alexandria in the second century of our era.  He belonged to a school of medicine called “empirical,” since its practitioners doubted theories and causality and relied on past experience as guidance in their treatment, though not putting much trust in it.  Furthermore, they did not trust that anatomy revealed function too obviously…

Sextus represented and jotted down the ideas of the school of the Pyrrhonian skeptics who were after some form of intellectual therapy resulting from the suspension of belief… The Pyrrhonian skeptics were docile citizens who followed customs and traditions whenever possible, but taught themselves to systematically doubt everything, and thus attain a level of serenity.  But while conservative in their habits, they were rabid in their fight against dogma.

Taleb asserts that his main aim is how not to be a turkey.

In a way, all I care about is making a decision without being the turkey.

Taleb introduces the themes for the next five chapters:

• • We focus on preselected segments of the seen and generalize from it to the unseen: the error of confirmation.
  • We fool ourselves with stories that cater to our Platonic thirst for distinct patterns: the narrative fallacy.
  • We behave as if the Black Swan does not exist: human nature is not programmed for Black Swans.
  • What we see is not necessarily all that is there.  History hides Black Swans from us and gives us a mistaken idea about the odds of these events: this is the distortion of silent evidence.
  • We “tunnel”: that is, we focus on a few well-defined sources of uncertainty, on too specific a list of Black Swans (at the expense of the others that do not easily come to mind).

 

CHAPTER 5: CONFIRMATION SHMONFIRMATION!

Taleb asks about two hypothetical situations.  First, he had lunch with O.J. Simpson and O.J. did not kill anyone during the lunch.  Isn’t that evidence that O.J. Simpson is not a killer?  Second, Taleb imagines that he took a nap on the railroad track in New Rochelle, New York.  He didn’t die during his nap, so isn’t that evidence that it’s perfectly safe to sleep on railroad tracks?  Of course, both of these situations are analogous to the 1,001 days during which the turkey was regularly fed.  Couldn’t the turkey conclude that there’s no evidence of any sort of Black Swan?

The problem is that people confuse no evidence of Black Swans with evidence of no possible Black Swans.  Just because there has been no evidence yet of any possible Black Swans does not mean that there’s evidence of no possible Black Swans.  Taleb calls this confusion the round-trip fallacy, since the two statements are not interchangeable.

Taleb writes that our minds routinely simplify matters, usually without our being consciously aware of it.  Note: In his book, Thinking, Fast and Slow, the psychologist Daniel Kahneman argues that System 1, our intuitive system, routinely oversimplifies, usually without our being consciously aware of it.

Taleb continues:

Many people confuse the statement “almost all terrorists are Moslems” with “almost all Moslems are terrorists.”  Assume that the first statement is true, that 99 percent of terrorists are Moslems.  This would mean that only about .001 percent of Moslems are terrorists, since there are more than one billion Moslems and only, say, ten thousand terrorists, one in a hundred thousand.  So the logical mistake makes you (unconsciously) overestimate the odds of a randomly drawn individual Moslem person… being a terrorist by close to fifty thousand times!

Taleb comments:

Knowledge, even when it is exact, does not often lead to appropriate actions because we tend to forget what we know, or forget how to process it properly if we do not pay attention, even when we are experts.

Taleb notes that the psychologists Daniel Kahneman and Amos Tversky did a number of experiments in which they asked professional statisticians statistical questions not phrased as statistical questions.  Many of these experts consistently gave incorrect answers.

Taleb explains:

This domain specificity of our inferences and reactions works both ways: some problems we can understand in their applications but not in textbooks; others we are better at capturing in the textbook than in the practical application.  People can manage to effortlessly solve a problem in a social situation but struggle when it is presented as an abstract logical problem.  We tend to use different mental machinery””so-called modules””in different situations: our brain lacks a central all-purpose computer that starts with logical rules and applies them equally to all possible situations.

Note: Again, refer to Daniel Kahneman’s book, Thinking, Fast and Slow.  System 1 is the fast-thinking intuitive system that works effortlessly and often subconsciously.  System 1 is often right, but sometimes very wrong.  System 2 is the logical-mathematical system that can be trained to do logical and mathematical problems.  System 2 is generally slow and effortful, and we’re fully conscious of what System 2 is doing because we have to focus our attention for it to operate. See: https://boolefund.com/cognitive-biases/

Taleb next writes:

An acronym used in the medical literature is NED, which stands for No Evidence of Disease.  There is no such thing as END, Evidence of No Disease.  Yet my experience discussing this matter with plenty of doctors, even those who publish papers on their results, is that many slip into the round-trip fallacy during conversation.

Doctors in the midst of the scientific arrogance of the 1960s looked down at mothers’ milk as something primitive, as if it could be replicated by their laboratories””not realizing that mothers’ milk might include useful components that could have eluded their scientific understanding””a simple confusion of absence of evidence of the benefits of mothers’ milk with evidence of absence of the benefits (another case of Platonicity as “it did not make sense” to breast-feed when we could simply use bottles).  Many people paid the price for this naive inference: those who were not breast-fed as infants turned out to be at an increased risk of a collection of health problems, including a higher likelihood of developing certain types of cancer””there had to be in mothers’ milk some necessary nutrients that still elude us.  Furthermore, benefits to mothers who breast-feed were also neglected, such as a reduction in the risk of breast cancer.

Taleb makes the following point:

I am not saying here that doctors should not have beliefs, only that some kinds of definitive, closed beliefs need to be avoided… Medicine has gotten better””but many kinds of knowledge have not.

Taleb defines naive empiricism:

By a mental mechanism I call naive empiricism, we have a natural tendency to look for instances that confirm our story and our vision of the world””these instances are always easy to find…

Taleb makes an important point here:

Even in testing a hypothesis, we tend to look for instances where the hypothesis proved true.

Daniel Kahneman has made the same point.  System 1 (intuition) automatically looks for confirmatory evidence, but even System 2 (the logical-mathematical-rational system) naturally looks for evidence that confirms a given hypothesis.  We have to train System 2 not only to do logic and math, but also to look for disconfirming rather than confirming evidence.  Taleb says:

We can get closer to the truth by negative instances, not by verification!  It is misleading to build a general rule from observed facts.  Contrary to conventional wisdom, our body of knowledge does not increase from a series of confirmatory observations, like the turkey’s.

Taleb adds:

Sometimes a lot of data can be meaningless; at other times one single piece of information can be very meaningful.  It is true that a thousand days cannot prove you right, but one day can prove you to be wrong.

Taleb introduces the philosopher Karl Popper and his method of conjectures and refutations.  First you develop a conjecture (hypothesis).  Then you focus on trying to refute the hypothesis.  Taleb:

If you think the task is easy, you will be disappointed””few humans have a natural ability to do this.  I confess that I am not one of them; it does not come naturally to me.

Our natural tendency, whether using System 1 or System 2, is to look only for corroboration.  This is called confirmation bias.

There are exceptions, notes Taleb.  Chess grand masters tend to look at where their move might be weak, whereas rookie chess players only look for confirmation.  Similarly, George Soros developed a unique ability to look always for evidence that his current hypothesis is wrong.  As a result of this and not getting attached to his opinions, Soros quickly exited many of his trades that wouldn’t have worked.  Soros is one of the most successful macro investors ever.

Taleb observes that seeing a red mini Cooper actually confirms the statement that all swans are white.  Why?  Because if all swans are white, then all nonwhite objects are not swans; in other words, the statement “if it’s a swan, then it’s white” is logically equivalent to the statement “if it’s not white, then it’s not a swan” (since all swans are white).  Taleb:

This argument, known as Hempel’s raven paradox, was rediscovered by my friend the (thinking) mathematician Bruno Dupire during one of our intense meditating walks in London””one of those intense walk-discussions, intense to the point of our not noticing the rain.  He pointed to a red Mini and shouted, “Look, Nassim, look!  No Black Swan!”

Again: Finding instances that confirm the statement “if it’s not white, then it’s not a swan” is logically equivalent to finding instances that confirm the statement “if it’s a swan, then it’s white.”  So consider all the objects that confirm the statement “if it’s not white, then it’s not a swan”:  red Mini’s, gray clouds, green cucumbers, yellow lemons, brown soil, etc.  The paradox is that we seem to gain ever more information about swans by looking at an infinite series of nonwhite objects.

Taleb concludes the chapter by noting that our brains evolved to deal with a much more primitive environment than what exists today, which is far more complex.

…the sources of Black Swans today have multiplied beyond measurability.  In the primitive environment they were limited to newly encountered wild animals, new enemies, and abrupt weather changes.  These events were repeatable enough for us to have built an innate fear of them.  This instinct to make inferences rather quickly, and to “tunnel” (i.e., focus on a small number of sources of uncertainty, or causes of known Black Swans) remains rather ingrained in us.  This instinct, in a word, is our predicament.

 

CHAPTER 6: THE NARRATIVE FALLACY

Taleb introduces the narrative fallacy:

We like stories, we like to summarize, and we like to simplify, i.e., to reduce the dimension of matters… The [narrative] fallacy is associated with our vulnerability to overinterpretation and our predilection for compact stories over raw truths.  It severely distorts our mental representation of the world; it is particularly acute when it comes to the rare event.

Taleb continues:

The narrative fallacy addresses our limited ability to look at sequences of facts without weaving an explanation into them, or, equivalently, forcing a logical link, an arrow of relationship, upon them.  Explanations bind facts together.  They make them all the more easily remembered; they help them make more sense.  Where this propensity can go wrong is when it increases our impression of understanding.

Taleb clarifies:

To help the reader locate himself: in studying the problem of induction in the previous chapter, we examined what could be inferred about the unseen, what lies outside our information set.  Here, we look at the seen, what lies within the information set, and we examine the distortions in the act of processing it.

Taleb observes that our brains automatically theorize and invent explanatory stories to explain facts.  It takes effort NOT to invent explanatory stories.

Taleb mentions post hoc rationalization.  In an experiment, women were asked to choose from among twelve pairs of nylon stockings the ones they preferred.  Then they were asked for the reasons for their choice.  The women came up with all sorts of explanations.  However, all the stockings were in fact identical.

Split-brain patients have no connection between the left and right hemispheres of their brains.  Taleb:

Now, say that you induced such a person to perform an act””raise his finger, laugh, or grab a shovel””in order to ascertain how how he ascribes a reason to his act (when in fact you know that there is no reason for it other than your inducing it).  If you ask the right hemisphere, here isolated from the left side, to perform the action, then ask the other hemisphere for an explanation, the patient will invariably offer some interpretation: “I was pointing at the ceiling in order to…,” “I saw something interesting on the wall”…

Now, if you do the opposite, namely instruct the isolated left hemisphere of a right-handed person to perform an act and ask the right hemisphere for the reasons, you will be plainly told “I don’t know.”

Taleb notes that the left hemisphere deals with pattern recognition.  (But, in general, Taleb warns against the common distinctions between the left brain and the right brain.)

Taleb gives another example.  Read the following:

A BIRD IN THE

THE HAND IS WORTH

TWO IN THE BUSH

Notice anything unusual?  Try reading it again.  Taleb:

The Sydney-based brain scientist Alan Snyder… made the following discovery.  If you inhibit the left hemisphere of a right-handed person (more technically, by directing low-frequency magnetic pulses into the left frontotemporal lobes), you will lower his rate of error in reading the above caption.  Our propensity to impose meaning and concepts blocks our awareness of the details making up the concept.  However, if you zap people’s left hemispheres, they become more realistic””they can draw better and with more verisimilitude.  Their minds become better at seeing the objects themselves, cleared of theories, narratives, and prejudice.

Again, System 1 (intuition) automatically invents explanatory stories.  System 1 automatically finds patterns, even when none exist.

Moreover, neurotransmitters, chemicals thought to transport signals between different parts of the brain, play a role in the narrative fallacy.  Taleb:

It appears that pattern perception increases along with the concentration in the brain of the chemical dopamine.  Dopamine also regulates moods and supplies an internal reward system in the brain (not surprisingly, it is found in slightly higher concentrations in the left side of the brains of right-handed persons than on the right side).  A higher concentration of dopamine appears to lower skepticism and result in greater vulnerability to pattern detection;  an injection of L-dopa, a substance used to treat patients with Parkinson’s disease, seems to increase such activity and lowers one’s suspension of belief.  The person becomes vulnerable to all manner of fads…

Our memory of the past is impacted by the narrative fallacy:

Narrativity can viciously affect the remembrance of past events as follows: we will tend to more easily remember those facts from our past that fit a narrative, while we tend to neglect others that do not appear to play a causal role in that narrative.  Consider that we recall events in our memory all the while knowing the answer of what happened subsequently.  It is literally impossible to ignore posterior information when solving a problem.  This simple inability to remember not the true sequence of events but a reconstructed one will make history appear in hindsight to be far more explainable than it actually was””or is.

Taleb again:

So we pull memories along causative lines, revising them involuntarily and unconsciously.  We continuously renarrate past events in the light of what appears to make what we think of as logical sense after these events occur.

One major problem in trying to explain and predict the facts is that the facts radically underdetermine the hypotheses that logically imply those facts.  For any given set of facts, there exist many theories that can explain and predict those facts.  Taleb:

In a famous argument, the logician W.V. Quine showed that there exist families of logically consistent interpretations and theories that can match a given set of facts.  Such insight should warn us that mere absence of nonsense may not be sufficient to make something true.

There is a way to escape the narrative fallacy.  Develop hypotheses and then run experiments that test those hypotheses.  Whichever hypotheses best explain and predict the phenomena in question can be provisionally accepted.

The best hypotheses are only provisionally true and they are never uniquely true.  The history of science shows that nearly all hypotheses, no matter how well-supported by experiments, end up being supplanted.  Odds are high that the best hypotheses of today””including general relativity and quantum mechanics””will be supplanted at some point in the future.  For example, perhaps string theory will be developed to the point where it can predict the phenomena in question with more accuracy and with more generality than both general relativity and quantum mechanics.

Taleb continues:

Let us see how narrativity affects our understanding of the Black Swan.  Narrative, as well as its associated mechanism of salience of the sensational fact, can mess up our projection of the odds.  Take the following experiment conducted by Kahneman and Tversky… : the subjects were forecasting professionals who were asked to imagine the following scenarios and estimate their odds.

Which is more likely?

• • A massive flood somewhere in America in which more than a thousand people die.
  • An earthquake in California, causing massive flooding, in which more than a thousand people die.

Most of the forecasting professionals thought that the second scenario is more likely than the first scenario.  But logically, the second scenario is a subset of the first scenario and is therefore less likely.  It’s the vividness of the second scenario that makes it appear more likely.  Again, in trying to understand these scenarios, System 1 can much more easily imagine the second scenario and so automatically views it as more likely.

Next Taleb defines two kinds of Black Swan:

…there are two varieties of rare events: a) the narrated Black Swans, those that are present in the current discourse and that you are likely to hear about on television, and b) those nobody talks about, since they escape models””those that you would feel ashamed discussing in public because they do not seem plausible.  I can safely say that it is entirely compatible with human nature that the incidences of Black Swans would be overestimated in the first case, but severely underestimated in the second one.

 

CHAPTER 7: LIVING IN THE ANTECHAMBER OF HOPE

Taleb explains:

Let us separate the world into two categories.  Some people are like the turkey, exposed to a major blowup without being aware of it, while others play reverse turkey, prepared for big events that might surprise others.  In some strategies and life situations, you gamble dollars to win a succession of pennies while appearing to be winning all the time.  In others, you risk a succession of pennies to win dollars.  In other words, you bet either that the Black Swan will happen or that it will never happen, two strategies that require completely different mind-sets.

Taleb adds:

So some matters that belong to Extremistan are extremely dangerous but do not appear to be so beforehand, since they hide and delay their risks””so suckers think they are “safe.”  It is indeed a property of Extremistan to look less risky, in the short run, than it really is.

Taleb describes a strategy of betting on the Black Swan:

…some business bets in which one wins big but infrequently, yet loses small but frequently, are worth making if others are suckers for them and if you have the personal and intellectual stamina.  But you need such stamina.  You also need to deal with people in your entourage heaping all manner of insult on you, much of it blatant.  People often accept that a financial strategy with a small chance of success is not necessarily a bad one as long as the success is large enough to justify it.  For a spate of psychological reasons, however, people have difficulty carrying out such a strategy, simply because it requires a combination of belief, a capacity for delayed gratification, and the willingness to be spat upon by clients without blinking.

 

CHAPTER 8: GIACOMO CASANOVA’S UNFAILING LUCK: THE PROBLEM OF SILENT EVIDENCE

Taleb:

Another fallacy in the way we understand events is that of silent evidence.  History hides both Black Swans and its Black Swan-generating ability from us.

Taleb tells the story of the drowned worshippers:

More than two thousand years ago, the Roman orator, belletrist, thinker, Stoic, manipulator-politician, and (usually) virtuous gentleman, Marcus Tullius Cicero, presented the following story.  One Diagoras, a nonbeliever in the gods, was shown painted tablets bearing the portraits of some worshippers who prayed, then survived a subsequent shipwreck.  The implication was that praying protects you from drowning.  Diagoras asked, “Where were the pictures of those who prayed, then drowned?”

This is the problem of silent evidence.  Taleb again:

As drowned worshippers do not write histories of their experiences (it is better to be alive for that), so it is with the losers in history, whether people or ideas.

Taleb continues:

The New Yorker alone rejects close to a hundred manuscripts a day, so imagine the number of geniuses that we will never hear about.  In a country like France, where more people write books while, sadly, fewer people read them, respectable literary publishers accept one in ten thousand manuscripts they receive from first-time authors.  Consider the number of actors who have never passed an audition but would have done very well had they had that lucky break in life.

Luck often plays a role in whether someone becomes a millionaire or not.  Taleb notes that many failures share the traits of the successes:

Now consider the cemetery.  The graveyard of failed persons will be full of people who shared the following traits: courage, risk taking, optimism, et cetera.  Just like the population of millionaires.  There may be some differences in skills, but what truly separates the two is for the most part a single factor: luck.  Plain luck.

Of course, there’s more luck in some professions than others.  In investment management, there’s a great deal of luck.  One way to see this is to run computer simulations.  You can see that by luck alone, if you start out with 10,000 investors, you’ll end up with a handful of investors who beat the market for 10 straight years.

(Photo by Volodymyr Pyndyk)

Taleb then gives another example of silent evidence.  He recounts reading an article about the growing threat of the Russian Mafia in the United States.  The article claimed that the toughness and brutality of these guys were because they were strengthened by their Gulag experiences.  But were they really strengthened by their Gulag experiences?

Taleb asks the reader to imagine gathering a representative sample of the rats in New York.  Imagine that Taleb subjects these rats to radiation.  Many of the rats will die.  When the experiment is over, the surviving rats will be among the strongest of the whole sample.  Does that mean that the radiation strengthened the surviving rats?  No.  The rats survived because they were stronger.  But every rat will have been weakened by the radiation.

Taleb offers another example:

Does crime pay?  Newspapers report on the criminal who get caught.  There is no section in The New York Times recording the stories of those who committed crimes but have not been caught.  So it is with cases of tax evasion, government bribes, prostitution rings, poisoning of wealthy spouses (with substances that do not have a name and cannot be detected), and drug trafficking.

In addition, our representation of the standard criminal might be based on the properties of those less intelligent ones who were caught.

Taleb next writes about politicians promising “rebuilding” New Orleans after Hurricane Katrina:

Did they promise to do so with the own money?  No.  It was with public money.  Consider that such funds will be taken away from somewhere else… That somewhere else will be less mediatized.  It may be… cancer research… Few seem to pay attention to the victims of cancer lying lonely in a state of untelevised depression.  Not only do these cancer patients not vote (they will be dead by the next ballot), but they do not manifest themselves to our emotional system.  More of them die every day than were killed by Hurricane Katrina; they are the ones who need us the most””not just our financial help, but our attention and kindness.  And they may be the ones from whom the money will be taken””indirectly, perhaps even directly.  Money (public or private) taken away from research might be responsible for killing them””in a crime that may remain silent.

Giacomo Casanova was an adventurer who seemed to be lucky.  However, there have been plenty of adventurers, so some are bound to be lucky.  Taleb:

The reader can now see why I use Casanova’s unfailing luck as a generalized framework for the analysis of history, all histories.  I generate artificial histories featuring, say, millions of Giacomo Casanovas, and observe the difference between the attributes of the successful Casanovas (because you generate them, you know their exact properties) and those an observer of the result would obtain.  From that perspective, it is not a good idea to be a Casanova.

 

CHAPTER 9: THE LUDIC FALLACY, OR THE UNCERTAINTY OF THE NERD

Taleb introduces Fat Tony (from Brooklyn):

He started as a clerk in the back office of a New York bank in the early 1980s, in the letter-of-credit department.  He pushed papers and did some grunt work.  Later he grew into giving small business loans and figured out the game of how you can get financing from the monster banks, how their bureaucracies operate, and what they like to see on paper.  All the while an employee, he started acquiring property in bankruptcy proceedings, buying it from financial institutions.  His big insight is that bank employees who sell you a house that’s not theirs just don’t care as much as the owners; Tony knew very rapidly how to talk to them and maneuver.  Later, he also learned to buy and sell gas stations with money borrowed from small neighborhood bankers.

…Tony’s motto is “Finding who the sucker is.”  Obviously, they are often the banks: “The clerks don’t care about nothing.”  Finding these suckers is second nature to him.

Next Taleb introduces non-Brooklyn John:

Dr. John is a painstaking, reasoned, and gentle fellow.  He takes his work seriously, so seriously that, unlike Tony, you can see a line in the sand between his working time and his leisure activities.  He has a PhD in electrical engineering from the University of Texas at Austin.  Since he knows both computers and statistics, he was hired by an insurance company to do computer simulations; he enjoys the business.  Much of what he does consists of running computer programs for “risk management.”

Taleb imagines asking Fat Tony and Dr. John the same question: Assume that a coin is fair.  Taleb flips the coin ninety-nine times and gets heads each time.  What are the odds that the next flip will be tails?

(Photo by Christian Delbert)

Because he assumes a fair coin and the flips are independent, Dr. John answers one half (fifty percent).  Fat Tony answers, “I’d say no more than 1 percent, of course.”  Taleb questions Fat Tony’s reasoning.  Fat Tony explains that the coin must be loaded.  In other words, it is much more likely that the coin is loaded than that Taleb got ninety-nine heads in a row flipping a fair coin.

Taleb explains:

Simply, people like Dr. John can cause Black Swans outside Mediocristan””their minds are closed.  While the problem is very general, one of its nastiest illusions is what I call the ludic fallacy””the attributes of the uncertainty we face in real life have little connection to the sterilized ones we encounter in exams and games.

Taleb was invited by the United States Defense Department to a brainstorming session on risk.  Taleb was somewhat surprised by the military people:

I came out of the meeting realizing that only military people deal with randomness with genuine, introspective intellectual honesty””unlike academics and corporate executives using other people’s money.  This does not show in war movies, where they are usually portrayed as war-hungry autocrats.  The people in front of me were not the people who initiate wars.  Indeed, for many, the successful defense policy is the one that manages to eliminate potential dangers without war, such as the strategy of bankrupting the Russians through the escalation in defense spending.  When I expressed my amazement to Laurence, another finance person who was sitting next to me, he told me that the military collected more genuine intellects and risk thinkers than most if not all other professions.  Defense people wanted to understand the epistemology of risk.

Taleb notes that the military folks had their own name for a Black Swan: unknown unknown.  Taleb came to the meeting prepared to discuss a new phrase he invented: the ludic fallacy, or the uncertainty of the nerd.

(Photo by Franky44)

In the casino you know the rules, you can calculate the odds, and the type of uncertainty we encounter there, we will see later, is mild, belonging to Mediocristan.  My prepared statement was this: “The casino is the only human venture I know where the probabilities are known, Gaussian (i.e., bell-curve), and almost computable.”…

In real life you do not know the odds; you need to discover them, and the sources of uncertainty are not defined.

Taleb adds:

What can be mathematized is usually not Gaussian, but Mandelbrotian.

What’s fascinating about the casino where the meeting was held is that the four largest losses incurred (or narrowly avoided) had nothing to do with gambling.

• • First, they lost around $100 million when an irreplaceable performer in their main show was maimed by a tiger.
  • Second, a disgruntled contractor was hurt during the construction of a hotel annex.  He was so offended by the settlement offered him that he made an attempt to dynamite the casino.
  • Third, a casino employee didn’t file required tax forms for years.  The casino ended up paying a huge fine (which was the least bad alternative).
  • Fourth, there was a spate of other dangerous scenes, such as the kidnapping of the casino owner’s daughter, which caused him, in order to secure cash for the ransom, to violate gambling laws by dipping into the casino coffers.

Taleb draws a conclusion about the casino:

A back-of-the-envelope calculation shows that the dollar value of these Black Swans, the off-model hits and potential hits I’ve just outlined, swamp the on-model risks by a factor of close to 1,000 to 1.  The casino spent hundreds of millions of dollars on gambling theory and high-tech surveillance while the bulk of their risks came from outside their models.

All this, and yet the rest of the world still learns about uncertainty and probability from gambling examples.

Taleb wraps up Part One of his book:

We love the tangible, the confirmation, the palpable, the real, the visible, the concrete, the known, the seen, the vivid, the visual, the social, the embedded, the emotionally laden, the salient, the stereotypical, the moving, the theatrical, the romanced, the cosmetic, the official, the scholarly-sounding verbiage (b******t), the pompous Gaussian economist, the mathematicized crap, the pomp, the Academie Francaise, Harvard Business School, the Nobel Prize, dark business suits with white shirts and Ferragamo ties, the moving discourse, and the lurid.  Most of all we favor the narrated.

Alas, we are not manufactured, in our current edition of the human race, to understand abstract matters””we need context.  Randomness and uncertainty are abstractions.  We respect what had happened, ignoring what could have happened.

 

PART TWO: WE JUST CAN’T PREDICT

Taleb:

…the gains in our ability to model (and predict) the world may be dwarfed by the increases in its complexity””implying a greater and greater role for the unpredicted.

 

CHAPTER 10: THE SCANDAL OF PREDICTION

Taleb highlights the story of the Sydney Opera House:

The Sydney Opera House was supposed to open in early 1963 at a cost of AU$ 7 million.  It finally opened its doors more than ten years later, and, although it was a less ambitious version than initially envisioned, it ended up costing around AU$ 104 million.

Taleb then asks:

Why on earth do we predict so much?  Worse, even, and more interesting: Why don’t we talk about our record in predicting?  Why don’t we see how we (almost) always miss the big events?  I call this the scandal of prediction.

The problem is that when our knowledge grows, our confidence about how much we know generally increases even faster.

Try the following quiz.  For each question, give a range that you are 90 percent confident contains the correct answer.

• • What was Martin Luther King, Jr.’s age at death?
  • What is the length of the Nile river, in miles?
  • How many countries belong to OPEC?
  • How many books are there in the Old Testament?
  • What is the diameter of the moon, in miles?
  • What is the weight of an empty Boeing 747, in pounds?
  • In what year was Mozart born?
  • What is the gestation period of an Asian elephant, in days?
  • What is the air distance from London to Tokyo, in miles?
  • What is the deepest known point in the ocean, in feet?

If you’re not overconfident, then you should have gotten nine out of ten questions right because you gave a 90 percent confidence interval for each question.  However, most people get more than one question wrong, which means most people are overconfident.

(Answers:  39, 4132, 12, 39, 2158.8, 390000, 1756, 645, 5959, 35994.)

A similar quiz is to randomly select some number, like the population of Egypt, and then ask 100 random people to give their 98 percent confidence interval.  “I am 98 percent confident that the population of Egypt is between 40 million and 120 million.”  If the 100 random people are not overconfident, then 98 out of 100 should get the question right.  In practice, however, it turns out that a high number (15 to 30 percent) get the wrong answer.  Taleb:

This experiment has been replicated dozens of times, across populations, professions, and cultures, and just about every empirical psychologist and decision theorist has tried it on his class to show his students the big problem of humankind: we are simply not wise enough to be trusted with knowledge.  The intended 2 percent error rate usually turns out to be between 15 percent and 30 percent, depending on the population and the subject matter.

I have tested myself and, sure enough, failed, even while consciously trying to be humble by carefully setting a wide range… Yesterday afternoon, I gave a workshop in London… I decided to make a quick experiment during my talk.

I asked the participants to take a stab at a range for the number of books in Umberto Eco’s library, which, as we know from the introduction to Part One, contains 30,000 volumes.  Of the sixty attendees, not a single one made the range wide enough to include the actual number (the 2 percent error rate became 100 percent).

Taleb argues that guessing some quantity you don’t know and making a prediction about the future are logically similar.  We could ask experts who make predictions to give a confidence interval and then track over time how accurate their predictions are compared to the confidence interval.

Taleb continues:

The problem is that our ideas are sticky: once we produce a theory, we are not likely to change our minds””so those who delay developing their theories are better off.  When you develop your opinions on the basis of weak evidence, you will have difficulty interpreting subsequent information that contradicts these opinions, even if this new information is obviously more accurate.  Two mechanisms are at play here: …confirmation bias… and belief perseverance [also called consistency bias], the tendency not to reverse opinions you already have.  Remember that we treat ideas like possessions, and it will be hard for us to part with them.

…the more detailed knowledge one gets of empirical reality, the more one will see the noise (i.e., the anecdote) and mistake it for actual information.  Remember that we are swayed by the sensational.

Taleb adds:

…in another telling experiment, the psychologist Paul Slovic asked bookmakers to select from eighty-eight variables in past horse races those that they found useful in computing the odds.  These variables included all manner of statistical information about past performances.  The bookmakers were given the ten most useful variables, then asked to predict the outcome of races.  Then they were given ten more and asked to predict again.  The increase in the information set did not lead to an increase in their accuracy; their confidence in their choices, on the other hand, went up markedly.  Information proved to be toxic.

When it comes to dealing with experts, many experts do have a great deal of knowledge.  However, most experts have a high error rate when it comes to making predictions.  Moreover, many experts don’t even keep track of how accurate their predictions are.

Another way to think about the problem is to try to distinguish those with true expertise from those without it.  Taleb:

• • Experts who tend to be experts: livestock judges, astronomers, test pilots, soil judges, chess masters, physicists, mathematicians (when they deal with mathematical problems, not empirical ones), accountants, grain inspectors, photo interpreters, insurance analysts (dealing with bell curve-style statistics).
  • Experts who tend to be… not experts: stockbrokers, clinical psychologists, psychiatrists, college admissions officers, court judges, councilors, personnel selectors, intelligence analysts… economists, financial forecasters, finance professors, political scientists, “risk experts,” Bank for International Settlements staff, august members of the International Association of Financial Engineers, and personal financial advisors.

Taleb comments:

You cannot ignore self-delusion.  The problem with experts is that they do not know what they do not know.  Lack of knowledge and delusion about the quality of you knowledge come together””the same process that makes you know less also makes you satisfied with your knowledge.

Taleb asserts:

Our predictors may be good at predicting the ordinary, but not the irregular, and this is where they ultimately fail.  All you need to do is miss one interest-rates move, from 6 percent to 1 percent in a longer-term projection (what happened between 2000 and 2001) to have all your subsequent forecasts rendered completely ineffectual in correcting your cumulative track record.  What matters is not how often you are right, but how large your cumulative errors are.

And these cumulative errors depend largely on the big surprises, the big opportunities.  Not only do economic, financial, and political predictors miss them, but they are quite ashamed to say anything outlandish to their clients””and yet events, it turns out, are almost always outlandish.  Furthermore… economic forecasters tend to fall closer to one another than to the resulting outcome.  Nobody wants to be off the wall.

Taleb notes a paper that analyzed two thousand predictions by brokerage-house analysts.  These predictions didn’t predict anything at all.  You could have done about as well by naively extrapolating the prior period to the next period.  Also, the average difference between the forecasts was smaller than the average error of the forecasts.  This indicates herding.

Taleb then writes about the psychologist Philip Tetlock’s research.  Tetlock analyzed twenty-seven thousand predictions by close to three hundred specialists.  The predictions took the form of more of x, no change in x, or less of x.  Tetlock found that, on the whole, these predictions by experts were little better than chance.  You could have done as well by rolling a dice.

Tetlock worked to discover why most expert predictors did not realize that they weren’t good at making predictions.  He came up with several methods of belief defense:

• • You tell yourself that you were playing a different game.  Virtually no social scientist predicted the fall of the Soviet Union.  You argue that the Russians had hidden the relevant information.  If you’d had enough information, you could have predicted the fall of the Soviet Union.  “It is not your skills that are to blame.”
  • You invoke the outlier.  Something happened that was outside the system.  It was a Black Swan, and you are not supposed to predict Black Swans.  Such events are “exogenous,” coming from outside your science.  The model was right, it worked well, but the game turned out to be a different one than anticipated.
  • The “almost right” defense.  Retrospectively, it is easy to feel that it was a close call.

Taleb writes:

We attribute our successes to our skills, and our failures to external events outside our control, namely to randomness… This causes us to think that we are better than others at whatever we do for a living.  Nine-four percent of Swedes believe that their driving skills  put them in the top 50 percent of Swedish drivers; 84 percent of Frenchmen feel that their lovemaking abilities put them in the top half of French lovers.

Taleb observes that we tend to feel a little unique, unlike others.  If we get married, we don’t consider divorce a possibility.  If we buy a house, we don’t think we’ll move.  People who lose their job often don’t expect it.  People who try drugs don’t think they’ll keep doing it for long.

Taleb says:

Tetlock distinguishes between two types of predictors, the hedgehog and the fox, according to a distinction promoted by the essayist Isaiah Berlin.  As in Aesop’s fable, the hedgehog knows one thing, the fox knows many things… Many of the prediction failures come from hedgehogs who are mentally married to a single big Black Swan event, a big bet that is not likely to play out.  The hedgehog is someone focusing on a single, improbable, and consequential event, falling for the narrative fallacy that makes us so blinded by one single outcome that we cannot imagine others.

Taleb makes it clear that he thinks we should be foxes, not hedgehogs.  Taleb has never tried to predict specific Black Swans.  Rather, he wants to be prepared for whatever might come.  That’s why it’s better to be a fox than a hedgehog.  Hedgehogs are much worse, on the whole, at making predictions than foxes are.

Taleb mentions a study by Spyros Makridakis and Michele Hibon of predictions made using econometrics.  They discovered that “statistically sophisticated or complex methods” are not clearly better than simpler ones.

Projects usually take longer and are more expensive than most people think.  For instance, students regularly underestimate how long it will take them to complete a class project.  Taleb then adds:

With projects of great novelty, such as a military invasion, an all-out war, or something entirely new, errors explode upward.  In fact, the more routine the task, the better you learn to forecast.  But there is always something nonroutine in our modern environment.

Taleb continues:

…we are too focused on matters internal to the project to take into account external uncertainty, the “unknown unknown,” so to speak, the contents of the unread books.

Another important bias to understand is anchoring.  The human brain, relying on System 1, will grab on to any number, no matter how random, as a basis for guessing some other quantity.  For example, Kahneman and Tversky spun a wheel of fortune in front of some people.  What the people didn’t know was that the wheel was pre-programmed to either stop at “10” or “65.”  After the wheel stopped, people were asked to write down their guess of the number of African countries in the United Nations.  Predictably, those who saw “10” guessed a much lower number (25% was the average guess) than those who saw “65” (45% was the average guess).

Next, Taleb points out that life expectancy is from Mediocristan.  It is not scalable.  The longer we live, the less long we are expected to live.  By contrast, projects and ventures tend to be scalable.  The longer we have waited for some project to be completed, the longer we can be expected to have to wait from that point forward.

Taleb gives the example of a refugee waiting to return to his or her homeland.  The longer the refugee has waited so far, the longer they should expect to have to wait going forward.  Furthermore, consider wars: they tend to last longer and kill more people than expected.  The average war may last six months, but if your war has been going on for a few years, expect at least a few more years.

Taleb argues that corporate and government projections have an obvious flaw: they do not include an error rate.  There are three fallacies involved:

• • The first fallacy: variability matters.  For planning purposes, the accuracy of your forecast matters much more than the forecast itself, observes Taleb.  Don’t cross a river if it is four feet deep on average.  Taleb gives another example.  If you’re going on a trip to a remote location, then you’ll pack different clothes if it’s supposed to be seventy degrees Fahrenheit with an expected error rate of forty degrees than if the margin of error was only five degrees.  “The policies we need to make decisions on should depend far more on the range of possible outcomes than on the expected final number.”
  • The second fallacy lies in failing to take into account forecast degradation as the projected period lengthens… Our forecast errors have traditionally been enormous…
  • The third fallacy, and perhaps the gravest, concerns a misunderstanding of the random character of the variables being forecast.  Owing to the Black Swan, these variables can accomodate far more optimistic””or far more pessimistic””scenarios than are currently expected.

Taleb points out that, as in the case of the depth of the river, what matters even more than the error rate is the worst-case scenario.

A Black Swan has three attributes: unpredictability, consequences, and retrospective explainability.  Taleb next examines unpredictability.

 

CHAPTER 11: HOW TO LOOK FOR BIRD POOP

Taleb notes that most discoveries are the product of serendipity.

Taleb writes:

Take this dramatic example of a serendipitous discovery.  Alexander Fleming was cleaning up his laboratory when he found that penicillium mold had contaminated one of his old experiments.  He thus happened upon the antibacterial properties of penicillin, the reason many of us are alive today (including…myself, for typhoid fever is often fatal when untreated)… Furthermore, while in hindsight the discovery appears momentous, it took a very long time for health officials to realize the importance of what they had on their hands.  Even Fleming lost faith in the idea before it was subsequently revived.

In 1965 two radio astronomists at Bell Labs in New Jersey who were mounting a large antenna were bothered by a background noise, a hiss, like the static that you hear when you have bad reception.  The noise could not be eradicated””even after they cleaned the bird excrement out of the dish, since they were convinced that bird poop was behind the noise.  It took a while for them to figure out that what they were hearing was the trace of the birth of the universe, the cosmic background microwave radiation.  This discovery revived the big bang theory, a languishing idea that was posited by earlier researchers.

What’s interesting (but typical) is that the physicists””Ralph Alpher, Hans Bethe, and George Gamow””who conceived of the idea of cosmic background radiation did not discover the evidence they were looking for, while those not looking for such evidence found it.

Furthermore, observes Taleb:

When a new technology emerges, we either grossly underestimate or severely overestimate its importance.  Thomas Watson, the founder of IBM, once predicted that there would be no need for more than just a handful of computers.

Taleb adds:

The laser is a prime illustration of a tool made for a given  purpose (actually no real purpose) that then found applications that were not even dreamed of at the time.  It was a typical “solution looking for a problem.”  Among the early applications was the surgical stitching of detached retinas.  Half a century later, The Economist asked Charles Townes, the alleged inventor of the laser, if he had had retinas on his mind.  He had not.  He was satisfying his desire to split light beams, and that was that.  In fact, Townes’s colleagues teased him quite a bit about the irrelevance of his discovery.  Yet just consider the effects of the laser in the world around you: compact disks, eyesight corrections, microsurgery, data storage and retrieval””all unforeseen applications of the technology.

Taleb mentions that the French mathematician Henri Poincare was aware that equations have limitations when it comes to predicting the future.

Poincare’s reasoning was simple: as you project into the future you may need an increasing amount of precision about the dynamics of the process that you are modeling, since your error rate grows very rapidly… Poincare showed this in a very simple case, famously known as the “three body problem.”  If you have only two planets in a solar-style system, with nothing else affecting their course, then you may be able to indefinitely predict the behavior of these planets, no sweat.  But add a third body, say a comet, ever so small, between the planets.  Initially the third body will cause no drift, no impact; later, with time, its effects on the other two bodies may become explosive.

Our world contains far more than just three bodies.  Therefore, many future phenomena are unpredictable due to complexity.

The mathematician Michael Berry gives another example: billiard balls.  Taleb:

If you know a set of basic parameters concerning the ball at rest, can compute the resistance of the table (quite elementary), and can gauge the strength of the impact, then it is rather easy to predict what would happen at the first hit… The problem is that to correctly predict the ninth impact, you need to take into account the gravitational pull of someone standing next to the table… And to compute the fifty-sixth impact, every single elementary particle of the universe needs to be present in your assumptions!

Moreover, Taleb points out, in the billiard ball example, we don’t have to worry about free will.  Nor have we incorporated relativity and quantum effects.

You can think rigorously, but you cannot use numbers.  Poincare even invented a field for this, analysis in situ, now part of topology…

In the 1960s the MIT meteorologist Edward Lorenz rediscovered Poincare’s results on his own””once again, by accident.  He was producing a computer model of weather dynamics, and he ran a simulation that projected a weather system a few days ahead.  Later he tried to repeat the same simulation with the exact same model and what he thought were the same input parameters, but he got wildly different results… Lorenz subsequently realized that the consequential divergence in his results arose not from error, but from a small rounding in the input parameters.  This became known as the butterfly effect, since a butterfly moving its wings in India could cause a hurricane in New York, two years later.  Lorenz’s findings generated interest in the field of chaos theory.

Much economics has been developed assuming that agents are rational.  However, Kahneman and Tversky have shown””in their work on heuristics and biases””that many people are less than fully rational.  Kahneman and Tversky’s experiments have been repeated countless times over decades.  Some people prefer apples to oranges, oranges to pears, and pears to apples.  These people do not have consistent preferences.  Furthermore, when guessing at an unknown quantity, many people will anchor on any random number even though the random number often has no relation to the quantity guessed at.

People also make different choices based on framing effects.  Kahneman and Tversky have illustrated this with the following experiment in which 600 people are assumed to have a deadly disease.

First Kahneman and Tversky used a positive framing:

• • Treatment A will save 200 lives for sure.
  • Treatment B has a 33% chance of saving everyone and a 67% chance of saving no one.

With this framing, 72% prefer Treatment A and 28% prefer Treatment B.

Next a negative framing:

• • Treatment A will kill 400 people for sure.
  • Treatment B has a 33% chance of killing no one and a 67% chance of killing everyone.

With this framing, only 22% prefer Treatment A, while 78% prefer Treatment B.

Note:  The two frames are logically identical, but the first frame focuses on lives saved, whereas the second frame focuses on lives lost.

Taleb argues that the same past data can confirm a theory and also its exact opposite.  Assume a linear series of points.  For the turkey, that can either confirm safety or it can mean the turkey is much closer to being turned into dinner.  Similarly, as Taleb notes, each day you live can either mean that you’re more likely to be immortal or that you’re closer to death.  Taleb observes that a linear regression model can be enormously misleading if you’re in Extremistan: Just because the data thus far appear to be in a straight line tells you nothing about what’s to come.

Taleb says the philosopher Nelson Goodman calls this the riddle of induction:

Let’s say that you observe an emerald.  It was green yesterday and the day before yesterday.  It is green again today.  Normally this would confirm the “green” property: we can assume that the emerald will be green tomorrow.  But to Goodman, the emerald’s color history could equally confirm the “grue” property.  What is this grue property?  The emerald’s grue property is to be green until some specified date… and then blue thereafter.

The riddle of induction is another version of the narrative fallacy””you face an infinity of “stories” that explain what you have seen.  The severity of Goodman’s riddle of induction is as follows: if there is no longer even a single unique way to “generalize” from what you see, to make an inference about the unknown, then how should you operate?  The answer, clearly, will be that you should employ “common sense,” but your common sense may not be so well developed with respect to some Extremistan variables.

 

CHAPTER 12: EPISTEMOCRACY, A DREAM

Taleb defines an epistemocrat as someone who is keenly aware that his knowledge is suspect.  Epistemocracy is a place where the laws are made with human fallibility in mind.  Taleb says that the major modern epistemocrat is the French philosopher Michel de Montaigne.

Montaigne is quite refreshing to read after the strains of a modern education since he fully accepted human weaknesses and understood that no philosophy could be effective unless it took into account our deeply ingrained imperfections, the limitations of our rationality, the flaws that make us human.  It is not that he was ahead of his time; it would be better said that later scholars (advocating rationality) were backward.

Montaigne was not just a thinker, but also a doer.  He had been a magistrate, a businessman, and the mayor of Bordeaux.  Taleb writes that Montaigne was a skeptic, an antidogmatist.

So what would an epistemocracy look like?

The Black Swan asymmetry allows you to be confident about what is wrong, not about what you believe is right.

Taleb adds:

The notion of future mixed with chance, not a deterministic extension of your perception of the past, is a mental operation that our mind cannot perform.  Chance is too fuzzy for us to be a category by itself.  There is an asymmetry between past and future, and it is too subtle for us to understanding naturally.

The first consequence of this asymmetry is that, in people’s minds, the relationship between the past and the future does not learn from the relationship between the past and the past previous to it.  There is a blind spot: when we think of tomorrow we do not frame it in terms of what we thought about yesterday or the day before yesterday.  Because of this introspective defect we fail to learn about the difference between our past predictions and the subsequent outcomes.  When we think of tomorrow, we just project it as another yesterday.

As yet another example of how we can’t predict, psychologists have shown that we can’t predict our future affective states in response to both pleasant and unpleasant events.  The point is that we don’t learn from our past errors in predicting our future affective states.  We continue to make the same mistake by overestimating the future impact of both pleasant and unpleasant events.  We persist in thinking that unpleasant events will make us more unhappy than they actually do.  We persist in thinking that pleasant events will make us happier than they actually do.  We simply don’t learn from the fact that we made these erroneous predictions in the past.

Next Taleb observes that it’s not only that we can’t predict the future.  We don’t know the past either.  Taleb gives this example:

• • Operation 1 (the melting ice cube): Imagine an ice cube and consider how it may melt over the next two hours while you play a few rounds of poker with your friends.  Try to envision the shape of the resulting puddle.
  • Operation 2 (where did the water come from?): Consider a puddle of water on the floor.  Now try to reconstruct in your mind’s eye the shape of the ice cube it may once have been.  Note that the puddle may not have necessarily originated from an ice cube.

It’s one thing to use physics and engineering to predict the forward process of an ice cube melting.  It’s quite another thing to try to reconstruct what it was that led to a puddle of water.  Taleb:

In a way, the limitations that prevent us from unfrying an egg also prevent us from reverse engineering history.

For these reasons, history should just be a collection of stories, argues Taleb.  History generally should not try to discover the causes of why things happened the way they did.

 

CHAPTER 13: APELLES THE PAINTER, OR WHAT DO YOU DO IF YOU CANNOT PREDICT?

Taleb writes about being a fool in the right places:

The lesson for the small is: be human!  Accept that being human involves some amount of epistemic arrogance in running your affairs.  Do not be ashamed of that.  Do not try to always withhold judgment””opinions are the stuff of life.  Do not try to avoid predicting””yes, after this diatribe about prediction I am not urging you to stop being a fool.  Just be a fool in the right places.

What you should avoid is unnecessary dependence on large-scale harmful predictions””those and only those.  Avoid the big subjects that may hurt your future: be fooled in small matters, not in the large.  Do not listen to economic forecasters or to predictors in social science (they are mere entertainers), but do make your own forecast for the picnic…

Know how to rank beliefs not according to their plausibility but by the harm they may cause.

Taleb advises us to maximize the serendipity around us.  You want to be exposed to the positive accident.  Taleb writes that Sextus Empiricus retold a story about Apelles the Painter.  Try as he might, Apelles was not able to paint the foam on a horse’s mouth.  He really tried hard but eventually gave up.  In irritation, he took a sponge and threw it at the painting.  The sponge left a pattern on the painting that perfectly depicted the foam.

(Illustration by Ileezhun)

Taleb recommends trial and error:

Indeed, we have psychological and intellectual difficulties with trial and error, and with accepting that series of small failures are necessary in life.  My colleague Mark Spitznagel understood that we humans have a mental hang-up about failures:  “You need to love to lose” was his motto.  In fact, the reason I felt immediately at home in America is precisely because American culture encourages the process of failure, unlike the cultures of Europe and Asia where failure is met with stigma and embarrassment.  America’s specialty is to take these small risks for the rest of the world, which explains this country’s disproportionate share in innovations.

Taleb then points out:

People are often ashamed of losses, so they engage in strategies that produce very little volatility but contain the risk of a large loss””like collecting nickles in front of steamrollers.

Taleb recommends a barbell strategy.  You put 85 to 90 percent of your money in extremely safe instruments like U.S. Treasury bills.

The remaining 10 to 15 percent you put in extremely speculative bets, as leveraged as possible (like options), preferably venture capital-style portfolios.

Taleb offers five tricks:

• • Make a distinction between positive contingencies and negative ones.  There are both positive and negative Black Swans.
  • Do not be narrow-minded.  Do not try to predict precise Black Swans because that tends to make you more vulnerable to the ones you didn’t predict.  Invest in preparedness, not in prediction.
  • Seize any opportunity, or anything that looks like opportunity.  They are rare, much rarer than you think.  Work hard, not in grunt work, but in chasing such opportunities and maximizing exposure to them.
  • Beware of precise plans by governments.
  • Do not waste your time trying to fight forecasters, stock analysts, economists, and social scientists.  People will continue to predict foolishly, especially if they are paid for it.

Taleb concludes:

All these recommendations have one point in common: asymmetry.  Put yourself in situations where favorable consequences are much larger than unfavorable ones.

Taleb explains:

This idea that in order to make a decision you need to focus on the consequences (which you can know) rather than the probability (which you can’t know) is the central idea of uncertainty.  Much of my life is based on it.

 

PART THREE: THOSE GRAY SWANS OF EXTREMISTAN

The final four items related to the Black Swan:

• • The world is moving deeper into Extremistan.
  • The Gaussian bell curve is a contagious and severe delusion.
  • Using Mandelbrotian, or fractal, randomness, some Black Swans can by turned into Gray Swans.
  • Some ideas about uncertainty.

 

CHAPTER 14: FROM MEDIOCRISTAN TO EXTREMISTAN, AND BACK

The economist Sherwin Rosen wrote in the early 1980s about “the economics of superstars.”  Think about some of the best professional athletes or actors earning hundreds of millions of dollars.

According to Rosen, this inequality comes from a tournament effect: someone who is marginally “better” can easily win the entire pot, leaving the others with nothing…

But the role of luck is missing in Rosen’s beautiful argument.  The problem here is the notion of “better,” this focus on skills as leading to success.  Random outcomes, or an arbitrary situation, can also explain success, and provide the initial push that leads to a winner-take-all result.  A person can get slightly ahead for entirely random reasons; because we like to imitate one another, we will flock to him.  The world of contagion is so underestimated!

As I am writing these lines, I am using a Macintosh, by Apple, after years of using Microsoft-based products.  The Apple technology is vastly better, yet the inferior software won the day.  How?  Luck.

Taleb next mentions the Matthew effect, according to which people take from the poor to give to the rich.  Robert K. Merton looked at the performance of scientists and found that an initial advantage would tend to follow someone through life.  The theory also can apply to companies, businessmen, actors, writers, and anyone else who benefits from past success.

Taleb observes that the vast majority of the largest five hundred U.S. corporations have eventually either shrunk significantly or gone out of business.  Why?  Luck plays a large role.

Taleb:

Capitalism is, among other things, the revitalization of the world thanks to the opportunity to be lucky.  Luck is the grand equalizer, because almost everyone can benefit from it…

Everything is transitory.  Luck both made and unmade Carthage; it both made and unmade Rome.

I said earlier that randomness is bad, but it is not always so.  Luck is far more egalitarian than even intelligence.  If people were rewarded strictly according to their abilities, things would still be unfair””people don’t choose their abilities.  Randomness has the beneficial effect of reshuffling society’s cards, knocking down the big guy.

 

CHAPTER 15: THE BELL CURVE, THAT GREAT INTELLECTUAL FRAUD

The Gaussian bell curve, also called the normal distribution, describes many things, including height.  Taleb presents the following data about height.  First, he assumes that the average height (men and women) is 1.67 meters, (about 5 feet 7 inches).  Then look at the following increments and consider the odds of someone being that tall:

• 10 centimeters taller than the average (1.77 m, or 5 feet 10): 1 in 6.3
• 20 centimeters taller than the average (1.87 m, or 6 feet 2): 1 in 44
• 30 centimeters taller than the average (1.97 m, or 6 feet 6): 1 in 740
• 40 centimeters taller than the average (2.07 m, or 6 feet 9): 1 in 32,000
• 50 centimeters taller than the average (2.17 m, or 7 feet 1): 1 in 3,500,000
• 60 centimeters taller than the average (2.27 m, or 7 feet 5): 1 in 1,000,000,000
• 70 centimeters taller than the average (2.37 m, or 7 feet 9): 1 in 780,000,000,000
• 80 centimeters taller than the average (2.47 m, or 8 feet 1): 1 in 1,600,000,000,000,000
• 90 centimeters taller than the average (2.57 m, or 8 feet 5): 1 in 8,900,000,000,000,000,000
• 100 centimeters taller than the average (2.67 m, or 8 feet 9): 1 in 130,000,000,000,000,000,000,000
• 110 centimeters taller than the average (2.77 m, or 9 feet 1): 1 in 36,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000

Taleb comments that the super fast decline is what allows you to ignore outliers in a normal distribution (the bell curve).

By contrast, consider the odds of being rich in Europe:

• • People with a net worth higher than 1 million: 1 in 63
  • Higher than 2 million: 1 in 250
  • Higher than 4 million: 1 in 1,000
  • Higher than 8 million: 1 in 4,000
  • Higher than 16 million: 1 in 16,000
  • Higher than 32 million: 1 in 64,000
  • Higher than 320 million: 1 in 6,400,000

The important point is that for this Mandelbrotian distribution, the speed of the decrease remains constant.

(Power law graph, via Wikimedia Commons)

Taleb:

This, in a nutshell, illustrates the difference between Mediocristan and Extremistan.

Taleb writes:

Consider this effect.  Take a random sample of any two people from the U.S. population who jointly earn $1 million per annum.  What is the most likely breakdown of their respective incomes?  In Mediocristan, the most likely combination is half a million each.  In Extremistan, it would be $50,000 and $950,000.

The situation is even more lopsided with book sales.  If I told you that two authors sold a total of a million copies of their books, the most likely combination is 993,000 copies sold for one and 7,000 for the other.  This is far more likely than that the books each sold 500,000 copies…

Why is this so?  The height problem provides a comparison.  If I told you that the total height of two people is fourteen feet, you would identify the most likely breakdown as seven feet each, not two feet and twelve feet; not even eight feet and six feet!

Taleb summarizes:

Although unpredictable large deviations are rare, they cannot be dismissed as outliers because, cumulatively, their impact is so dramatic.

The traditional Gaussian way of looking at the world begins by focusing on the ordinary, and then deals with exceptions or so-called outliers as ancillaries.  But there is a second way, which takes the exceptional as a starting point and treats the ordinary as subordinate.

Taleb continues by noting that if there are strong forces bringing things back into equilibrium, then you can use the Gaussian approach.  (That’s why much of economics is based on equilibrium.)  If there is a rational reason for the largest not to be too far from the average, then again you can use the Gaussian approach.  If there are physical limitations preventing very large observations, once again the Gaussian approach works.

Another example of where the Gaussian approach works is a cup of coffee.  There are several trillion particles in a coffee cup.  But for the cup of coffee to jump off a table, all the particles would have to jump in the same direction.  That’s not going to happen in the lifetime of this universe, notes Taleb.

Taleb explains that the Gaussian family is the only class of distributions for which the average and the standard deviation are sufficient to describe.  Moreover, correlation and regression have little or no significance outside of the Gaussian.  Taleb observes that correlation and standard deviation can be very unstable and can depend largely on which historical periods you look at.

The French mathematician Poincare was suspicious of the Gaussian, writes Taleb.

Poincare wrote that one of his friends, an unnamed “eminent physicist,” complained to him that physicists tended to use the Gaussian curve because they thought mathematicians believed it a mathematical necessity; mathematicians used it because they believed that physicists found it to be an empirical fact.

Taleb adds:

If you’re dealing with qualitative inference, such as in psychology or medicine, looking for yes/no answers to which magnitudes don’t apply, then you can assume you’re in Mediocristan without serious problems.  The impact of the improbable cannot be too large.  You have cancer or you don’t, you are pregnant or you are not, et cetera… But if you are dealing with aggregates, where magnitudes do matter, such as income, your wealth, return on a portfolio, or book sales, then you will have a problem and get the wrong distribution if you use the Gaussian, as it does not belong there.  One single number can disrupt all your averages; one single loss can eradicate a century of profits.

 

CHAPTER 16: THE AESTHETICS OF RANDOMNESS

Taleb:

Fractality is the repetition of geometric patterns at different scales, revealing smaller and smaller versions of themselves.

Taleb explains:

There is no qualitative change when an object changes size.  If you look at the coast of Britain from an airplane, it resembles what you see when you look at it with a magnifying glass.  This character of self-affinity implies that one deceptively short and simple rule of iteration can be used, either by a computer or, more randomly, by Mother Nature, to build shapes of seemingly great complexity… Mandelbrot designed the mathematical object now known as the Mandelbrot set, the most famous object in the history of mathematics.  It became popular with followers of chaos theory because it generates pictures of ever increasing complexity by using a deceptively minuscule recursive rule; recursive means that something can be reapplied to itself infinitely.  You can look at the set at smaller and smaller resolutions without ever reaching the limit; you will continue to see recognizable shapes.  The shapes are never the same, yet they bear an affinity to one another, a strong family resemblance.

Taleb notes that most computer-generated objects are based on some version of the Mandelbrotian fractal.  Taleb writes of Benoit Mandelbrot:

His talks were invaded by all sorts of artists, earning him the nickname the Rock Star of Mathematics.  The computer age helped him become one of the most influential mathematicians in history, in terms of the applications of his work, way before his acceptance by the ivory tower.  We will see that, in addition to its universality, his work offers an unusual attribute: it is remarkably easy to understand.

Let’s consider again Mediocristan.  Taleb:

I am looking at the rug in my study.  If I examine it with a microscope, I will see a very rugged terrain.  If I look at it with a magnifying glass, the terrain will be smoother but still highly uneven.  But when I look at it from a standing position, it appears uniform””it is almost as smooth as a sheet of paper.  The rug at eye level corresponds to Mediocristan and the law of large numbers: I am seeing the sum of undulations, and these iron out.  This is like Gaussian randomness: the reason my cup of coffee does not jump is that the sum of all of its moving particles becomes smooth.  Likewise, you reach certainties by adding up small Gaussian uncertainties: this is the law of large numbers.

The Gaussian is not self-similar, and that is why my coffee cup does not jump on my desk.

How does fractal geometry relate to things like the distribution of wealth, the size of cities, returns in the financial markets, the number of casualties in war, or the size of planets?  Taleb:

The key here is that the fractal has numerical or statistical measures that are (somewhat) preserved across scales””the ratio is the same, unlike the Guassian.

Taleb argues that fractals can make Black Swans gray:

Fractals should be the default, the approximation, the framework.  They do not solve the Black Swan problem and do not turn all Black Swans into predictable events, but they significantly mitigate the Black Swan problem by making such large events conceivable.

Taleb continues:

I have shown in the wealth lists in Chapter 15 the logic of a fractal distribution: if wealth doubles from 1 million to 2 million, the incidence of people with at least that much money is cut in four, which is an exponent of two.  If the exponent were one, then the incidence of that wealth or more would be cut in two.  The exponent is called the “power” (which is why some people use the term power law).

Taleb presents the following table with the assumed exponents (powers) for various phenomena:

Phenomenon	Assumed Exponent (vague approximation)
Frequency of use of words	1.2
Number of hits on websites	1.4
Number of books sold in the U.S.	1.5
Telephone calls received	1.22
Magnitude of earthquakes	2.8
Diameter of moon craters	2.14
Intensity of solar flares	0.8
Intensity of wars	0.8
Net worth of Americans	1.1
Number of persons per family name	1
Population of U.S. cities	1.3
Market moves	3 (or lower)
Company size	1.5
People killed in terrorist attacks	2 (but possibly a much lower exponent)

Note that these exponents (powers) are best guesses on the basis of statistical information.  It’s often hard to know the true parameters“”if they exist.  Also note that you will have a huge sampling error.  Finally, note that, because of the way the math works (you use the negative of the exponent), a lower power implies greater deviations.

Taleb observes:

My colleagues and I worked with around 20 million pieces of financial data.  We all had the same data set, yet we never agreed on exactly what the exponent was in our sets.  We knew the data revealed a fractal power law, but we learned that one could not produce a precise number.  But what we did know“”that the distribution is scalable and fractal“”was sufficient for us to operate and make decisions.

 

CHAPTER 17: LOCKE’S MADMEN, OR BELL CURVES IN THE WRONG PLACES

Taleb laments the fact that Gaussian tools are still widely used even when they don’t apply to the phenomena in question:

The strangest thing is that people in business usually agree with me when they listen to me talk or hear me make my case.  But when they go to the office the next day they revert to the Gaussian tools so entrenched in their habits.  Their minds are domain-dependent, so they can exercise critical thinking at a conference while not doing so in the office.  Furthermore, the Gaussian tools give them numbers, which seem to be “better than nothing.”  The resulting measure of future uncertainty satisfies our ingrained desire to simplify even if that means squeezing into one single number matters that are too rich to be described that way.

Taleb later describes how various researchers disputed Taleb’s main points:

People would find data in which there were no jumps or extreme events, and show me a “proof” that one could use the Gaussian.  [This is like observing O.J. Simpson and concluding he’s not a killer because you never saw him kill someone while you were observing him.]  The entire statistical business confused absence of proof with proof of absence.  Furthermore, people did not understand the elementary asymmetry involved: you need one single observation to reject the Gaussian, but millions of observations will not fully confirm the validity of its application.  Why?  Because the Gaussian bell curve disallows large deviations, but tools of Extremistan, the alternative, do not disallow long quiet stretches.

The hedge fund Long-Term Capital Management, or LTCM, was founded by people considered to be geniuses, including Nobel winners Robert Merton, Jr., and Myron Scholes.  LTCM, using Gaussian methods, claimed that it had very sophisticated ways of measuring risk.  According to their Gaussian models, they had virtually no real risk.  Then LTCM blew up.  A Black Swan.

Taleb comments:

The magnitude of the losses was spectacular, too spectacular to allow us to ignore the intellectual comedy.  Many friends and I though that the portfolio theorists would suffer the fate of tobacco companies: they were endangering people’s savings and would soon be brought to account for the consequences of their Gaussian-inspired methods.

None of that happened.

Instead, MBAs in business schools went on learning portfolio theory.  And the option formula went on bearing the name Black-Scholes-Merton, instead of reverting to its true owners, Louis Bachelier, Ed Thorp, and others.

Despite the overwhelming evidence that Gaussian assumptions do not apply to past financial data, many researchers continue to make Gaussian assumptions.  Taleb says this resembles Locke’s definition of a madman: someone “reasoning correctly from erroneous premises.”

Taleb asserts that military people focus first on having realistic assumptions.  Only later do they focus on correct reasoning.

This is where you learn from the minds of military people and those who have responsibilities in security.  They do not care about “perfect” ludic reasoning; they want realistic ecological assumptions.  In the end, they care about lives.

 

CHAPTER 18: THE UNCERTAINTY OF THE PHONY

People like to refer to the uncertainty principle and then talk about the limits of our knowledge.  However, uncertainties about subatomic particles are very small and very numerous.  They average out, says Taleb: They obey the law of large numbers and they are Gaussian.

Taleb writes about trying to visit his ancestral village of Amioun, Lebanon:

Beirut’s airport is closed owing to the conflict between Israel and the Shiite militia Hezbollah.  There is no published airline schedule that will inform me when the war will end, if it ends.  I can’t figure out if my house will be standing, if Amioun will still be on the map“”recall that the family house was destroyed once before.  I can’t figure out if the war is going to degenerate into something even more severe.  Looking into the outcome of the war, with all my relatives, friends, and property exposed to it, I face true limits of knowledge.  Can someone explain to me why I should care about subatomic particles that, anyway, converge to a Gaussian?  People can’t predict how long they will be happy with recently acquired objects, how long their marriages will last, how their new jobs will turn out, yet it’s subatomic particles that they cite as “limits of prediction.”  They’re ignoring a mammoth standing in front of them in favor of matter even a microscope would not allow them to see.

 

PART FOUR: THE END

CHAPTER 19: HALF AND HALF, OR HOW TO GET EVEN WITH THE BLACK SWAN

Taleb concludes:

Half the time I am a hyperskpetic; the other half I hold certainties and can be intransigent about them, with a very stubborn disposition… I am skeptical when I suspect wild randomness, gullible when I believe that randomness is mild.

Half the time I hate Black Swans, the other half I love them.  I like the randomness that produces the texture of life, the positive accidents, the success of Apelles the painter, the potential gifts you do not have to pay for.  Few understand the beauty in the story of Apelles; in fact, most people exercise their error avoidance by repressing the Apelles in them.

Taleb continues:

In the end this is a trivial decision making rule: I am very aggressive when I can gain exposure to positive Black Swans“”when a failure would be of small moment“”and very conservative when I am under threat from a negative Black Swan.  I am very aggressive when an error in a model can benefit me, and paranoid when the error can hurt.  This may not be too interesting except that it is exactly what other people do not do…

Half the time I am intellectual, the other half I am a no-nonsense practitioner.  I am no-nonsense and practical in academic matters, and intellectual when it comes to practice.

Half the time I am shallow, the other half I want to avoid shallowness.  I am shallow when it comes to aesthetics; I avoid shallowness in the context of risks and returns.  My aestheticism makes me put poetry before prose, Greeks before Romans, dignity before elegance, elegance before culture, culture before erudition, erudition before knowledge, knowledge before intellect, and intellect before truth.  But only for matters that are Black Swan free.  Our tendency is to be very rational, except when it comes to the Black Swan.

Taleb’s final points:

We are quick to forget that just being alive is an extraordinary piece of good luck, a remote event, a chance occurrence of monstrous proportions.

Imagine a speck of dust next to a planet a billion times the size of the earth.  The speck of dust represents the odds in favor of your being born; the huge planet would be the odds against it.  So stop sweating the small stuff… remember that you are a Black Swan.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: [email protected]

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

September 18, 2022

Gregory Zuckerman’s new book, The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution, is a terrific book.

When Zuckerman set out to write a book about Jim Simons’s Renaissance Technologies, Simons and others made it clear to Zuckerman not to expect any help from them. Zuckerman wasn’t surprised. He knew that Simons and team were among the most secretive traders in Wall Street history. Zuckerman writes:

There were compelling reasons I was determined to tell Simons’s story. A former math professor, Simons is arguably the most successful trader in the history of modern finance. Since 1988, Renaissance’s flagship Medallion hedge fund has generated average annual returns of 66 percent… No one in the investment world comes close. Warren Buffett, George Soros, Peter Lynch, Steve Cohen, and Ray Dalio all far short…

Zuckerman adds:

Simons’s pioneering methods have been embraced in almost every industry, and reach nearly every corner of everyday life. He and his team were crunching statistics, turning tasks over to machines, and relying on algorithms more than three decades ago–long before these tactics were embraced in Silicon Valley, the halls of government, sports stadiums, doctors’ offices, military command centers, and pretty much everywhere else forecasting is required.

With persistence, Zuckerman ended up doing over four hundred interviews with more than thirty current and former Renaissance employees. And he did interviews with a larger number of friends, family members, and others. Zuckerman:

I owe deep gratitude to each individual who spent time sharing memories, observations, and insights. Some accepted substantial personal risk to help me tell this story. I hope I rewarded their faith.

(Jim Simons, by Gert-Martin Greuel, via Wikimedia Commons)

 

Part One: Money Isn’t Everything

CHAPTER ONE

In the winter of 1952, Jimmy Simons was fourteen years old. He was trying to earn spending money at Breck’s garden supply near Newton, Massachusetts, where his home was. Jimmy was absent-minded and misplaced almost everything. So the owners asked him to sweep the floor. A few weeks later, having finished the Christmas-time job, the owners asked Jimmy what he wanted to do. He replied:

I want to study mathematics at MIT.

Breck’s owners burst out laughing. How could someone so absent-minded study mathematics? And at MIT? Zuckerman writes:

The skepticism didn’t bother Jimmy, not even the giggles. The teenager was filled with preternatural confidence and an unusual determination to accomplish something special, the result of supportive parents who had experienced both high hopes and deep regrets in their own lives.

Jimmy remained an only child of Marcia and Matthew Simons after Marcia endured a series of miscarriages.

A sharp intellect with an outgoing personality and subtle wit, Marcia volunteered in Jimmy’s school but never had the opportunity to work outside the home. She funneled her dreams and passions into Jimmy, pushing him academically and assuring him that success was ahead.

Matty Simons was a sales manager for 20^th Century Fox. He loved the job. But then his father-in-law, Peter Kantor, asked him to work at his shoe factory. Peter promised Matty an ownership stake. Matty felt obliged to join the family business. Zuckerman:

Matty Simons spent years as the general manager of the shoe factory, but he never received the ownership share Peter had promised. Later in life, Matty told his son he wished he hadn’t forgone a promising and exciting career to do what was expected of him.

Simons says the lesson he learned was to do what you like in life, not what you think you ‘should’ do. Simons never forgot this lesson.

Even as a young kid, Simons loved to think, often about mathematics. Zuckerman:

Unlike his parents, Jimmy was determined to focus on his own passions. When he was eight, Dr. Kaplan, the Simons family doctor, suggested a career in medicine, saying it was the ideal profession “for a bright Jewish boy.”

Jimmy bristled.

“I want to be a mathematician or a scientist,” he replied.

The doctor tried to reason with the boy. “Listen, you can’t make any money in mathematics.”

Jimmy said he wanted to try.

Zuckerman continues:

He loved books, frequently visiting a local library to take out four a week, many well above his grade level. Mathematical concepts captivated him most, however.

After finishing high school in three years, the seventeen-year-old took a trip across the country with a friend. One thing they encountered was severe poverty, which made them more sensitive to the predicaments of society’s disadvantaged.

Soon Simons enrolled at MIT.

(MIT logo, via Wikimedia Commmons)

He skipped the first year of mathematics because he had taken advanced-placement courses in high school. Zuckerman:

Overconfident during the second semester of his freshman year, Simons registered for a graduate course in abstract algebra. It was an outright disaster. Simons was unable to keep up with the classmates and couldn’t understand the point of the assignments and course topics.

Simons bought a book on the subject and took it home for the summer, reading and thinking for hours at a time. Finally, it clicked. Simons aced subsequent algrebra classes. Though he received a D in an upper-level calculus course in his sophomore year, the professor allowed him to enroll in the next level’s class, which discussed Stokes’ theorem, a generalization of Isaac Newton’s fundamental theorem of calculus that relates line integrals to surface integrals in three dimensions. The young man was fascinated–a theorem involving calculus, algebra, and geometry seemed to produce simple, unexpected harmony. Simons did so well in the class that students came to him seeking help.

“I just blossomed,” Simons says. “It was a glorious feeling.”

Simons loved the beauty of mathematics. Although Simons realized he wasn’t quite the best, he had an imaginative approach to problems and an instinct to focus on the kinds of problems that might lead to breakthroughs.

When Simons returned to MIT to begin his graduate studies, his advisor suggested he finish his PhD at the University of California, Berkeley, so he could work with a professor named Shiing-Shen Chern, a former math prodigy from China and a leading differential geometer and topologist.

Meanwhile, he had met an eighteen-year-old named Barbara Bluestein. They talked a great deal and eventually decided to get engaged. Over strong objections from her parents, Barbara decided to go with Simons to Berkeley. The pair got married in Reno, Nevada. Simons used the money they had left after the marriage to play poker. He won enough to buy Barbara a black bathing suit.

Back at Berkeley:

…Simons made progress on a PhD dissertation focused on differential geometry–the study of curved, multidimensional spaces using methods from calculus, topology, and linear algebra. Simons also spent time on a new passion: trading. The couple had received $5,000 as a wedding gift, and Simons was eager to multiply the cash.

Simons bought a couple of stocks. But they didn’t move and he asked a broker if they had anything “more exciting.” The broker suggested soybeans.

Simons knew nothing about commodities or how to trade futures… but he became an eager student. At the time, soybeans sold for $2.50 per bushel. When the broker said Merrill Lynch’s analysts expected prices to go to three dollars or even higher, Simons’s eyes widened. He bought two futures contracts, watched soybeans soar, and scored several thousand dollars of profits in a matter of days.

Simons was hooked.

“I was fascinated by the action and the possibility I could make money short-term,” he says.

After a respected journal published Simons’s dissertation, he won a prestigious, three-year teaching position at MIT. However, Simons began to worry that his whole life would be research and teaching.

“Is this it? Am I going to do this my whole life?” he asked Barbara one day at home. “There has to be more.”

In 1963, Simons accepted a research position at Harvard.

 

CHAPTER TWO

Zuckerman writes:

In 1964, Simons quit Harvard University to join an intelligence group helping to fight the ongoing Cold War with the Soviet Union. The group told Simons he could continue his mathematics research as he worked on government assignments. Just as important, he doubled his previous salary and began paying off his debts.

Simons’s offer came from the Princeton, New Jersey, division of the Institute for Defense Analyses, an elite research organization that hired mathematicians from top universities to assist the National Security Agency–the United States’ largest and most secretive intelligence agency–in detecting and attacking Russian codes and ciphers.

…The IDA taught Simons how to develop mathematical models to discern and interpret patterns in seemingly meaningless data. He began using statistical analysis and probability theory, mathematical tools that would influence his work.

Simons learned that he liked making algorithms and testing things out on a computer. Simons became a sleuthing star.

Also, Simons learned by seeing how the group recruited new researchers. The recruits were identified by brainpower, creativity, and ambition, rather than for any particular expertise or background. Simons met Lenny Baum, one of the most-accomplished code breakers. Baum developed a saying that became the group’s credo:

“Bad ideas is good, good ideas is terrific, no ideas is terrible.”

Zuckerman notes:

The team routinely shared credit and met for champagne toasts after discovering solutions to particularly thorny problems. Most days, researchers wandered into one another’s offices to offer assistance or lend an ear. When staffers met each day for afternoon tea, they discussed the news, played chess, worked on puzzles, or competed at Go, the complicated Chinese board game.

Simons and his wife threw regular dinner parties at which IDA staffers became inebriated on Barbara’s rum-heavy Fish House Punch. The group played high-stakes poker matches that lasted until the next morning, with Simons often walking away with fistfuls of his colleagues’ cash.

Meanwhile, Simons was making progress in his research on minimal varieties, a subfield of differential geometry in which he long had an interest. Zuckerman:

He was hoping to discover and codify universal principles, rules, and truths, with the goal of furthering the understanding of these mathematical objects. Albert Einstein argued that there is a natural order in the world; mathematicians like Simons can be seen as searching for evidence of that structure. There is true beauty to their work, especially when it succeeds in revealing something about the universe’s natural order. Often, such theories find practical applications, even many years later, while advancing our knowledge of the universe.

Eventually, a series of conversations with Frederick Almgren Jr., a professor at nearby Princeton University who had solved the problem in three dimensions, helped Simons achieve a breakthrough. Simons created a partial differential equation of his own, which became known as the Simons equation, and used it to develop a uniform solution through six dimensions…

In 1968, Simons published “Minimal Varieties in Riemannian Manifolds,” which became a foundational paper for geometers, proved crucial in related fields, and continues to garner citations, underscoring its enduring significance. These achievements helped establish Simons as one of the world’s preeminent geometers.

At the same time, Simons was studying the stock market in an effort to figure out how to make money. Collaborating with Baum and two other colleagues, Simons developed a stock-trading system. Simons and his colleagues ignored fundamental information such as earnings, dividends, and corporate news. Instead, they searched for “macroscopic variables” that could predict the short-term behavior of the stock market.

Here’s what was really unique: [They] didn’t try to identify or predict these states using economic theory or other conventional methods, nor did the researchers seek to address why the market entered certain states. Simons and his colleagues used mathematics to determine the set of states best fitting the observed pricing data; their model then made its bets accordingly. The whys didn’t matter, Simons and his colleagues seemed to suggest, just the strategies to take advantage of the inferred states.

Simons and his colleagues used a mathematical tool called a hidden Markov model.

(Hidden Markov models, via Wikimedia Commons)

Also, they weren’t alone. For instance, mathematician Ed Thorp developed an early form of computerized trading.

***

Simons was really good at identifying the most promising ideas of his colleagues. Soon, he was in line to become deputy director of IDA. Then came the Vietnam war. Simons wrote a six-paragraph letter to The New York Times, arguing that there were better uses of the nation’s resources than the Vietnam war. As a result, IDA fired Simons.

A friend asked Simons what his ideal job was. Simons said he’d like to be the chair of a large math department, but he was too young and didn’t know the right people. The friend told Simons that he had an idea. Soon thereafter, Simons received a letter from John Toll, president of SUNY Stony Brook, a public university on Long Island. The school had already spent five years looking for someone to chair its math department.

Toll was leading a $100 million, government-funded push to make SUNY Stony Brook the “Berkeley of the East.”

In 1968, Simons moved his family to Long Island and he began recruiting. One person he targeted was a Cornell University mathematician named James Ax, who had won the prestigious Cole Prize in number theory. Simons and Ax had been friendly as graduate students at Berkeley. Simons charmed Ax into moving to Stony Brook. Zuckerman:

Ax’s decision sent a message that Simons meant business. As he raided other schools, Simons refined his pitch, focusing on what it might take to lure specific mathematicians. Those who valued money got raises; those focused on personal research got lighter class loads, extra leave, generous research support, and help evading irritating administrative requirements.

Meanwhile, Simons’s marriage to Barbara was struggling. They eventually divorced. Barbara went on to earn a PhD in computer science at Berkeley in 1981. Subsequently, she had a distinguished career. Asked about her marriage to Simons, she reflected that they had gotten married when they were too young.

Now alone on Long Island, Simons sought a nanny to help when his three children were at his house. That’s how he met Marilyn Hawrys, a twenty-two-year-old blond who later became a graduate student in economics at Stony Brook. Simons asked her on a date, and they started seeing each other.

Around this time, Simons made a breakthrough with Shiing-Shen Chern.

On his own, Simons made a discovery related to quantifying shapes in curved, three-dimensional spaces. He showed his work to Chern, who realized the insight could be extended to all dimensions. In 1974, Chern and Simons published “Characteristic Forms and Geometric Invariants,” a paper that introduced Chern-Simons invariants… which proved useful in various aspects of mathematics.

In 1976, at the age of thirty-seven, Simons was awarded the American Mathematical Society’s Oswald Veblen Prize in Geometry, the highest honor in the field, for his work with Chern and his earlier research in minimal varieties. A decade later, theoretical physicist Edward Witten and others would discover that Chern-Simons theory had applications to a range of areas in physics, including condensed matter, string theory, and supergravity. It even became crucial to methods used by Microsoft and others in their attempts to develop quantum computers capable of solving problems vexing modern computers, such as drug development and artificial intelligence. By 2019, tens of thousands of citations in academic papers–approximately three a day–referenced Chern-Simons theory, cementing Simons’s position in the upper echelon of mathematics and physics.

Simons was ready for a new challenge. He had recently invested money with Charlie Freifeld, who had taken a course from Simons at Harvard. Freifeld used econometric models to predict the prices of commodities. Soon Simons’s investment with Freifeld had increased tenfold. This got Simons’s excited again about the challenge of investing.

In 1978, Simons left academia to start an investment firm focused on currency trading. (World currencies had recently been allowed to float.) Some academics thought that Simons was squandering a rare talent.

 

CHAPTER THREE

Early summer of 1978, a few miles down the road from Stony Brook University:

Simons sat in a storefront office in the back of a dreary strip mall. He was next to a woman’s clothing boutique, two doors down from a pizza joint, and across from the tiny, one-story Stony Brook train station. His space, built for a retail establishment, had beige wallpaper, a single computer terminal, and spotty phone service. From his window, Simons could barely see the aptly named Sheep Pasture Road, an indication of how quickly he had gone from broadly admired to entirely obscure.

The odds weren’t in favor of a forty-year-old mathematician embarking on his fourth career, hoping to revolutionize the centuries-old world of investing.

Simons hadn’t shown any real talent in investing. He acknowledged that his investment with Freifeld had been “completely lucky.”

They only held on to the profits because they had agreed to cash out if they ever made a large amount. Weeks after they sold their position, sugar prices plummeted, which neither Freifeld nor Simons had predicted. They had barely avoided disaster. Zuckerman:

Somehow, Simons was bursting with self-confidence. He had conquered mathematics, figured out code-breaking, and built a world-class university department. Now he was sure he could master financial speculation, partly because he had developed a special insight into how financial markets operated.

[…]

It looks like there’s some structure here, Simons thought.

He just had to find it.

Simons decided to treat financial markets like any other chaotic system. Just as physicists pore over vast quantities of data and build elegant models to identify laws in nature, Simons would built mathematical models to identify order in financial markets. His approach bore similarities to the strategy he had developed years earlier at the Institute for Defense Analyses, when he and his colleagues wrote the research paper that determined that markets existed in various hidden states that could be identified with mathematical models. Now Simons would test the approach in real life.

Simons named his company Monemetrics, combining “money” and “econometrics.” He then began a process he knew well: hiring a team of big brains. Zuckerman:

He did have an ideal partner in mind for his fledgling firm: Leonard Baum, one of the coauthors of the IDA research paper and a mathematician who had spent time discerning hidden states and making short-term predictions in chaotic environments. Simons just had to convince Baum to risk his career on Simons’s radical, unproven approach.

Baum’s parents had fled Russia for Brooklyn to escape poverty and anti-Semitism. In high school, Baum was six feet tall and his school’s top sprinter. He also played tennis. Baum graduated Harvard University in 1953 and then earned a PhD in mathematics. Zuckerman:

After joining the IDA in Princeton, Baum was even more successful breaking code than Simons, receiving credit for some of the unit’s most important, and still classified, achievements.

[…]

Balding and bearded, Baum pursued math research while juggling government assignments, just like Simons. Over the course of several summers in the late 1960s, Baum and Lloyd Welch, an information theorist working down the hall, developed an algorithm to analyze Markov chains, which are sequences of events in which the probability of what happens next depends only on the current state, not past events. In a Markov chain, it is impossible to predict future steps with certainty, yet one can observe the chain to make educated guesses about possible outcomes…

A hidden Markov process is one in which the chain of events is governed by unknown, underlying parameters or variables. One sees the results of the chain but not the “states” that help explain the progression of the chain… Some investors liken financial markets, speech recognition patterns, and other complex chains of events to hidden Markov models.

The Baum-Welch algorithm provided a way to estimate probabilities and parameters within these complex sequences with little more information than the output of the processes…

Baum usually minimized the importance of his accomplishment. Today, though, Baum’s algorithm, which allows a computer to teach itself states and probabilities, is seen as one of the twentieth century’s notable advances in machine learning, paving the way for breakthroughs affecting the lives of millions in fields from genomics to weather prediction. Baum-Welch enabled the first effective speech recognition system and even Google’s search engine.

Zuckerman again:

Baum began working with Simons once a week. By 1979, Baum, then forty-eight years old, was immersed in trading, just as Simons had hoped. A top chess player in college, Baum felt he had discovered a new game to test his mental faculties. He received a one-year leave of absence from the IDA and moved his family to Long Island and a rented, three-bedroom Victorian house lined with tall bookcases…

It didn’t take Baum long to develop an algorithm directing Monemetrics to buy currencies if they moved a certain level below their recent trend line and sell if they veered too far above it. It was a simple piece of work, but Baum seemed on the right path, instilling confidence in Simons.

Zuckerman adds:

Baum became so certain their approach would work, and so hooked on investing, that he quit the IDA to work full-time with Simons.

Baum would receive 25 percent of the company’s profits. Simons would test strategies in Monemetrics. If they worked, he would implement them in a limited investment partnership he launched called Limroy, which included money from outside investors. Simons had tried to raise $4 million and had gotten close enough that he felt ready to launch. Zuckerman:

To make sure he and Baum were on the right track, Simons asked James Ax, his prized recruit at Stony Brook, to come by and check out their strategies. Like Baum a year or so earlier, Ax knew little about investing and cared even less. He immediately understood what his former colleagues were trying to accomplish, though, and became convinced they were onto something special. Not only could Baum’s algorithm succeed in currencies, Ax argued, but similar predictive models could be developed to trade commodities, such as wheat, soybeans, and crude oil. Hearing that, Simons persuaded Ax to leave academia, setting him up with his own trading account. Now Simons was really excited. He had two of the most acclaimed mathematicians working with him to unlock the secrets of the markets and enough cash to support their efforts.

One day Baum realized that Margaret Thatcher was keeping the British pound at an unsustainably low level.

Overcome with excitement, he rushed to the office to tell Simons. They started buying as much as they could of the British pound, which shot up in value. They then made accurate predictions for the Japanese Yen, West German deutsche mark, and Swiss franc. The fund made tens of millions of dollars. Zuckerman:

After racking up early currency winnings, Simons amended Limroy’s charter to allow it to trade US Treasury bond futures contracts as well as commodities. He and Baum–who now had their own, separate investment accounts–assembled a small team to build sophisticated models that might identify profitable trades in currency, commodity, and bond markets.

Simons was having a great time, but the fun wouldn’t last.

***

Simons needed someone to program their computers. He discovered Greg Hullender, a nineteen-year-old student at the California Institute of Technology. Simons offered Hullender $9,000 a year plus a share of the firm’s profits.

Limroy proceeded to lose money for the next six months. Simons was despondent. At one point he told Hullender, “Sometimes I look at this and feel I’m just some guy who doesn’t really know what he’s doing.” Zuckerman:

In the following days, Simons emerged from his funk, more determined than ever to build a high-tech trading system guided by algorithms, or step-by-step computer instructions rather than human judgment.

[…]

The technology for a fully automated system wasn’t there yet, Simons realized, but he wanted to try some more sophisticated methods.

Simons proceeded to launch a project of gathering as much past data as possible. He got commodity, bond, and currency prices going back decades, even before World War II in some cases. Zuckerman:

Eventually, the group developed a system that could dictate trades for various commodity, bond, and currency markets.

The system produced automated trade recommendations, a step short of automated trades, but the best they could do then. But soon Simons and Baum lost confidence in their system. They couldn’t understand why the system was making certain recommendations, and also they had another losing streak.

Simons and Baum drifted towards a more traditional investing approach. They looked for undervalued investments and also tried to get the news faster than others in order to react to it before others. They were investing about $30 million at that point. Zuckerman writes:

Their traditional trading approach was going so well that, when the boutique next door closed, Simons rented the space and punched through the adjoining wall. The new space was filled with offices for new hires, including an economist and others who provided expert intelligence and made their own trades, helping to boost returns. At the same time, Simons was developing a new passion: backing promising technology companies, including an electronic dictionary company called Franklin Electronic Publishers, which developed the first hand-held computer.

In 1982, Simons changed Monemetrics’ name to Renaissance Technologies Corporation, reflecting his developing interest in these upstart companies. Simons came to see himself as a venture capitalist as much as a trader.

Meanwhile, Baum excelled by relying on his own research and intuition:

He was making so much money trading various currencies using intuition and instinct that pursuing a systematic, “quantitative” style of trading seemed a waste of time. Building formulas was difficult and time-consuming, and the gains figured to be steady but never spectacular. By contrast, quickly digesting the office’s news ticker, studying newspaper articles, and analyzing geopolitical events seemed exciting and far more profitable.

Between July 1979 and March 1982, Baum made $43 million in profits, almost double his original stake from Simons.

Baum tended to hold on to his investments when he thought that a given trend would continue. Eventually this caused a rift between Baum and Simons. For example, in the fall of 1979, both Baum and Simons bought gold around $250 an ounce.

(Photo by Daniel Schreurs)

By January 1980, gold had soared past $700. Simons sold his position, but Baum held on thinking the trend would continue. Around this time, Simons learned that people were lining up to sell their physical gold–like jewelry–to take advantage of the high prices. Simons grew concerned that the increase in supply could crush gold prices.

When he got back in the office, Simons ordered Baum to sell. Baum refused. Baum was sitting on more than $10 million in profits and gold had shot past $800 an ounce. Baum was driving Simons crazy. Finally Simons called the firm’s broker and put the phone to Baum’s ear. Simons ordered Baum: “Tell him you’re selling.” Baum finally capitulated. Within months, gold shot past $865 an ounce and Baum was complaining that Simons had cost him serious money. But just a few months later, gold was under $500.

In 1983, Federal Reserve Chair Paul Volcker predicted a decline in interest rates, and inflation appeared to be under control. Baum purchased tens of millions of dollars of US bonds. However, panic selling overcame the bond market in the late spring of 1984 amid a large increase in bond issuance by the Reagan administration.

Once again, Simons told Baum to lighten up, but Baum refused. Baum also had a huge bet that the Japanese Yen would continue to appreciate. That bet was also backfiring. Zuckerman:

When the value of Baum’s investment positions had plummeted 40 percent, it triggered an automatic clause in his agreement with Simons, forcing Simons to sell all of Baum’s holdings and unwind their trading affiliation, a sad denouement to a decades-long relationship between the esteemed mathematicians.

Ultimately, Baum was very right about US bonds. By then, Baum was only trading for himself. Baum also returned to Princeton. He was now sleeping better and he had time for mathematics. Baum focused on prime numbers and the Riemann hypothesis. Also, for fun, he traveled the United States and competed in Go tournaments.

Meanwhile, Simons was upset about the losses. He considered just focusing on technology investing. He gave clients an opportunity to withdraw, but most kept faith that Simons would figure out a way to improve results.

 

CHAPTER FOUR

Simons came to the conclusion that Baum’s approach, using intellect and instinct, was not a reliable way to make money. Simons commented: “If you make money, you feel like a genius. If you lose, you’re a dope.” Zuckerman:

Simons wondered if the technology was yet available to trade using mathematical models and preset algorithms, to avoid the emotional ups and downs that come with betting on markets with only intelligence and intuition. Simons still had James Ax working for him, a mathematician who seemed perfectly suited to build a pioneering computer trading system. Simons resolved to back Ax with ample support and resources, hoping something special would emerge.

Zuckerman continues:

In 1961, Ax earned a PhD in mathematics from the University of California, Berkeley, where he became friends with Simons, a fellow graduate student. Ax was the first to greet Simons and his wife in the hospital after Barbara gave birth to their first child. As a mathematics professor at Cornell University, Ax helped develop a branch of pure mathematics called number theory. In the process, he forged a close bond with a senior, tenured academic named Simon Kochen, a mathematical logician. Together, the professors tried to prove a famous fifty-year-old conjecture made by the famed Austrian mathematician Emil Artin, meeting immediate and enduring frustration. To blow off steam, Ax and Kochen initiated a weekly poker game with colleagues and others in the Ithaca, New York, area. What started as friendly get-togethers, with winning pots that rarely topped fifteen dollars, grew in intensity until the men fought over stakes reaching hundreds of dollars.

[…]

Ax spent the 1970s searching for new rivals and ways to best them. In addition to poker, he took up golf and bowling, while emerging as one of the nation’s top backgammon players.

In 1979, Ax joined Simons. First Ax looked at fundamentals. Zuckerman:

Ax’s returns weren’t remarkable, so he began developing a trading system to take advantage of his math background. Ax mined the assorted data Simons and his team had collected, crafting algorithms to predict where various currencies and commodities were headed.

[…]

Ax’s predictive models had potential, but they were quite crude. The trove of data Simons and others had collected proved of little use, mostly because it was riddled with errors and faulty prices. Also, Ax’s trading system wasn’t in any way automated–his trades were made by phone, twice a day, in the morning and at the end of the trading day.

Ax soon began to rely on a former professor: Sandor Straus earned a PhD in mathematics from Berkeley in 1972 and went to teach at Stony Brook. Straus thrived. And he wore a long ponytail with John Lennon-style glasses. In 1976, Straus joined Stony Brook’s computer center. He helped Ax and others develop computer simulations. But Straus wasn’t happy and was worried about money. Simons offered to double Straus’s salary if he joined Monemetrics as a computer specialist.

Against the consistent advice of his father and friends, Straus eventually decided to join Monemetrics. Straus began collecting a wide range of data. Zuckerman:

No one had asked Straus to track down so much information. Opening and closing prices seemed sufficient to Simons and Ax. They didn’t even have a way to use all the data Straus was gathering, and with computer-processing power still limited, that didn’t seem likely to change. But Straus figured he’d continue collecting the information in case it came in handy down the road.

Straus became somewhat obsessive in his quest to locate pricing data before others realized its potential value. Straus even collected information on stock trades, just in case Simons’s team wanted it at some point in the future. For Straus, gathering data became a matter of personal pride.

Zuckerman adds:

…No one had told Straus to worry so much about the prices, but he had transformed into a data purist, foraging and cleaning data the rest of the world cared little about.

…Some other traders were gathering and cleaning data, but no one collected as much as Strauss, who was becoming something of a data guru.

Straus’s data helped Ax improve his trading results…

Simons asked Henry Laufer, another Stony Brook mathematician, to join Monemetrics. Laufer agreed. Zuckerman:

Laufer created computer simulations to test whether certain strategies should be added to their trading model. The strategies were often based on the idea that prices tend to revert after an initial move higher or lower. Laufer would buy futures contracts if they opened at unusually low prices compared with their previous closing price, and sell if prices began the day much higher than their previous close. Simons made his own improvements to the evolving system, while insisting that the team work together and share credit.

In 1985, Ax moved to Huntington Beach, California. Ax and Straus established a new company: Axcom Limited. Simons would get 25 percent of the profits, while Ax and Straus split the remaining 75 percent. Laufer didn’t want to move west, so he returned to Stony Brook. Zuckerman writes:

By 1986, Axcom was trading twenty-one different futures contracts, including the British pound, Swiss franc, deutsche mark, Eurodollars, and commodities including wheat, corn, and sugar. Mathematical formulas developed by Ax and Straus generated most of the firm’s moves, though a few decisions were based on Ax’s judgment calls. Before the beginning of trading each day, and just before the end of trading in the late afternoon, a computer program would send an electronic message to Greg Olsen, their broker at an outside firm, with an order and some simple conditions. One example: “If wheat opens above $4.25, sell 36 contracts.”

However, Simons and the team were not finding new ways to make money, nor were they improving on their existing methods, which allowed rivals to catch up. Zuckerman:

Eventually, Ax decided they needed to trade in a more sophisticated way. They hadn’t tried using more-complex math to build trading formulas, partly because the computing power didn’t seem sufficient. Now Ax thought it might be time to give it a shot.

Ax had long believed financial markets shared characteristics with Markov chains, those sequences of events in which the next event is only dependent on the current state. In a Markov chain, each step along the way is impossible to predict with certainty, but future steps can be predicted with some degree of accuracy if one relies on a capable model…

To improve their predictive models, Ax concluded it was time to bring in someone with experience developing stochastic equations, the broader family of equations to which Markov chains belong. Stochastic equations model dynamic processes that evolve over time and can involve a high level of uncertainty.

Soon Rene Carmona, a professor at University of California, Irvine, got a call from a friend who told him a group of mathematicians was looking for someone with his specialty–stochastic differential equations. Simons, Ax, and Straus were interested. Zuckerman:

Perhaps by hiring Carmona, they could develop a model that would produce a range of likely outcomes for their investments, helping to improve their performance.

Carmona was eager to lend a hand–he was consulting for a local aerospace company at the time and liked the idea of picking up extra cash working for Axcom a few days a week. The challenge of improving the firm’s trading results also intrigued him.

“The goal was to invent a mathematical model and use it as a framework to infer some consequences and conclusions,” Carmona says. “The name of the game is not to always be right, but to be right often enough.”

In 1987, after having made little progress, Carmona decided to spend the summer working full-time for Axcom. Yet Carmona still couldn’t generate useful results. Carmona soon realized that they needed regressions that could capture nonlinear relationships in market data. Zuckerman explains:

He suggested a different approach. Carmona’s idea was to have computers search for relationships in the data Strauss had amassed. Perhaps they could find instances in the remote past of similar trading environments, then they could examine how prices reacted. By identifying comparable trading situations and tracking what subsequently happened to prices, they could develop a sophisticated and accurate forecasting model capable of detecting hidden patterns.

For this approach to work, Axcom needed a lot of data, even more than Strauss and the others had collected. To solve the problem, Strauss began to model data rather than just collect it. In other words, to deal with gaps in historical data, he used computer models to make educated guesses as to what was missing. They didn’t have extensive cotton pricing data from the 1940s, for example, but maybe creating the data would suffice…

Carmona suggested letting the model run the show by digesting all the various pieces of data and spitting out buy-and-sell decisions. In a sense, he was proposing an early machine-learning system. The model would generate predictions for various commodity prices based on complex patterns, clusters, and correlations that Carmona and the others didn’t understand themselves and couldn’t detect with the naked eye.

Elsewhere, statisticians were using similar approaches–called kernel methods–to analyze patterns in data sets.

At first, Simons couldn’t get comfortable because he couldn’t understand why the model was reaching certain conclusions. Carmona told Simons to follow the data. Ax urged Simons to let the computers do it.

(Illustration by Dmitry Gorelkin)

Zuckerman:

When the Axcom team started testing the approach, they quickly began to see improved results. The firm began incorporating higher dimensional kernel regression approaches, which seemed to work best for trending models…

Simons was convinced they could do even better. Carmona’s ideas helped, but they weren’t enough. Simons called and visited, hoping to improve Axcom’s performance, but he mostly served as the pool operator, finding wealthy investors for the fund and keeping them happy, while attending to the various technology investments that made up about half of the $100 million assets now held by the firm.

Seeking even more mathematical firepower, Simons arranged for a well-respected academic to consult with the firm. That move would lay the groundwork for a historic breakthrough.

 

CHAPTER FIVE

Elwyn Berlekamp attended MIT. In his senior year, Berlekamp won a prestigious math competition to become a Putnam Fellow. While pursuing a PhD at MIT, he focused on electrical engineering, studying with Peter Elias and Claude Shannon. One day Shannon pulled Berlekamp aside and told him it wasn’t a good time to invest in the stock market. Berlekamp had no money, so he laughed. Also, he thought investing was a game where rich people play and that it doesn’t do much to improve the world.

During the summers of 1960 and 1962, Berlekamp worked as a research assistant at Bell Laboratories research center in Murray Hill, New Jersey. While there, he worked for John Larry Kelly, Jr. Kelly was a brilliant physicist who had been a pilot in the US Navy in World War II. Kelly smoked six packs of cigarettes a day and invented a betting system to bet on college and professional football. Kelly invented the Kelly criterion.

The Kelly criterion can be written as follows (Zuckerman doesn’t mention this, but it’s worth noting):

• • F = p – [q/o]

where

• • F = Kelly criterion fraction of current capital to bet
  • o = Net odds, or dollars won per $1 bet if the bet wins (e.g., the bet may pay 5 to 1, meaning you win $5 per each $1 bet if the bet wins)
  • p = probability of winning
  • q = probability of losing = 1 – p

The Kelly criterion has a unique mathematical property: if you know the probability of winning and the net odds (payoff), then betting exactly the percentage determined by the Kelly criterion leads to the maximum long-term compounding of capital, assuming that you’re going to make a long series of bets. Betting any percentage that is not equal to that given by the Kelly criterion will inevitably lead to lower compound growth over a long period of time.

Berlekamp finished his PhD at the University of California, Berkeley. He became an assistant professor of electrical engineering. Zuckerman:

Berlekamp became an expert in decoding digital information, helping NASA decipher images coming back from satellites exploring Mars, Venus, and other parts of the solar system. Employing principles he had developed studying puzzles and games… Berlekamp cofounded a branch of mathematics called combinatorial game theory and wrote a book called Algebraic Coding Theory, a classic in the field.

By the late 1960s, the Institute for Defense Analyses (IDA) hired Berlekamp. He met Simons, but the two didn’t hit it off, despite having both spent time at MIT, Berkeley, and IDA.

“His mathematics were different from mine,” Berlekamp says. “And Jim had an insatiable urge to do finance and make money. He likes action… He was always playing poker and fussing around with the markets.”

In 1973, Berlekamp became the part owner of a cryptography company. In 1985, Eastman Kodak acquired the company, giving Berlekamp an unexpected windfall. Berlekamp complained that the money caused challenges in his marriage. His wife wanted a bigger house, while he wanted to travel.

While trying to figure out how to invest the money, a friend told him to look at commodities. This led Berlekamp to contact Simons, who told him, “I have just the opportunity for you.” Berlekamp started flying to Huntington Beach a couple of times a month to learn to trade and to see if his expertise in statistical information theory might be useful. Zuckerman says:

For all the brainpower the team was employing, and the help they were receiving from Carmona and others, Axcom’s model usually focused on two simple and commonplace trading strategies. Sometimes, it chased prices, or bought various commodities that were moving higher or lower on the assumption that the trend would continue.

Other times, the model wagered that a price move was petering out and would reverse: a reversion strategy.

Ax had access to more extensive pricing information than his rivals, thanks to Straus’s growing collection of clean, historic data. Since price movements often resembled those of the past, that data enabled the firm to more accurately determine when trends were likely to continue and when they were ebbing. Computing power had improved and become cheaper, allowing the team to produce more sophisticated trading models, including Carmona’s kernel methods–the early, machine-learning strategy that had made Simons so uncomfortable. With those advantages, Axcom averaged annual gains of about 20 percent, topping most rivals.

Yet Simons kept asking why returns weren’t better. Adding to the tension, their rivals were multiplying.

With the Kelly criterion in mind, Berlekamp told Ax that when the odds were higher, they should make bigger bets. Ax said they would, but seemed noncommittal. Zuckerman:

Berlekamp discovered other problems with Axcom’s operations. The firm traded gold, silver, copper, and other metals, as well as hogs and other meats, and grains and other commodities. But their buy-and-sell orders were still placed through emailed instructions to their broker, Greg Olsen, at the open and close of each trading day, and Axcom often held on to investments for weeks or even months at a time.

That’s a dangerous approach, Berlekamp argued, because markets can be volatile. Infrequent trading precluded the firm from jumping on new opportunities as they arose and led to losses during extended downturns. Berlekamp urged Ax to look for smaller, short-term opportunities–get in and get out. Ax brushed him off again, this time citing the cost of doing rapid trading. Besides, Straus’s intraday price data was riddled with inaccuracies–he hadn’t fully “cleaned” it yet–so they couldn’t create a reliable model for short-term trades.

Meanwhile, some investors didn’t have faith in Simons’s venture-capital investments. So Simons closed it down in March 1988 and opened, with Ax, an offshore hedge fund focused solely on trading. They named the hedge fund Medallion because each had gotten a prestigious math award.

Within six months, Medallion was struggling. Part of it seemed to be due to the fact that Ax had gotten less focused. He moved to an isolated spot near Malibu and he rarely came in to the office even though he was managing nearly a dozen employees. Soon Ax purchased a spectacular home on a cliff in Pacific Palisades.

At one point in 1989, Medallion was down nearly 30 percent from the middle of the previous year. Simons ordered Axcom to halt all trading based on the firm’s struggling, longer-term predictive signals until Ax and his team developed a plan to improve results. In the meantime, Ax was only allowed to do short-term trading, which was just 10 percent of the fund’s activity.

Ax thought Simons’s order violated their partnership agreement. Ax was going to sue Simons. Technically, however, Axcom was trading for a general partnership controlled by Simons.

Then Berlekamp offered to buy Ax’s stake. Ax agreed. The deal left Berlekamp with a 40 percent stake in the firm, while Straus and Simons had 25 percent each. Ax still had 10 percent. But he effectively retired from trading. Ax moved to San Diego. He wrote poetry, took screenwriting classes, and wrote a science-fiction thriller.

 

CHAPTER SIX

Berlekamp moved the firm close to his home in Berkeley.

The team forged ahead, with Berlekamp focused on implementing some of the most promising recommendations Ax had ignored. Simons, exhausted from months of bickering with Ax, supported the idea.

“Let’s bank some sure things,” Berlekamp told Simons.

In addition to the issue of cost, short-term trading interested few investors because the gains were tiny. Zuckerman:

Berlekamp hadn’t worked on Wall Street and was inherently skeptical of long-held dogmas developed by those he suspected weren’t especially sophisticated in their analysis. He advocated for more short-term trades. Too many of the firm’s long-term moves had been duds, while Medallion’s short-term trades had proved its biggest winners, thanks to the work of Ax, Carmona, and others. It made sense to try to build on that success. Berlekamp also enjoyed some good timing–by then, most of Straus’s intraday data had been cleaned up, making it easier to develop fresh ideas for shorter-term trades.

Their goal remained the same: scrutinize historic price information to discover sequences that might repeat, under the assumption that investors will exhibit similar behavior in the future.

Berlekamp also observed that doing a higher number of shorter-term trades meant that no individual trade could hurt results. This reduces the portfolio’s risk. Berlekamp and his colleagues view Medallion as being like a casino.

“If you trade a lot, you only need to be right 51 percent of the time,” Berlekamp argued to a colleague. “We need a smaller edge on each trade.”

Zuckerman writes:

Simons and his researchers didn’t believe in spending much time proposing and testing their own intuitive trade ideas. They let the data point them to the anomalies signaling opportunity. They also didn’t think it made sense to worry about why these phenomena existed. All that mattered was that they happened frequently enough to include in their updated trading system, and that they could be tested to ensure they weren’t statistical flukes.

Zuckerman again:

Beyond the repeating sequences that seemed to make sense, the system Berlekamp, Laufer, and Straus developed spotted barely perceptible patterns in various markets that had no apparent explanation. These trends and oddities sometimes happened so quickly that they were unnoticeable to most investors. They were so faint, the team took to calling them ghosts, yet they kept reappearing with enough frequency to be worth additions to their mix of trade ideas. Simons had come around to the view that the whys didn’t matter, just that the trades worked.

[…]

By late 1989, after about six months of work, Berlekamp and his colleagues were reasonably sure their rebuilt trading system–focused on commodity, currency, and bond markets–could prosper.

[…]

The firm implemented its new approach in late 1989 with the $27 million Simons still managed. The results were almost immediate, startling nearly everyone in the office. They did more trading than ever, cutting Medallion’s average holding time to just a day and a half from a week and a half, scoring profits almost every day.

At one point, Medallion had every one of its positions with the Stotler Group, a commodity-trading firm run by Karsten Mahlmann. There were rumors Stotler was in trouble. Berlekamp wasn’t sure what to do. But Simons was. He ordered Berlekamp to move all their positions to a different broker. So he did. Soon Stotler filed for bankruptcy. Zuckerman:

Simons and his firm had narrowly escaped a likely death blow.

***

Zuckerman writes:

For much of 1990, Simons’s team could do little wrong, as if they had discovered a magic formula after a decade of fumbling around in the lab. Rather than transact only at the open and close of trading each day, Berlekamp, Laufer, and Strauss traded at noon, as well. Their system became mostly short-term moves, with long-term trades representing about 10 percent of activity.

One day, Axcom made more than $1 million, a first for the firm. Simons rewarded the team with champagne, much as the IDA’s staff had passed around flutes of bubbly after discovering solutions to thorny problems.

(Photo by William MacGregor)

Zuckerman:

Medallion scored a gain of 55.9 percent in 1990, a dramatic improvement on its 4 percent loss the previous year. The profits were especially impressive because they were over and above the hefty fees charged by the fund, which amounted to 5 percent of all assets managed and 20 percent of all gains generated by the fund.

Simons was convinced that the team had discovered a highly profitable strategy. Nonetheless, he kept messing around with trade ideas like gold. Zuckerman:

Berlekamp was baffled. It was Simons who had pushed to develop a computerized trading system free of human involvement, and it was Simons who wanted to rely on the scientific method, testing overlooked anomalies rather than using crude charts or gut instinct. Berlekamp, Laufer, and the rest of the team had worked diligently to remove humans from the trading loop as much as possible. Now Simons was saying he had a good feeling about gold prices and wanted to tweak the system?

[…]

“We must still rely on human judgment and manual intervention to cope with a drastic, sudden change,” [Simons] explained in a letter [to clients] that month.

Simons told Berlekamp how much better the fund should be doing. Simons was convinced the $40 million fund could achieve remarkable success. Berlekamp didn’t think the fund could do much better than it already had. Eventually Berlekamp–who was enjoying teaching at Berkeley more than ever–suggested to Simons that Simons buy him out. Zuckerman:

Which is exactly what Simons did. In December 1990, Axcom was disbanded; Simons purchased Berlekamp’s ownership interest for cash, while Strauss and Ax traded their Axcom stakes for shares in Renaissance, which began to manage the Medallion fund. Berlekamp returned to Berkeley to teach and do full-time math research, selling his Axcom shares at a price that amounted to six times what he had paid just sixteen months earlier…

“It never occurred to me that we’d go through the roof,” Berlekamp says.

 

CHAPTER SEVEN

Zuckerman writes:

Like the technical traders before him, Simons practiced a form of pattern analysis and searched for telltale sequences and correlations in market data. He hoped to have a bit more luck than investors before him by doing his trading in a more scientific manner, however. Simons agreed with Berlekamp that technical indicators were better at guiding short-term trades than long-term investments. But Simons hoped rigorous testing and sophisticated predictive models, based on statistical analysis rather than eyeballing price charts, might help him escape the fate of the chart adherents who had crashed and burned.

But Simons didn’t realize that others were busy crafting similar strategies, some using their own high-powered computers and mathematical algorithms. Several of these traders already had made enormous progress, suggesting that Simons was playing catch-up.

Zuckerman:

Edward Thorp became the first modern mathematician to use quantitative strategies to invest sizable sums of money. Thorp was an academic who had worked with Claude Shannon, the father of information theory, and embraced the proportional betting system of John Kelly, the Texas scientist who had influenced Elwyn Berlekamp.

By the late 1980s, Thorp’s fund had $300 million under management, while Simons’s Medallion fund had only $25 million. Thorp’s fund traded warrants, options, convertible bonds, and other derivative securities.

I wrote about Thorp’s autobiography,A Man for All Markets, here: https://boolefund.com/man-for-all-markets/

***

Gerry Bamburger, a computer-science graduate of Columbia University, gave technical support to Morgan Stanley’s stock traders. Zuckerman:

Bamburger sensed opportunity. If the bank created a database tracking the historic prices of various paired stocks, it could profit simply by betting on the return of these price-spreads to their historic levels after block trades or other unusual activity. Bamburger’s bosses were swayed, setting him up with half a million dollars and a small staff. Bamburger began developing computer programs to take advantage of “temporary blips” of paired shares… By 1985, he was implementing his strategy with six or seven stocks at a time, while managing $30 million, scoring profits for Morgan Stanley.

Morgan Stanley gave Bamburger a new boss: Nunzio Tartaglia, an astrophysicist. This prompted Bamburger to quit Morgan Stanley and join Ed Thorp’s hedge fund.

Meanwhile, Tartaglia renamed his group Automated Proprietary Trading (APT). Zuckerman:

New hires, including a former Columbia University computer-science professor named David Shaw and mathematician Robert Frey, improved profits. The Morgan Stanley traders became some of the first to embrace the strategy of statistical arbitrage, or stat arb…. The team’s software ranked stocks by their gains or losses over the previous weeks, for example. APT would then sell short, or bet against, the top 10 percent of the winners within an industry while buying the bottom 10 percent of the losers on the expectation that these trading patterns would revert.

Eventually, APT suffered losses. Morgan Stanley ended up shutting the group down. Zuckerman comments:

It wouldn’t be clear for many years, but Morgan Stanley had squandered some of the most lucrative trading strategies in the history of finance.

***

One of the Medallion fund’s chief competitors was a fund run by David Shaw, who had been a part of Morgan Stanley’s APT group. Zuckerman:

Shaw, a supercomputing expert, hired math and science PhDs who embraced his scientific approach to trading. He also brought on whip-smart employees from different backgrounds. English and philosophy majors were among Shaw’s favorite hires, but he also hired a chess master, stand-up comedians, published writers, an Olympic-level fencer, a trombone player, and a demolitions specialist.

Soon Shaw’s fund became successful and was managing several hundred million dollars. Jim Simons wasn’t sure what methods Shaw was using, but he did realize that he needed to hire more help in order to catch up. Simons contacted Donald Sussman, a hedge-fund manager who had helped Shaw launch his fund. Perhaps Sussman would give Simons a similar boost.

 

CHAPTER EIGHT

Zuckerman writes about Simons meeting with Sussman:

Simons had discarded a thriving academic career to do something special in the investing world. But, after a full decade in the business, he was managing barely more than $45 million, a mere quarter the assets of Shaw’s firm. The meeting had import–backing from Sussman could help Renaissance hire employees, upgrade technology, and become a force on Wall Street.

Simons’s presentation to Sussman went well. But in the end, because Sussman was the sole source of capital for D. E. Shaw–which was generating 40 percent returns per year–Sussman decided not to invest in Renaissance. Simons approached other potential backers, but no one would invest. Most thought it was crazy to rely on trading models generated by computers. Many also thought Simons’s fees were too high. Simons charged 5 percent of assets plus 20 percent of profits, whereas most hedge funds at the time charged 2 percent of assets plus 20 percent of profits. Perhaps most importantly, Renaissance had fewer than two years of impressive performance.

Simons knew he needed to hire more help. He turned to the mathematician Henry Laufer. Zuckerman:

Joining Stony Brook’s math department in 1971, Laufer focused on complex variables and algebraic geometry, veering away from classical areas of complex analysis to develop insights into more contemporary problems.

In 1992, Laufer joined Simons as a full-time employee. Zuckerman:

Laufer made an early decision that would prove extraordinarily valuable: Medallion would employ a single trading model rather than maintain various models for different investments and market conditions, a style most quantitative firms would embrace. A collection of trading models was simpler and easier to pull off, Laufer acknowledged. But, he argued, a single model could draw on Straus’s vast trove of pricing data, detecting correlations, opportunities, and other signals across various asset classes. Narrow, individual models, by contrast, can suffer from too little data.

Just as important, Laufer understood that a single, stable model based on some core assumptions about how prices and markets behave would make it easier to add new investments later on.

Zuckerman continues:

Simons wondered if there might be a better way to parse their data trove. Perhaps breaking the day up into finer segments might enable the team to dissect intraday pricing information and unearth new, undetected patterns. Laufer… eventually decided five-minute bars were the ideal way to carve things up. Crucially, Straus now had access to improved computer-processing power, making it easier for Laufer to compare small slices of historic data…

Laufer’s five-minute bars gave the team the ability to identify new trends, oddities, and other phenomena, or, in their parlance, nonrandom trading effects. Straus and others conducted tests to ensure they hadn’t mined so deeply into their data that they had arrived at bogus trading strategies, but many of the new signals seemed to hold up.

Zuckerman adds:

Simons was challenging them to solve yet another vexing problem: Given the range of possible trades they had developed and the limited amount of money that Medallion managed, how much should the bet on each trade? And which moves should they pursue and prioritize? Laufer began developing a computer program to identify optimal trades throughout the day, something Simons began calling his betting algorithm. Laufer decided it would be “dynamic,” adapting on its own along the way and relying on real-time analysis to adjust the fund’s mix of holdings given the probabilities of future market moves–an early form of machine learning.

Simons, with about a dozen employees, realized that he still needed to hire more people in order to take on D. E. Shaw and other top funds. One person Simons hired was mathematician and programmer Nick Patterson. His coding ability gave him an advantage over other mathematicians. He graduated from the University of Cambridge.

(Photo by Cristian Saulean)

Patterson was a strong chess player and was also a stud at poker.

(Photo by Krastiu Vasilev)

After completing graduate school, Patterson worked as a cryptologist for the British government, where he made use of Bayes’s theorem of probability.

Because computing power was expanding exponentially, Patterson thought that Simons had a chance to revolutionize investing by applying high-level math and statistics.

Patterson began working on how to reduce trading costs:

…Laufer and Patterson began developing sophisticated approaches to direct trades to various futures exchanges to reduce the market impact of each trade. Now Medallion could better determine which investments to pursue, a huge advantage as it began trading new markets and investments. They added German, British, and Italian bonds, then interest-rate contracts in London, and, later, futures on Nikkei Stock Average, Japanese government bonds, and more.

The fund began trading more frequently. Having first sent orders to a team of traders five times a day, it eventually increased to sixteen times a day, reducing the impact on prices by focusing on the periods when there was the most volume.

Medallion increased 71 percent in 1994. This was especially impressive because the Federal Reserve had hiked interest rates several times, which led to losses for many investors. Simons still didn’t know why their models were working. He told a colleague: “I don’t know why planets orbit the sun. That doesn’t mean I can’t predict them.” Zuckerman:

At the time, most academics were convinced markets were inherently efficient, suggesting that there were no predictable ways to beat the market’s return, and that the financial decision-making of individuals was largely rational. Simons and his colleagues sensed the professors were wrong. They believed investors are prone to cognitive biases, the kinds that lead to panics, bubbles, boom, and busts.

Simons didn’t realize it, but a new strain of economics was emerging that would validate his instincts. In the 1970s, Israeli psychologists Amos Tversky and Daniel Kahneman had explored how individuals make decisions, demonstrating how prone most are to act irrationally. Later, economist Richard Thaler used psychological insights to explain anomalies in investor behavior, spurring the growth of the field of behavioral economics, which explored the cognitive biases of individuals and investors.

Link to my blog post on cognitive biases: https://boolefund.com/cognitive-biases/

(Illustration by Alain Lacroix)

Link to my blog post on the psychology of misjudgment: https://boolefund.com/the-psychology-of-misjudgment/

Simons saw how successful Medallion’s updated approach was, and he remembered how difficult it was for Baum, Ax, and himself to profit from instincts. As a result, Simons committed to not overriding the model. Medallion’s model profited from the errors and overreactions of other investors. Medallion’s core assumption was that investors will behave in the future like they did in the past.

Other investors at long last began noticing Medallion’s stellar results. Some of these investors made an investment in Medallion. By that point, Medallion was sharing its track record, but it wasn’t sharing much about how their trading system worked because it didn’t want rivals to catch on.

By the end of 1993, Medallion had $280 million under management. Simons grew concerned the they wouldn’t be as profitable if they got too big. So he decided, for the time being, not to let new investors into the fund. Furthermore, Simons got even more secretive about Medallion’s trading model. Simons even pressured his investors not to divulge any information about the fund’s operations.

Zuckerman writes:

Medallion was still on a winning streak. It was scoring big profits trading futures contracts and managed $600 million, but Simons was convinced the hedge fund was in a serious bind. Laufer’s models, which measured the fund’s impact on the market with surprising precision, concluded that Medallion’s returns would wane if it managed much more money. Some commodity markets, such as grains, were just too small to handle additional buying and selling by the fund without pushing prices around. There were also limitations to how much more Medallion could do in bigger bond and currency markets.

Moreover:

Simons worried his signals were getting weaker as rivals adopted similar strategies.

Yet Simons continued to search for ways to grow the fund. There was only one way to expand: start investing in stocks. The trouble was that Medallion had never developed any model to invest in stocks profitably.

Simons’s son Paul battled a birth disorder–ectodermal dysplasia. Paul worked almost every day to strengthen his body, constantly doing push-ups and pull-ups. Paul was also an accomplished skier and endurance bicycle rider. One day Paul was taking a fast ride through Old Field Road in Setauket, near the home where he grew up. An elderly woman backed out of her driveway, out of nowhere, and crushed Paul, killing him instantly. Jim Simons and Barbara were completely devastated.

 

CHAPTER NINE

Zuckerman comments:

When it came to stocks, Simons seemed well out of his depth.

Most successful stock investors at the time focused on fundamentals, poring over financial statements in order to understand things such as assets, liabilities, sales, earnings, and cash flows.

Nick Patterson began a side job he liked: recruiting talent for Renaissance. Zuckerman:

One day, after reading in the morning paper that IBM was slashing costs, Patterson became intrigued. He was aware of the accomplishments of the computer giant’s speech-recognition group and thought their work bore similarity to what Renaissance was doing. In early 1993, Patterson sent separate letters to Peter Brown and Robert Mercer, deputies of the group, inviting them to visit Renaissance’s offices to discuss potential positions.

Zuckerman explains that Robert Mercer’s passion for computers had been sparked by his father Thomas:

It began the very moment Thomas showed Robert the magnetic drum and punch cards of an IBM 650, one of the earliest mass-produced computers. After Thomas explained the computer’s inner workings to his son, the ten-year-old began creating his own programs, filling up an oversize notebook. Bob carried that notebook around for years before he ever had access to an actual computer.

At Sandia High School and the University of New Mexico, Mercer was a member of the chess, auto, and Russian clubs. He was low-key, but he came alive for mathematics. In 1964, he and two classmates won top honors in a national mathematics contest. Zuckerman:

While studying physics, chemistry, and mathematics at the University of New Mexico, Mercer got a job at a weapons laboratory at the Kirkland Air Force Base eight miles away, just so he could help program the base’s supercomputer.

[…]

…Mercer spent the summer on the lab’s mainframe computer rewriting a program that calculated electromagnetic fields generated by nuclear fusion bombs. In time, Mercer found ways to make the program one hundred times faster, a real coup. Mercer was energized and enthused, but his bosses didn’t seem to care about his accomplishment. Instead of running the old computations at the new, faster speed, they instructed Mercer to run computations that were one hundred times the size. It seemed Mercer’s revved-up speed made little difference to them, an attitude that helped mold the young man’s worldview.

[…]

He turned cynical, viewing government as arrogant and inefficient.

Mercer earned a PhD in computer science from the University of Illinois, then joined IBM and its speech-recognition group in 1972.

***

Peter Brown’s father Henry Brown introduced the world’s first money-market mutual fund. Few investors showed any interest, however. Henry worked every day except Christmas in 1972, when Peter was seventeen. Zuckerman:

His lucky break came the next year in the form of a New York Times article about the fledgling fund. Clients began calling, and soon Henry and his partner were managing $100 million in their Reserve Primary Fund. The fund grew, reaching billions of dollars, but Henry resigned, in 1985, to move [with his wife] to the Brown family’s farm in a Virginia hamlet, where he raised cattle on five hundred acres.

Zuckerman continues:

Peter reserved his own ambitions for science and math. After graduating from Harvard University with an undergraduate degree in mathematics, Brown joined a unit of Exxon that was developing ways to translate spoken language into computer text, an early form of speech-recognition technology. Later, he’d earn a PhD in computer science from Carnegie Mellon University in Pittsburgh.

In 1984, Brown joined IBM’s speech-recognition group. Zuckerman:

Brown, Mercer, and their fellow mathematicians and scientists, including the group’s hard-driving leader, Fred Jelinek, viewed language very differently from the traditionalists. To them, language could be modeled like a game of chance. At any point in a sentence, there exists a certain probability of what might come next, which can be estimated based on past, common usage…

Their goal was to feed their computers with enough data of recorded speech and written text to develop a probabilistic, statistical model capable of predicting likely word sequences based on sequences of sounds…

In mathematical terms, Brown, Mercer, and the rest of Jelinek’s team viewed sounds as the output of a sequence in which each step along the way is random, yet dependent on the previous step–a hidden Markov model. A speech-recognition system’s job was to take a set of observed sounds, crunch the probabilities, and make the best possible guess about the “hidden” sequences of words that could have generated those sounds. To do that, the IBM researchers employed the Baum-Welch algorithm–codeveloped by Jim Simons’s early trading partner Lenny Baum–to zero in on the various language probabilities. Rather than manually programming in static knowledge about how language worked, they created a program that learned from data.

Mercer jumped rope to stay in shape. He had a hyper-efficient style of communication, usually saying nothing and otherwise saying only a few words. Brown, by contrast, was more approachable and animated.

Jelinek created a ruthless and fierce culture. Zuckerman:

Researchers would posit ideas and colleagues would do everything they could to eviscerate them, throwing personal jabs along the way. They’d fight it out until reaching a consensus on the merits of the suggestion… It was no-holds-barred intellectual combat.

…Brown stood out for having unusual commercial instincts, perhaps the result of his father’s influence. Brown urged IBM to use the team’s advances to sell new products to customers, such as a credit-evaluation service, and even tried to get management to let them manage a few billion dollars of IBM’s pension-fund investments with their statistical approach, but failed to garner much support.

[…]

At one point, Brown learned of a team of computer scientists, led by a former Carnegie Mellon classmate, that was programming a computer to play chess. He set out to convince IBM to hire the team. One winter day, while Brown was in an IBM men’s room, he got to talking with Abe Peled, a senior IBM research executive, about the exorbitant cost of the upcoming Super Bowl’s television commercials. Brown said he had a way to get the company exposure at a much lower cost–hire the Carnegie Mellon team and reap the resulting publicity when their machine beat a world champion in chess…

The IBM brass loved the idea and hired the team, which brought its Deep Thought program along.

Mercer was skeptical that hedge funds create good for society. But he agreed to visit Renaissance and he was impressed that the company seemed to be about science. Also, both Mercer and Brown were not paid much at IBM. Brown, for his part, became more interested when he learned that Simons’s had worked with Lenny Baum, coinventor of the Baum-Welch algorithm.

Simons offered to double their salaries. Mercer and Brown joined Renaissance in 1993.

 

CHAPTER TEN

Brown suggested that Renaissance interview David Magerman, whom Brown knew from IBM. Magerman’s specialty was programming, which Renaissance needed. Soon Renaissance hired Magerman.

Magerman had a difficult upbringing, growing up without much money. His father was a math whiz who’d never been able to develop his talents. He took it out on his son. Magerman was left with a desire to earn praise from people in power, some of whom Magerman saw as father figures. He also seemed to pick fights unnecessarily. Zuckerman:

“I needed to right wrongs and fight for justice, even if I was turning molehills into mountains,” Magerman acknowledges. “I clearly had a messiah complex.”

Magerman studied mathematics and computer science at the University of Pennsylvania. He excelled. Zuckerman:

At Stanford University, Magerman’s doctoral thesis tackled the exact topic Brown, Mercer, and other IBM researchers were struggling with: how computers could analyze and translate language using statistics and probability.

Although Simons had initially had Brown and Mercer working in different areas of Renaissance, the pair had been working together in their spare time on how to fix Renaissance’s stock-trading system. Soon Mercer figured out the key problem. Simons let Mercer join Brown in the stock-research area. Zuckerman:

The Brown-Mercer reunion represented a new chapter in an unusual partnership between two scientists with distinct personalities who worked remarkably well together. Brown was blunt, argumentative, persistent, loud, and full of energy. Mercer conserved his words and rarely betrayed emotion, as if he was playing a never-ending game of poker. The pair worked, though, yin with yang.

[…]

…[While at IBM, they] developed a certain work style–Brown would quickly write drafts of their research and then pass them to Mercer, a much better writer, who would begin slow and deliberate rewrites.

Brown and Mercer threw themselves into their new assignment [to revamp Renaissance’s stock-trading model.] They worked late into the evening and even went home together; during the week they shared a living space in the attic of a local elderly woman’s home, returning to their families on weekends. Over time, Brown and Mercer discovered methods to improve Simons’s stock-trading system.

Zuckerman:

[Brown and Mercer] decided to program the necessary limitations and qualifications into a single trading system that could automatically handle all potential complications. Since Brown and Mercer were computer scientists, and they had spent years developing large-scale software projects at IBM and elsewhere, they had the coding chops to build a single automated system for trading stocks.

[…]

Brown and Mercer treated their challenge as a math problem, just as they had with language recognition at IBM. Their inputs were the fund’s trading costs, its various leverages, risk parameters, and assorted other limitations and requirements. Given all those factors, they built the system to solve and construct an ideal portfolio, making optimal decisions, all day long, to maximize returns.

Zuckerman writes:

The beauty of the approach was that, by combining all their trading signals and portfolio requirements into a single, monolithic model, Renaissance could easily test and add new signals, instantly knowing if the gains from a potential new strategy were likely to top its costs. They also made their system adaptive, or capable of learning and adjusting on its own…

If the model’s recommended trades weren’t executed, for whatever reason, it self-corrected, automatically searching for buy-or-sell orders to nudge the portfolio back where it needed to be… The system repeated on a loop several times an hour, conducting an optimization process that weighed thousands of potential trades before issuing electronic trade instructions. Rivals didn’t have self-improving models; Renaissance now had a secret weapon, one that would prove crucial to the fund’s future success.

Eventually, Brown and Mercer developed an elaborate stock-trading system that featured a half million lines of code, compared to tens of thousands of lines in [the] old system. The new system incorporated all necessary restrictions and requirements; in many ways, it was just that kind of automated trading system Simons had dreamed of years earlier. Because [the] fund’s stock trades were now less sensitive to the market’s fluctuations, it began holding on to shares a bit longer, two days or so, on average.

…[The model] continued to identify enough winning trades to make serious money, usually by wagering on reversions after stocks got out of whack. Over the years, Renaissance would add twists to this bedrock strategy, but, for more than a decade, those would just be second order complements to the firm’s core reversion-to-the-mean predictive signals.

However, there were problems early on. Zuckerman:

It soon became clear that the new stock-trading system couldn’t handle much money, undermining Simons’s original purpose in pushing into equities. Renaissance placed a puny $35 million in stocks; when more money was traded, the gains dissipated… Even worse, Brown and Mercer couldn’t figure out why their system was running into so many problems.

Brown and Mercer began putting together the old team from IBM, including Magerman.

Simons gave Brown and Mercer six months to get the fund’s trading system working.

Magerman started spending all his free time trying to fix the trading system. He basically lived at the office. Eventually he identified two errors and was able to fix them. Magerman told Brown, who didn’t seem too excited. Magerman then showed Mercer, who had written all the code. Mercer checked Magerman’s work and then told him that he was right.

Brown and Mercer restarted the system and profits immediately started coming in.

 

CHAPTER ELEVEN

Renaissance’s trading system gained 21 percent in 1997, lower than the 32, 38, and 71 percent gained in 1996, 1995, and 1994. The system still had issues.

Simons drew on his experiences with the IDA and also from when he managed talented mathematicians at Stony Brook. One lesson Simons had learned was the importance of having researchers work together. Zuckerman:

…Medallion would have a single, monolithic trading system. All staffers enjoyed full access to each line of the source code underpinning their moneymaking algorithms, all of it readable in cleartext on the firm’s internal network. There would be no corners of the code accessible only to top executives; anyone could make experimental modifications to improve the trading system. Simons hoped his researchers would swap ideas, rather than embrace private projects…

Simons created a culture of unusual openness. Staffers wandered into colleagues’ offices offering suggestions and initiating collaborations. When they ran into frustrations, the scientists tended to share their work and ask for help, rather than move on to new projects, ensuring the promising ideas weren’t “wasted,” as Simons put it. Groups met regularly, discussing intimate details of their progress and fielding probing questions from Simons… Once a year, Simons paid to bring employees and their spouses to exotic vacation locales, strengthening the camaraderie.

Furthermore, peer pressure was used. Employees were often working on presentations, and felt the need to try to impress one another.

Simons gave all employees the chance to share in Renaissance’s profits based on clear and transparent formulas. Zuckerman notes:

Simons began sharing equity, handing a 10 percent stake in the firm to Laufer and, later, giving sizable slices to Brown, Mercer, and Mark Silber, who was now the firm’s chief financial officer, and others, steps that reduced Simons’s ownership to just over 50 percent. Other top-performing employees could buy shares, which represented equity in the firm. Staffers also could invest in Medallion, perhaps the biggest perk of them all.

Hiring wasn’t easy. To attract scientists and mathematicians, Renaissance employees would emphasize certain positive parts of their job. Solving puzzles was fun. There was camaraderie. And things moved at a fast pace. Zuckerman:

“You have money in the bank or not, at the end of the day,” Mercer told science writer Sharon McGrayne. “You don’t have to wonder if you succeeded… it’s just a very satisfying thing.”

Zuckerman continues:

By 1997, Medallion’s staffers had settled on a three-step process to discover statistically significant moneymaking strategies, or what they called their trading signals. Identify anomalous patterns in historic pricing data; make sure the anomalies were statistically significant, consistent over time, and nonrandom; and see if the identified pricing behavior could be explained in a reasonable way.

[…]

…more than half of the trading signals Simons’s team was discovering were nonintuitive, or those they couldn’t fully understand. Most quant firms ignore signals if they can’t develop a reasonable hypothesis to explain them, but Simons and his colleagues never liked spending too much time searching for the causes of market phenomena. If their signals met various measures of statistical strength, they were comfortable wagering on them. They only steered clear of the most preposterous ideas.

Zuckerman adds:

It’s not that they wanted trades that didn’t make any sense; it’s just that these were the statistically valid strategies they were finding. Recurring patterns without apparent logic to explain them had an added bonus: They were less likely to be discovered and adopted by rivals, most of whom wouldn’t touch these kinds of trades.

The danger was that unexplainable signals could be simple coincidences. Zuckerman:

Often, the Renaissance researchers’ solution was to place such head-scratching signals in their trading system, but to limit the money allocated to them, at least at first, as they worked to develop an understanding of why the anomalies appeared. Over time, they frequently discovered reasonable explanations, giving Medallion a leg up on firms that had dismissed the phenomena. They ultimately settled on a mix of sensible signals, surprising trades with strong statistical results, and a few bizarre signals so reliable they couldn’t be ignored.

Zuckerman again:

By then, Medallion increasingly was relying on strategies that its system taught itself, a form of machine learning. The computers, fed with enough data, were trained to spit out their own answers. A consistent winner, for example, might automatically receive more cash, without anyone approving the shift or even being aware of it.

Simons developed enough enthusiasm and confidence in the future of Renaissance that he moved the firm to a new office. The compound was wood and glass. Each office had a view of the woods. There was a gym, lighted tennis courts, a library with a fireplace, and a large auditorium where Simons hosted biweekly seminars from visiting scholars (usually having little to do with finance). The cafeteria and common areas were large, with plenty of whiteboards, so that staffers could meet, discuss, and debate.

Zuckerman describes Brown and Mercer:

Intense and energetic, Brown hustled from meeting to meeting, riding a unicycle through the halls and almost running over colleagues. Brown worked much of the night on a computer near the Murphy bed in his office, grabbing a nap when he tired.

[…]

Analytical and unemotional, Mercer was a natural sedative for his jittery partner. Mercer worked hard, but he liked to go home around six p.m.

One important factor of Renaissance’s success, as Zuckerman explains:

If a strategy wasn’t working, or when market volatility surged, Renaissance’s system tended to automatically reduce positions and risk.

This contrasted with many other hedge funds, including Long-Term Capital Management (LTCM), which tended to hold or to increase positions that had gone against them in the belief that they would ultimately be proved right. In the summer of 1998, LTCM blew up when its positions went against the fund. Traders at LTCM were more right than wrong, but because they had a great deal of leverage and because they maintained or increased their bets, they simply didn’t have enough capital to survive. Renaissance wouldn’t make this mistake because, as noted, the fund typically reduced positions and risk if a strategy wasn’t working or if market volatility picked up.

Despite all the improvements, Renaissance’s stock-trading system still only generated about 10 percent of the fund’s profits.

In March 2000, Medallion encountered major difficulties. Zuckerman:

It wasn’t just the mounting losses that had everyone concerned–it was the uncertainty over why things were so bad. The Medallion portfolio held commodities, currencies, and bond futures, and its stock portfolio was largely composed of offsetting positions aimed at sidestepping broad market moves. The losses shouldn’t be happening. But because so many of the system’s trading signals had developed on their own through a form of machine learning, it was hard to pinpoint the exact cause of the problems or when they might ebb; the machines seemed out of control.

Brown was freaking out and barely sleeping. Magerman felt nauseous. Mercer tried to keep his composure. But many were very worried.

Simons urged his team to stick with the model. After more all-nighters, a few researchers thought they figured out the problem. If certain stocks rallied in the preceding weeks, Medallion’s system would buy them on the expectation that the rally would continue. But as the market sold off, this momentum strategy wasn’t working at all. So the team put a halt to the fund’s momentum strategy. Immediately, Medallion began making money again. Zuckerman comments:

By the fall of 2000, word of Medallion’s success was starting to leak out. That year, Medallion soared 99 percent, even after it charged clients 20 percent of their gains and 5 percent of the money invested with Simons. Over the previous decade, Medallion and its 140 employees had enjoyed a better performance than funds managed by George Soros, Julian Robertson, Paul Tudor Jones, and other investing giants. Just as impressive, Medallion had recorded a Sharpe ratio of 2.5 in its most recent five-year period, suggesting the fund’s gains came with low volatility and risk compared with those of many competitors.

 

Part Two: Money Changes Everything

CHAPTER TWELVE

Zuckerman writes:

Something unusual was going on at Jim Simons’s hedge fund in 2001.

Profits were piling up as Renaissance began digesting new kinds of information. The team collected every trade order, including those that hadn’t been completed, along with annual and quarterly earnings reports, records of stock trades by corporate executives, government reports, and economic predictions and papers.

[…]

Soon, researchers were tracking newspaper and newswire stories, internet posts, and more obscure data–such as offshore insurance claims–racing to get their hands on pretty much any information that could be quantified and scrutinized for its predictive value. The Medallion fund became something of a data sponge, soaking up a terabyte, or one trillion bytes, of information annually, buying expensive disc drives and processors to digest, store, and analyze it all, looking for reliable patterns.

“There’s no data like more data,” Mercer told a colleague, an expression that became the firm’s hokey mantra.

Renaissance’s goal was to predict the price of a stock or other investment “at every point in the future,” Mercer later explained. “We want to know in three seconds, three days, three weeks, and three months.”

Zuckerman again:

It became clear to Mercer and others that trading stocks bore similarities to speech recognition, which was part of why Renaissance continued to raid IBM’s computational linguistics team. In both endeavors, the goal was to create a model capable of digesting uncertain jumbles of information and generating reliable guesses about what might come next–while ignoring traditionalists who employed analysis that wasn’t nearly as data driven.

[…]

…After soaring 98.5 percent in 2000, the Medallion fund rose 33 percent in 2001. By comparison, the S&P 500, the commonly used barometer of the stock market, managed a measly average gain of 0.2 percent over those two years, while rival hedge funds gained 7.3 percent.

Zuckerman adds:

Investment professionals generally judge a portfolio’s risk by its Sharpe ratio, which measures returns in relation to volatility; the higher one’s Sharpe, the better. For most of the 1990s, Medallion had a strong Sharpe ratio of about 2.0, double the level of the S&P 500. But adding foreign-market algorithms and improving Medallion’s trading techniques sent its Sharpe soaring to about 6.0 in early 2003, about twice the ratio of the largest quant firms and a figure suggesting there was nearly no risk of the fund losing money over a whole year.

(Holy grail, Photo by Charon)

Simons’s team appeared to have discovered something of a holy grail in investing: enormous returns from a diversified portfolio generating relatively little volatility and correlation to the overall market. In the past, a few others had developed investment vehicles with similar characteristics. They usually had puny portfolios, however. No one had achieved what Simons and his team had–a portfolio as big as $5 billion delivering this kind of astonishing performance.

Zuckerman later says:

Brown and Mercer’s staffers often spent the night programming their computers, competing to see who could stay in the office the longest, then rushing back in the morning to see how effective their changes had been. If Brown was going to push himself all day and sleep by his computer keyboard at night, his underlings felt the need to keep up…

[…]

By 2003, the profits of Brown and Mercer’s stock-trading group were twice those of Laufer’s futures team, a remarkable shift in just a few years. Rewarding his ascending stars, Simons announced that Brown and Mercer would become executive vice presidents of the entire firm, co-managing all of Renaissance’s trading, research, and technical activities.

Zuckerman:

In 2002, Simons increased Medallion’s investor fees to 36 percent of each year’s profits, raising hackles among some clients. A bit later, the firm boosted the fees to 44 percent. Then, in early 2003, Simons began kicking all his investors out of the fund. Simons had worried that performance would ebb if Medallion grew too big, and he preferred that he and his employees kept all the gains. But some investors had stuck with Medallion through difficult periods and were crushed.

Nick Simons, Jim Simons’s third-eldest son shared his father’s passions for hiking and adventure. He was taking a trip around the world before he was going to learn organic chemistry and apply for medical school. A week before he was scheduled to come home, he went freediving near Amed, a coastal strip of fishing villages in eastern Bali. While freediving, one person would be down, and the other up, in order to keep track of one another. Nick and a friend thus took turns. At one point, Nick’s friend had to go ashore to unfog his mask. When he got back out, he found Nick’s body near the bottom. They weren’t able to resuscitate him. Nick had drowned.

When Jim and Marilyn found out, they were inconsolable. Zuckerman:

In September, Jim, Marilyn, and other family members traveled to Nepal for the first time, joining some of Nick’s friends in searching for a way to continue Nick’s legacy. Nick had been drawn to Kathmandu and had an interest in medicine, so they funded a maternity ward at a hospital in the city. Later, Jim and Marilyn would start the Nick Simons Institute, which offers healthcare assistance to those living in Nepal’s rural areas, most of whom don’t have basic emergency services.

 

CHAPTER THIRTEEN

Medallion paid out its profits to investors–mostly its own employees by now–once per year. That helped keep the fund small enough to maintain the same high level of profitability. Simons, Henry Laufer, and others were sure that if the fund got much bigger, its profitability would suffer.

As a result, there were some profitable investments–mostly of a longer-term nature–that the fund couldn’t implement. So Simons thought that they should launch a second fund. This fund couldn’t be as profitable as Medallion; however, it could manage a lot more money than Medallion, giving outside investors a chance to invest with Renaissance.

One reason Simons wanted to launch the new fund was to give his scientists and mathematicians a new challenge. Many of them now had more money than they ever imagined, so keeping them challenged and energized was something Simons worried about.

Zuckerman:

His researchers settled on one that would trade with little human intervention, like Medallion, yet would hold investments a month or even longer. It would incorporate some of Renaissance’s usual tactics, such as finding correlations and patterns in prices, but would add other, more fundamental strategies, including buying inexpensive shares based on price-earnings ratios, balance-sheet data, and other information.

After thorough testing, the scientists determined the new hedge fund could beat the stock market by a few percentage points each year, while generating lower volatility than the overall market. It could produce the kinds of steady returns that hold special appeal for pension funds and other large institutions. Even better, the prospective fund could score those returns even if it managed as much as $100 billion, they calculated, an amount that would make it history’s largest hedge fund.

The new fund was called Renaissance Institutional Equities Fund, or RIEF. The firm emphasized to investors that the new fund wouldn’t resemble Medallion. Yet many investors ignored the warning, thinking it was the same scientists and the same research. Soon RIEF had $14 billion under management. Zuckerman:

Other than making the occasional slipup, Simons was an effective salesman, a world-class mathematician with a rare ability to connect with those who couldn’t do stochastic differential equations. Simons told entertaining stories, had a dry sense of humor, and held interests far afield from science and moneymaking. He also demonstrated unusual loyalty and concern for others, qualities the investors may have sensed.

Zuckerman adds:

By the spring of 2007, it was getting hard to keep investors away. Thirty-five billion dollars had been ploughed into RIEF, making it one of the world’s largest hedge funds… Simons made plans for other new funds, initiating work on the Renaissance Institutional Futures Fund, RIFF, to trade futures contracts on bonds, currencies, and other assets in a long-term style. A new batch of scientists was hired, while staffers from other parts of the company lent a hand, fulfilling Simons’s goal of energizing and unifying staffers.

During the summer of 2007, a few quant funds were in trouble. There was selling that seemed to be impacting all the quant funds, despite the fact that these funds thought their investment strategy was unique. The downturn became known as the “quant quake.”

Medallion was approaching the point where it could face a margin call. During the technology-stock meltdown in 2000, Brown hadn’t known what to do. But this time, he knew: Medallion should maintain, and maybe even increase, its bets. Mercer agreed with Brown. So did Henry Laufer, who said: “Trust the models–let them run.” Simons, however, disagreed. Zuckerman:

Simons shook his head. He didn’t know if his firm could survive much more pain. He was scared. If losses grew, and they couldn’t come up with enough collateral, the banks would sell Medallion’s positions and suffer their own huge losses. If that happened, no one would deal with Simons’s fund again. It would be a likely death blow, even if Renaissance suffered smaller financial losses than its bank lenders.

Medallion needed to sell, not buy, he told his colleagues.

“Our job is to survive,” Simons said. “If we’re wrong, we can always add [positions] later.”

Brown seemed shocked by what he was hearing. He had absolute faith in the algorithms he and his fellow scientists had developed…

Medallion began reducing its positions. Zuckerman again:

Some rank-and-file senior scientists were upset–not so much by the losses, but because Simons had interfered with the trading system and reduced positions…

“You’re dead wrong,” a senior researcher emailed Simons.

“You believe in the system, or you don’t,” another scientist said, with some disgust.

But Simons continued to oversee a reduction in Medallion’s positions. He wondered aloud how far the selloff could go, and he wanted to make sure Medallion survived.

Soon Simons ordered his firm to stop selling. Their positions seemed to be stabilizing. Some Renaissance scientists complained that their gains would have been larger if they had maintained their positions rather than lightening up. But Simons claimed that he would make the same decision again. Remarkably, by the end of 2007, Medallion had gained 86 percent.

***

Zuckerman writes about 2008:

…[The] Medallion fund thrived in the chaos, soaring 82 percent that year, helping Simons make over $2 billion in personal profits. The enormous gains sparked a call from a House of Representatives committee asking Simons to testify as part of its investigation into the causes of the financial collapse. Simons prepped diligently with his public-relations advisor Jonathan Gasthalter. With fellow hedge-fund managers George Soros to his right and John Paulson on his left, Simons told Congress that he would back a push to force hedge funds to share information with regulators and that he supported higher taxes for hedge-fund managers.

Simons was something of an afterthought, however, both at the hearings and in the finance industry itself. All eyes were on Paulson, Soros, and a few other investors who, unlike Simons, had successfully anticipated the financial meltdown. They did it with old-fashioned investment research, a reminder of the enduring potential and appeal of those traditional methods.

Medallion was now managing $10 billion, and had averaged 45 percent returns–net of fees–since 1988. Simons had created the best long-term track record in the world. Now, at age seventy-two, Simons decided to turn the reins over to Brown and Mercer.

 

CHAPTER FOURTEEN

Zuckerman:

Jim Simons liked making money. He enjoyed spending it, too.

Stepping down from Renaissance gave Simons–who, by then, was worth about $11 billion–more time on his 220-foot yacht, Archimedes. Named for the Greek mathematician and inventor, the $100 million vessel featured a formal dining room that sat twenty, a wood-burning fireplace, a spacious Jacuzzi, and a grand piano. Sometimes, Simons flew friends on his Gulfstream G450 to a foreign location, where they’d join Jim and Marilyn on the super-yacht.

Zuckerman continues:

Years earlier, Marilyn had carved out space in her dressing room to launch a family foundation. Over time, she and Jim gave over $300 million to Stony Brook University, among other institutions. As Simons edged away from Renaissance, he became more personally involved in their philanthropy. More than anything, Simons relished tackling big problems. Soon, he was working with Marilyn to target two areas in dire need of solutions: autism research and mathematics education.

In 2004, Simons helped launch Math for America, a nonprofit focused on math education and supporting outstanding teachers with annual stipends of $15,000.

Simons remained Renaissance’s chairman. At first, the transition was difficult. But soon Simons found his philanthropic goals to be challenging and engaging. Zuckerman:

…[Renaissance] now employed about 250 staffers and over sixty PhDs, including experts in artificial intelligence, quantum physicists, computational linguists, statisticians, and number theorists, as well as other scientists and mathematicians.

Astronomers, who are accustomed to scrutinizing large, confusing data sets and discovering evidence of subtle phenomena, proved especially capable of identifying overlooked market patterns…

Medallion still did bond, commodity, and currency trades, and it made money from trending and reversion-predicting signals… More than ever, though, it was powered by complex equity trades featuring a mix of complex signals, rather than simple pair trades…

Zuckerman adds:

The gains on each trade were never huge, and the fund only got it right a bit more than half the time, but that was more than enough.

“We’re right 50.75 percent of the time… but we’re 100 percent right 50.75 percent of the time,” Mercer told a friend. “You can make billions that way.”

Zuckerman continues:

Driving these reliable gains was a key insight: Stocks and other investments are influenced by more factors and forces than even the most sophisticated investors appreciated…

[…]

…By analyzing and estimating hundreds of financial metrics, social media feeds, barometers of online traffic, and pretty much anything that can be quantified and tested, they uncovered new factors, some borderline impossible for most to appreciate.

“The inefficiencies are so complex they are, in a sense, hidden in the markets in code,” a staffer says. “RenTec decrypts them. We find them across time, across risk factors, across sectors and industries.”

Even more important: Renaissance concluded that there are reliable mathematical relationships between all these forces. Applying data science, the researchers achieved a better sense of when various factors were relevant, how they interrelated, and the frequency with which they influenced shares. They also tested and teased out subtle, nuanced mathematical relationships between various shares–what staffers call multidimensional anomalies–that other investors were oblivious to or didn’t fully understand.

Zuckerman quotes a Renaissance employee:

“There is no individual bet we make that we can explain by saying we think one stock is going to go up or another down,” a senior staffer says. “Every bet is a function of all the other bets, our risk profile, and what we expect to do in the near and distant future. It’s a big, complex optimization based on the premise that we predict the future well enough to make money from our predictions, and that we understand risk, cost, impact, and market structure well enough to leverage the hell out of it.”

Renaissance also excelled at disguising its buying and selling so that others wouldn’t know what it was doing. This helped to minimize the costs of trading.

***

Bob Mercer, while being highly intelligent, surprised his colleagues with political views that didn’t seem to be based on much evidence or critical thinking. Of course, part of this could be that Mercer’s colleagues tended to have a liberal bias. In any case, Mercer was a Rational Rifle Association member who believed in guns and gold. Mercer didn’t like taxes and he was a skeptic of climate change.

Furthermore, Mercer emerged as a key donor to right-wing causes. Unusually, while most big contributors wanted something in return from politicians, Mercer didn’t ask for anything in return. Zuckerman:

Mercer’s penchant for privacy limited his activity, however, as did his focus on Renaissance. It was his second-oldest daughter, Rebekah, who started showing up at conservative fund-raising events and other get-togethers, becoming the family’s public face, and the one driving its political strategy.

Zuckerman continues:

…in 2011, the Mercers met conservative firebrand Andrew Breitbart at a conference. Almost immediately, they were intrigued with his far-right news organization, Breitbart News Network, expressing interest in funding its operations. Breitbart introduced the Mercers to his friend, Steve Bannon, a former Goldman Sachs banker, who drew up a term sheet under which the Mercer family purchased nearly 50 percent of Breitbart News for $10 million.

In March 2012, Breitbart collapsed on a Los Angeles sidewalk and died of heart failure at the age of forty-three. Bannon and the Mercers convened an emergency meeting in New York to determine the network’s future, and decided that Bannon would become the site’s executive chairman. Over time, the site became popular with the “alt-right,” a loose conglomeration of groups, some of which embraced tenets of white supremacy and viewed immigration and multiculturalism as threats.

[…]

Bannon helped broker a deal for Mercer to invest in an analytics firm called Cambridge Analytica, the US arm of the British behavioral research company SCL Group. Cambridge Analytica specialized in the kinds of advanced data Mercer was accustomed to parsing at Renaissance, and the type of information that Rebekah said the GOP lacked.

Zuckerman writes:

In February 2014, Mercer and other conservative political donors gathered at New York’s Pierre hotel to strategize about the 2016 presidential election. He told attendees he had seen data indicating that mainstream Republicans, such as Jeb Bush and Marco Rubio, would have difficulty winning. Only a true outsider with a sense of the voters’ frustrations could emerge victorious, Mercer argued.

At a gathering of GOP backers who were going to meet Trump, Rebekah walked straight to Trump. Zuckerman:

“It’s bad,” Trump acknowledged.

“No, it’s not bad–it’s over,” she told Trump. “Unless you make a change.”

Rebekah told Trump to bring in Steve Bannon and Kellyanne Conway. Zuckerman:

Before long, Bannon was running the campaign, and Conway was its manager, becoming a ubiquitous and effective television presence. Bannon helped instill order on the campaign, making sure Trump focused on two things–disparaging Clinton’s character and promoting a form of nationalism that Bannon branded “America First” …

***

Meanwhile, Jim Simons was supporting Democrats. Zuckerman says:

Ever since he and his childhood friend, Jim Harpel, had driven across the country and witnessed some of the hardships experienced by minorities and others, Simons had leaned left politically…. By the middle of 2016, Simons had emerged as the most important supporter of the Democratic Party’s Priorities USA Action super PAC and a key backer of Democratic House and Senate candidates. By the end of that year, Simons would donate more than $27 million to Democratic causes. Marilyn Simons was even more liberal than her husband, and Jim’s son, Nathaniel, had established a nonprofit foundation focused on climate change mitigation and clean-energy policy, issues the Trump campaign generally mocked or ignored.

Associates and others at Renaissance began asking Jim Simons if he could do anything about Mercer’s political activities. But Mercer had always been pleasant and respectful to Simons. Zuckerman:

“He’s a nice guy,” he insisted to a friend. “He’s allowed to use his money as he wishes. What can I do?”

 

CHAPTER FIFTEEN

Zuckerman writes:

Rebekah Mercer was emerging as a public figure in her own right… GQ named Mercer the seventeenth most powerful person in Washington, DC, calling her “the First Lady of the alt-right.” The family’s political clout, along with its ongoing support for the president-elect, seemed assured.

David Magerman was unhappy with Bob Mercer’s support of Trump. Magerman thought that much of what Trump was doing was harming the country. Eventually, he did an interview with Gregory Zuckerman of The Wall Street Journal. Magerman didn’t hold back. Soon after that, he got a call from Renaissance telling him he was suspended without pay.

The Mercers were facing other consequences of their political activities. Zuckerman:

At one point, the New York State Democratic Committee ran a television advertisement flashing Bob and Rebekah Mercer’s faces on the screen, saying they were the “same people who bankrolled Trump’s social media bot army and Steve Bannon’s extremist Breitbart News.”

In March 2017, about sixty demonstrators gathered outside Mercer’s home, decrying his funding of far-right causes and calling for higher taxes on the wealthy. A week later, a second group held a protest, some holding signs reading: “Mercer Pay Your Taxes.”

[…]

The Mercers received death threats, friends said, forcing the family to hire security. For a family that relished its privacy, their growing infamy was both shocking and disturbing.

Magerman felt some uncertainty about whether his public criticism of his boss was the right move. Zuckerman:

Magerman had mixed feelings of his own. He had made so much money at the firm that he didn’t have to worry about the financial pain of getting fired. He loathed what Mercer was doing to the country and wanted to stop his political activity. But Magerman also remembered how kind Mercer and his wife had been to him when he first joined the firm, inviting him to dinners at Friendly’s and movie nights with their family. Magerman respected Bob for his intelligence and creativity, and a big part of him still yearned to please the powerful men in his life. At that point, Magerman had spent two decades at Renaissance and he felt an appreciation for the firm. He decided that if he could go on speaking about Mercer’s politics, he’d return to his old job.

Magerman decided to attend a poker tournament at New York’s St. Regis hotel benefitting Math for America.

He wanted to reintroduce himself to key Renaissance people. Zuckerman:

The event was a highly anticipated annual showdown for quants, professional poker players, and others. Magerman knew Simons, Mercer, Brown, and other Renaissance executives would be there.

Rebekah Mercer was there, too. Magerman tried to approach her, and she started yelling at him. Zuckerman:

“How could you do this to my father? He was so good to you,” she said.

[…]

“You’re pond scum,” Mercer told him, repeatedly. “You’ve been pond scum for twenty-five years. I’ve always known it.”

Get out of here, she told Magerman.

Soon security forced Magerman to leave. Shortly thereafter, Renaissance fired Magerman.

Meanwhile, criticism of Renaissance grew, focused on Mercer. Zuckerman:

…nearly fifty protesters picketed the hedge fund itself, saying Mercer was their target, adding to the discomfort of executives, who weren’t accustomed to such negative publicity.

By October 2017, Simons was worried the controversy was jeopardizing Renaissance’s future. The firm’s morale was deteriorating.

Finally, Simons made the difficult decision to ask Mercer to step down from his co-CEO role.

Overall, while the Mercers pulled back on many activities, they seemed to be happy with Trump. Zuckerman:

The Mercers told friends they were happy the Trump administration had cut taxes and chosen conservative judges, among other moves, suggesting they didn’t regret becoming so involved in national politics.

 

CHAPTER SIXTEEN

In late December 2018, the stock market was collapsing. The S&P 500 Index fell almost 10 percent during that month, the worst December since 1931. Simons was worried. He called Ashvin Chhabra, who runs Euclidean Capital, a firm that manages Simons’s money. Simons asked Chhabra whether they should sell short. Chhabra suggested they wait until the market calmed down before considering whether to do anything. Simons agreed.

Zuckerman comments:

Hanging up, neither Simons nor Chhabra focused on the rich irony of their exchange. Simons had spent more than three decades pioneering and perfecting a new way to invest. He had inspired a revolution in the financial world, legitimizing a quantitative approach to trading. By then, it seemed everyone in the finance business was trying to invest the Renaissance way: digesting data, building mathematical models to anticipate the direction of various investments, and employing automated trading systems. The establishment had thrown in the towel. Today, even banking giant JP Morgan Chase puts hundreds of its new investment bankers and investment professionals through mandatory coding lessons. Simons’s success had validated the field of quantitative investing.

[…]

The goal of quants like Simons was to avoid relying on emotions and gut instinct. Yet, that’s exactly what Simons was doing after a few difficult weeks in the market. It was a bit like Oakland A’s executive Billy Beane scrapping his statistics to draft a player with the clear look of a star.

Zuckerman continues:

Simons’s phone call is a stark reminder of how difficult it can be to turn decision-making over to computers, algorithms, and models–even, at times, for the inventors of these very approaches. His conversation with Chhabra helps explain the faith investors have long placed in stock-and-bond pickers dependent on judgment, experience, and old-fashioned research.

By 2019, however, confidence in the traditional approach had waned. Years of poor performance had investors fleeing actively managed stock-mutual funds, or those professing an ability to beat the market’s returns. At that point, these funds, most of which embrace traditional approaches to investing, controlled just half of the money entrusted by clients in stock-mutual funds, down from 75 percent a decade earlier. The other half of the money was in index funds and other so-called passive vehicles, which simply aim to match the market’s returns, acknowledging how challenging it is to top the market.

Zuckerman later notes:

There are reasons to think the advantages that firms like Renaissance enjoy will only expand amid an explosion of new kinds of data that their computer-trading models can digest and parse. IBM has estimated that 90 percent of the world’s data sets have been created in the last two years alone, and that forty zettabytes–or forty-four trillion gigabytes–of data will be created by 2020, a three-hundred-fold increase from 2005.

Today, almost every kind of information is digitized and made available as part of huge data sets, the kinds that investors once only dreamed of tapping. The rage among investors is for alternative data, which includes just about everything imaginable, including instant information from sensors and satellite images around the world. Creative investors test for money-making correlations and patterns by scrutinizing the tones of executives on conference calls, traffic in the parking lots of retail stores, records of auto-insurance applications, and recommendations by social media influencers.

Rather than wait for figures on agricultural production, quants examine sales of farm equipment or satellite images of crop yields. Bills of lading for cargo containers can give a sense of global shifts. Systematic traders can even get cell phone-generated data on which aisles, and even which shelfs, consumers are pausing to browse within stores. If you seek a sense of the popularity of a new product, Amazon reviews can be scraped. Algorithms are being developed to analyze the backgrounds of commissioners and others at the Food and Drug Administration to predict the likelihood of a new drug’s approval.

Zuckerman adds:

Years after Simons’s team at Renaissance adopted machine-learning techniques, other quants have begun to embrace these methods. Renaissance anticipated a transformation in decision-making that’s sweeping almost every business and walk of life. More companies and individuals are accepting and embracing models that continuously learn from their successes and failures. As investor Matthew Granade has noted, Amazon, Tencent, Netflix, and others that rely on dynamic, ever-changing models are emerging dominant. The more data that’s fed to the machines, the smarter they’re supposed to become.

All that said, quantitative models don’t have as much of an advantage–if any–when making longer-term investment decisions. If you’re going to hold a stock for a year, you only have 118 data points back to the year 1900. As a result, some fundamental investors who hold stocks for 1 to 5 years (or longer) will likely continue to prosper, at least until artificial intelligence for long-term stock picking gets a lot better than it is now. Most quant funds are focused on investments with short or very short holding periods.

***

As quantitative trading models become ever more widely adopted, the nature of the financial markets itself could change, introducing new risks. Zuckerman:

Author and former risk manager Richard Bookstaber has argued that risks today are significant because the embrace of quant models is “system-wide across the investment world,” suggesting that future troubles for these investors would have more impact than in the past. As more embrace quantitative trading, the very nature of financial markets could change. New types of errors could be introduced, some of which have yet to be experienced, making them harder to anticipate. Until now, markets have been driven by human behavior, reflecting the dominant roles played by traders and investors. If machine learning and other computer models become the most influential factors in the markets, they may become less predictable and maybe even less stable, since human nature is roughly constant while the nature of this kind of computerized trading can change rapidly.

On the other hand, computerized trading could very well make markets more stable, argues Zuckerman:

The dangers of computerized trading are generally overstated, however. There are so many varieties of quant investing that it is impossible to generalize about the subject. Some quants employ momentum strategies, so they intensify the selling by other investors in a downturn. But other approaches–including smart beta, factor investing, and style investing–are the largest and fastest-growing investment categories in the quant world. Some of these practitioners have programmed their computers to buy when stocks get cheap, helping to stabilize the market.

It’s important to remember that market participants have always tended to pull back and do less trading during crises, suggesting that any reluctance by quants to trade isn’t so very different from past approaches. If anything, markets have become more placid as quant investors have assumed dominant positions. Humans are prone to fear, greed, and outright panic, all of which tend to sow volatility in financial markets. Machines could make markets more stable, if they elbow out individuals governed by biases and emotions. And computer-driven decision-making in other fields, such as the airline industry, has generally led to fewer mistakes.

***

Zuckerman notes Renaissance’s three-decade track record:

By the summer of 2019, Renaissance’s Medallion fund had racked up average annual gains, before investors fees, of about 66 percent since 1988, and a return after fees of approximately 39 percent. Despite RIEF’s early stumbles, the firm’s three hedge funds open for outside investors have also outperformed rivals and market indexes. In June 2019, Renaissance managed a combined $65 billion, making it one of the largest hedge-fund firms in the world, and sometimes represented as much as 5 percent of daily stock-trading volume, not including high-frequency traders.

Renaissance relies on the scientific method rather than human intuition and judgment:

…staffers embrace the scientific method to combat cognitive and emotional biases, suggesting there’s value to this philosophical approach when tackling challenging problems of all kinds. They propose hypotheses and then test, measure, and adjust their theories, trying to let data, not intuition and instinct, guide them.

“The approach is scientific,” Simons says. “We use very rigorous statistical approaches to determine what we think is underlying.”

Another lesson of the Renaissance experience is that there are more factors and variables influencing financial markets and individual investments than most realize or can deduce. Investors tend to focus on the most basic forces, but there are dozens of factors, perhaps whole dimensions of them, that are missed. Renaissance is aware of more of the forces that matter, along with the overlooked mathematical relationships that affect stock prices and other investments, than most anyone else.

However, Zuckerman observes:

For all the unique data, computer firepower, special talent, and trading and risk management expertise Renaissance has gathered, the firm only profits on barely more than 50 percent of its trades, a sign of how challenging it is to try to beat the market–and how foolish it is for most investors to try.

Simons and his colleagues generally avoid predicting pure stock moves. It’s not clear any expert or system can reliably predict individual stocks, at least over the long term, or even the direction of financial markets. What Renaissance does is try to anticipate stock moves relative to other stocks, to an index, to a factor model, and to an industry.

I disagree that no one can predict individual stocks. Especially when their assets under management weren’t too large, Warren Buffett and his partner Charlie Munger clearly were more often right than wrong in predicting how individual businesses–and their associated stocks–were going to perform over time. However, now that Berkshire Hathaway has hundreds of billions of dollars, including over one hundred billion in cash, Buffett and Munger are forced to look only at larger businesses, the stocks of which tend to be more efficiently priced.

In brief, if an investor focuses on microcap stocks (market caps of $300 million or less)–which are ignored by the vast majority of professional investors–it’s quite possible for someone who is patient, diligent, and reasonably intelligent to successfully pick stocks so as to generate performance in excess of the S&P 500 Index and the Russell Microcap Index. Or an investor could apply a quantitative value approach to microcap stocks and also do better than the S&P 500 and the Russell Microcap Index. That’s what the Boole Microcap Fund does.

Zuckerman concludes the chapter:

The Simons Foundation, with an annual budget of $450 million, had emerged as the nation’s second-largest private funder of research in basic science. Math for America, the organization Simons helped found, provided annual stipends of $15,000 to over one thousand top math and science teachers in New York City. It also hosted hundreds of annual seminars and workshops, creating a community of skilled and enthusiastic teachers. There were signs the initiative was helping public schools retain the kinds of teachers who previously had bolted for private industry.

 

EPILOGUE

Zuckerman notes that by Spring 2019, Simons was focused on his two greatest challenges–curing autism, and discovering the origins of the universe and life itself:

True breakthroughs in autism research hadn’t been achieved and time was ticking by. Six years earlier, the Simons Foundation had hired Louis Reichardt, a professor of physiology and neuroscience who was the first American to climb both Mount Everest and K2. Simons handed Reichardt an even more daunting challenge: improve the lives of those with autism.

The foundation helped establish a repository of genetic samples from 2,800 families with at least one child on the autism spectrum, accelerating the development of animal models, a step towards potential human treatments. By the spring of 2019, Simons’s researchers had succeeded in gaining a deeper understanding of how the autistic brain works and were closing in on drugs with the potential to help those battling the condition.

Zuckerman continues:

Simons was just as hopeful about making headway on a set of existential challenges that have confounded humankind from its earliest moments. In 2014, Simons recruited Princeton University astrophysicist David Spergel, who is known for groundbreaking work measuring the age and composition of the universe. Simons tasked Spergel with answering the eternal question of how the universe began. Oh, a please try to do it in a few years, while I’m still around, Simons said.

Simons helped fund a $75 million effort to build an enormous observatory with an array of ultrapowerful telescopes in Chile’s Atacama Desert, a plateau 17,000 feet above sea level featuring especially clear, dry skies. It’s an ideal spot to measure cosmic microwave radiation and get a good look into creation’s earliest moments. The project, led by a group of eight scientists including Spergel and Brain Keating–an astrophysicist who directs the Simons Observatory and happens to be the son of Simons’s early partner, James Ax–is expected to be completed by 2022…

Many scientists assume the universe instantaneously expanded after creation, something they call cosmic inflation. That event likely produced gravitational waves and twisted light, or what Keating calls “the fingerprint of the Big Bang.”…

…Subscribing to a view that time never had a starting point, Simons simultaneously supports work by Paul Steinhardt, the leading proponent of the noninflationary, bouncing model, an opposing theory to the Big Bang.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time. See the historical chart here: https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps. Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals. We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio. The size of each position is determined by its rank. Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost). Positions are held for 3 to 5 years unless a stock approachesintrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods. We also aim to outpace the Russell Microcap Index by at least 2% per year (net). The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: [email protected]

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

September 11, 2022

Jordan Ellenberg has written a wonderful book,How Not to Be Wrong: The Power of Mathematical Thinking (Penguin, 2014).

Steven Pinker comments:

Like Lewis Carroll, George Gamow, and Martin Gardner before him, Jordan Ellenberg shows how mathematics can delight and stimulate the mind. But he also shows that mathematical thinking should be in the toolkit of every thoughtful person–of everyone who wants to avoid fallacies, superstitions, and other ways of being wrong.

Here’s the outline:

• • When Am I Going to Use This?

PART I. LINEARITY

• • One. Less Like Sweden
  • Two. Straight Locally, Curved Globally
  • Three. Everyone is Obese
  • Four. How Much Is That in Dead Americans?
  • Five. More Pie Than Plate

PART II. INFERENCE

• • Six. The Baltimore Stockbroker and the Bible Code
  • Seven. Dead Fish Don’t Read Minds
  • Eight. Reductio Ad Unlikely
  • Nine. The International Journal of Haruspicy
  • Ten. Are You There God? It’s Me, Bayesian Inference

PART III. EXPECTATION

• • Eleven. What to Expect When You’re Expecting to Win the Lottery
  • Twelve. Miss More Planes!
  • Thirteen. Where the Train Tracks Meet

PART IV. REGRESSION

• • Fourteen. The Triumph of Mediocrity
  • Fifteen. Galton’s Ellipse
  • Sixteen. Does Lung Cancer Make You Smoke Cigarettes?

PART V. EXISTENCE

• • Seventeen. There Is No Such Thing as Public Opinion
  • Eighteen. “Out of Nothing I Have Created a Strange New Universe”

• • How to Be Right

 

WHEN AM I GOING TO USE THIS?

Ellenburg tells the story of Abraham Wald.

This story, like many World War II stories, starts with the Nazis hounding a Jew out of Europe and ends with the Nazis regretting it…. He was the grandson of a rabbi and the son of a kosher baker, but the younger Wald was a mathematician almost from the start. His talent for the subject was quickly recognized, and he was admitted to study mathematics at the University of Vienna, where he was drawn to subjects abstract and recondite even by the standards of pure mathematics: set theory and metric spaces.

In the mid-1930s, Austria was in economic distress and it wasn’t possible for a foreigner to be hired as a professor. Wald ended up taking a job at the Cowles Commission, an economic institute then in Colorado Springs. A few months later, Wald was offered a professorship of statistics at Columbia University. When World War II came, the Statistical Research Group was formed.

The Statistical Research Group (SRG), where Wald spent much of World War II, was a classified program that yoked the assembled might of American statisticians to the war effort–something like the Manhattan Project, except the weapons being developed were equations, not explosives.

The mathematical talent at SRG was extraordinary.

Frederick Mosteller, who would later found Harvard’s statistics department, was there. So was Leonard Jimmy Savage, the pioneer of decision theory and great advocate of the field that came to be called Bayesian statistics. Norbert Wiener, the MIT mathematician and the creator of cybernetics, dropped by from time to time. This was a group where Milton Friedman, the future Nobelist in economics, was often the fourth-smartest person in the room.

The smartest person in the room was usually Abraham Wald.

Planes need armor in order to lessen their chance of being shot down. You don’t want too much armor due to the added weight, but you also don’t want too little armor, causing more planes to be shot down. So there was a question about where to put extra armor on the planes.

The planes that came back had bullet holes per square foot distributed as follows: 1.11 for the engine, 1.73 for the fuselage, 1.55 for the fuel system, and 1.8 for the rest of the plane.

Given this knowledge, where should you put the extra armor? At first glance, you might think the added armor should not be on the engine or fuel system, but on the fuselage and the rest of the plane. However, Wald said the extra armor should go not where the bullet holes are, but where they aren’t: on the engines. Why? Because you have to consider the planes that never made it back. Those planes had bullet holes on the engines, which is why they never made it back. Ellenberg comments:

If you go to the recover room at the hospital, you’ll see a lot more people with bullet holes in their legs than people with bullet holes in their chests.

That’s because the people shot in the chest generally don’t recover. Ellenberg again:

One thing the American defense establishment has traditionally understood very well is that countries don’t win wars just by being braver… The winners are usually the guys who get 5% fewer of their planes shot down, or use 5% less fuel, or get 5% more nutrition into their infantry at 95% of the cost. That’s not the stuff war movies are made of, but it’s the stuff wars are made of. And there’s math every step of the way.

The reason Wald figured out where to put the armor was due to his mathematical training.

A mathematician is always asking, “What assumptions are you making? Are they justified?”

The officers were assuming that the planes that returned were a random sample of all planes. But this wasn’t the case. There was no reason to assume the planes had an equal chance of survival no matter where they had been hit.

Ellenberg asserts:

Mathematics is the extension of common sense by other means.

Ellenberg again:

Mathematics is the study of things that come out a certain way because there is no other way they could possibly be.

For example:

…we have built-in mental systems for assessing the likelihood of an uncertain outcome. But those systems are pretty weak and unreliable, especially when it comes to events of extreme rarity. That’s when we shore up our intuition with a few sturdy, well-placed theorems and techniques, and make out of it a mathematical theory of probability.

(Gaussian normal distribution, Illustration by Peter Hermes Furian)

The specialized language in which mathematicians converse with one another is a magnificent tool for conveying complex ideas precisely and swiftly. But its foreignness can create among outsiders the impression of a sphere of thought totally alien to ordinary thinking. That’s exactly wrong.

Math is like an atomic-powered prosthesis that you attach to your common sense, vastly multiplying its reach and strength. Despite the power of mathematics, and despite its sometimes forbidding notation and abstraction, the actual mental work involved is little different from the way we think about more down-to-earth problems.

Ellenberg continues:

Mathematics is not settled. Even concerning the basic objects of study, like numbers and geometric figures, our ignorance is much greater than our knowledge. And the things we do know were arrived at only after massive effort, contention, and confusion. All this sweat and tumult is carefully screened off in your textbook.

Ellenberg then explains that mathematical facts can be simple or complicated, and they can be shallow or profound. The focus of the book is on the simple yet profound. These tools can help you not be wrong.

Ellenberg mentions his own experience:

As a graduate student, I dedicated myself to number theory, what Gauss called “the queen of mathematics,” the purest of the pure subjects, the sealed garden at the center of the convent, where we contemplated the same questions about numbers and equations that troubled the Greeks and have gotten hardly less vexing in the twenty-five hundred years since.

Ellenberg then adds:

But something funny happened. The more abstract and distant from lived experience my research got, the more I started to notice how much math was going on in the world outside the walls. Not Galois representations or cohomology, but ideas that were simpler, older, and just as deep… I started writing articles for magazines and newspapers about the way the world looked through a mathematical lens, and I found, to my surprise, that even people who said they hated math were willing to read them.

 

PART I: LINEARITY

ONE: LESS LIKE SWEDEN

Daniel J. Mitchell of the libertarian Cato Institute wrote a blog post in 2012, during the battle over the Affordable Care Act. The blog post was titled, “Why Is Obama Trying to Make America More Like Sweden when Swedes Are Trying to Be Less Like Sweden?” Mitchell writes: “If Swedes have learned from their mistakes and are now trying to reduce the size and scope of government, why are American politicians determined to repeat those mistakes?”

The answer has to do with a nonlinear relationship between prosperity and Swedishness, writes Ellenberg. It’s false to say that reducing the size of government automatically increases prosperity, just as it’s false to say that increasing the size of government automatically increases prosperity. Arguably, there’s an optimal size for government that America is below and that Sweden is above.

 

TWO: STRAIGHT LOCALLY, CURVED GLOBALLY

Ellenberg continues the theme of nonlinearity:

You might not have thought you needed a professional mathematician to tell you that not all curves are straight lines. But linear reasoning is everywhere. You’re doing it every time you say that if something is good to have, having more of it is even better.

Ellenberg points out that if you look at one section of a curve very closely, it looks like a line. Ellenberg explains calculus by using the example of a missile that follows a curved path due to gravity:

Now here’s the conceptual leap. Newton said, look, let’s go all the way. Reduce your field of view until it’s infinitesimal–so small that it’s smaller than any size you can name, but not zero. You’re studying the missile’s arc, not over a very short time interval, but at a single moment. What was almost a line becomes exactly a line. And the slope of this line is what Newton called the fluxion, and what we’d now call the derivative.

(Projectile motion illustration by Ayush12gupta, via Wikimedia Commons)

Ellenberg introduces Zeno’s paradox. In order to walk from one place to another place, first you must walk half the distance. Then you must walk half of the remaining distance. After that, in order to get to your destination, you must walk half of the remaining distance. Thus it seems you can never reach your destination. To go anywhere, first you must go half way there, then you must cover half the remaining distance, ad infinitum. Ellenberg observes that all motion is ruled out. To wave your hand, first you must raise your hand. To do that, first you must raise it halfway, etc.

What’s the solution? Ellenberg says to consider the infinite series:

1/2 + 1/4 + 1/8 + 1/16 + 1/32 + ….

If you add up the first twenty terms, you get 0.999999. If you keep adding terms, you get 0.9999….., which doesn’t end. But is 0.9999… equal to 1?

Ellenberg says to consider that

0.3333…. = 1/3

Multiply both sides by 3:

0.9999…. = 1

You could also do it this way, multiplying 0.9999…. by 10:

10 x (0.9999…) = 9.9999….

Subtract the decimal from both sides:

10 x (0.9999…) – 1 x (0.9999…) = 9.9999… – 0.9999…

9 x (0.9999…) = 9

This still doesn’t fully answer the question because we assume that 0.3333… = 1/3, which one could argue is not quite true.

Ellenberg presents another brain teaser:

1 + 2 + 4 + 8 + 16 + 32 + ….

What does this series equal? It must be infinite, right?

What if you multiplied it by 2:

2 x (1 + 2 + 4 + 8 + …) = 2 + 4 + 8 + 16 + …

It looks like the same series but without the 1 at the beginning. That implies:

2 x (1 + 2 + 4 + 8 + …) – 1 x (1 + 2 + 4 + 8 + …) = –1

Which means:

1 + 2 + 4 + 8 + … = –1

Really? Here’s another interesting example:

1 – 1 + 1 – 1 + 1 – 1 + …

It looks like:

(1 – 1) + (1 – 1) + (1 – 1) + … = 0 + 0 + 0 + … = 0

But it also can be written:

1 – (1 – 1) – (1 – 1) – (1 – 1) = 1 – 0 – 0 – 0 = 1

If you define T as:

T = 1 – 1 + 1 – 1 + 1 – 1 + …

Then take the negative of both sides:

–T = – 1 + 1 – 1 + 1 … = T – 1

Then you get –2T = –1, or T = 1/2.

Now back to the infinite series:

0.9999….

Augustin-Louis Cauchy introduced the idea of limit into calculus in the 1820s. Ellenberg:

The sum 0.9 + 0.09 + 0.009 + … gets closer and closer to 1 the more terms you add. And it never gets any farther away. No matter how tight a cordon we draw around the number 1, the sum will eventually, after some finite number of steps, penetrate it, and never leave. Under those circumstances, Cauchy said, we should simply define the value of the infinite sum to be 1. And then he worked very hard to prove that committing oneself to this definition didn’t cause horrible contradictions to pop up elsewhere. By the time this labor was done, he’s constructed a framework that made Newton’s calculus completely rigorous. When we say a curve looks locally like a straight line at a certain angle, we now mean more or less this: as you zoom in tighter and tighter, the curve resembles the given line more and more closely.

So we can take 1 and 0.999… to be equal. Ellenberg concludes:

One of the great joys of mathematics is the incontrovertible feeling that you’ve understood something the right way, all the way down to the bottom; it’s a feeling I haven’t experienced in any other sphere of mental life.

 

THREE: EVERYONE IS OBESE

Ellenberg read an article about Americans getting more obese. The article concludes that if this trend continues, ALL Americans will be obese by 2048.

As another example of linear regression, Ellenberg asks the reader to consider the relationship between SAT scores and tuition. Generally, the higher the average SAT score, the higher the tuition. What is linear regression?

…if you replace the actual tuition with the estimate the line suggests, and then you compute the difference between the estimated and the actual tuition for each school, and then you square each of these numbers, and you add all those squares up, you get some kind of total measure of the extent to which the line misses the points, and you choose the line that makes this measure as small as possible. This business of summing squares smells like Pythagoras, and indeed the underlying geometry of linear regression is no more than Pythagoras’s theorem transposed and upgraded to a much-higher-dimensional setting…

Here’s an example of linear regression:

(Least squares fit by Prof. Boykin, via Wikimedia Commons)

Ellenberg continues:

Linear regression is a marvelous tool, versatile, scalable, and as easy to execute as clicking a button on your spreadsheet. You can use it for data involving two variables… but it works just a well for three variables, or a thousand. Whenever you want to understand which variables drive which other variables, and in which direction, it’s the first thing you reach for. And it works on any data set at all.

However, linear regression is a tool that can be misused if it is applied to phenomena that aren’t linear.

Back to obesity: Overweight means having a body-mass index of 25 or higher.

In the early 1970s, just under half of Americans had a BMI that high. By the early 1990s that figure had risen to almost 60%, and by 2008 almost three-quarters of the U.S. population was overweight.

If you extrapolate the trend, you conclude that ALL Americans will be overweight by 2048. That doesn’t make sense.

 

FOUR: HOW MUCH IS THAT IN DEAD AMERICANS?

Ellenberg writes:

How bad is the conflict in the Middle East? Counterterrorism specialist Daniel Byman of Georgetown University lays down some cold, hard numbers in Foreign Affairs: “The Israeli military reports that from the start of the second intifada [in 2000] through the end of October 2005, Palestinians killed 1,074 Israelis and wounded 7,520–astounding figures for such a small country, the proportional equivalent of more than 50,000 dead and 300,000 wounded for the United States.”

Ellenberg adds:

Eventually (or perhaps immediately?) this reasoning starts to break down. When there are two men left in the bar at closing time, and one of them coldclocks the other, it is not the equivalent in context to 150 million Americans getting simultaneously punched in the face.

Or: when 11% of the population of Rwanda was wiped out in 1994, all agree that it was among the worst crimes of the century. But we don’t describe the bloodshed there by saying, “In the context of 1940s Europe, it was nine times as bad as the Holocaust.”

As a proportion of the population, South Dakota has a high degree of brain cancer while North Dakota has a low degree of it. Similarly, Maine has a high degree of brain cancer while Vermont has a low degree of it. This seems strange.

To understand what’s doing on, Ellenberg considers coin flipping. If you flip one coin ten times, you may get 8 or 9 heads, which is a proportion of 80% or 90%. But if you flip a coin one hundred times, you’ll never get 80% heads unless you repeat the flipping a few billion times.

What causes a large number of coin tosses to move toward 50%? The Law of Large Numbers. Ellenberg:

…if you flip enough coins, there’s only the barest chance of getting as many as 51%. Observing a highly unbalanced result in ten flips is unremarkable; getting the same proportional imbalance in a hundred flips would be so startling as to make you wonder whether someone has mucked with your coins.

In 1756, Abraham de Moivre published The Doctrine of Chances. De Moivre wanted to know how close to 50% heads you would get if you flipped a large number of coins.

De Moivre’s insight is that the size of the typical discrepancy is governed by the square root of the number of coins you toss. Toss a hundred times as many coins as before and the typical discrepancy grows by a factor of 10–at least, in absolute terms. As a proportion of the total number of tosses, the discrepancy shrinks as the number of coins grows, because the square root of the number of coins grows much more slowly than does the number of coins itself.

…if you want to know how impressed to be by a good run of heads, you can ask how many square roots away from 50% it is. The square root of 100 is 10. So when I got 60 heads in 100 tries, that was exactly one square root away from 50-50. The square root of 1,000 is about 31; so when I got 538 heads in 1,000 tries, I did something even more surprising, even though I got only 53.8% heads in the latter case and 60% heads in the former.

But de Moivre wasn’t done. He found that the discrepancies from 50-50, in the long run, always tend to form themselves into a perfect bell curve, or, as we call it in the biz, the normal distribution.

Ellenberg continues:

The bell curve… is tall in the middle and very flat near the edges, which is to say that the farther a discrepancy is from zero, the less likely it is to be encountered. And this can be precisely quantified.

One common misperception is the so-called law of averages (which Ellenberg points out is not well-named because laws should be true but the law of averages is false). If you flip ten heads in a row, what are the odds that the next flip will be heads? Based on the Law of Large Numbers, you might think the next flip is extremely likely to be tails. But it’s not. It’s still only 50% likely to be tails and 50% likely to be heads. Ellenberg explains:

The way the overall proportion settles down to 50% isn’t that fate favors tails to compensate for the heads that have already landed; it’s that those first ten flips become less and less important the more flips we make. If I flip the coin a thousand more times, and get about half heads, then the proportion of heads in the first 1,010 flips is also going to be close to 50%.

So can wars and other violent events be compared based on proportion of population? Ellenberg concludes the chapter:

Most mathematicians would say that, in the end, the disasters and the atrocities of history form what we call a partially ordered set. That’s a fancy way of saying that some pairs of disasters can be meaningfully compared and others cannot. This isn’t because we don’t have accurate enough death counts, or firm enough opinions as to the relative merits of being annihilated by a bomb versus dying of war-induced famine. It’s because the question of whether one war was worse than another is fundamentally unlike the question of whether one number is bigger than another. The latter question always has an answer. The former does not.

 

FIVE: MORE PIE THAN PLATE

Ellenberg writes:

Between 1990 and 2008, the U.S. economy gained a net 27.3 million jobs. Of those, 26.7 million, or 98%, came from the “nontradable sector”: the part of the economy including things like government, health care, retail, and food service, which can’t be outsourced and which don’t produce goods to be shipped overseas.

…So [is] growth as concentrated in the nontradable part of the economy as it could possibly be? That’s what it sounds like–but that’s not quite right. Jobs in the tradable sector grew by a mere 620,000 between 1990 and 2008, that’s true. But it could have been worse–they could have declined! That’s what happened between 2000 and 2008; the tradable sector lost about 3 million jobs, while the nontradable sector added 7 million. So the nontradable sector accounted for 7 million jobs out of the total gain of 4 million, or 175%!

The slogan to live by here is:

Don’t talk about percentages of numbers when the numbers might be negative.

Ellenberg gives the example of a coffee shop. Say he lost $500 on coffee, but made $750 on a pastry case and $750 on a CD Rack. Overall, he made $1,000. 75% of profits came from the pasty case. Or you could say that 75% of profits came from the CD rack. To say either one is misleading.

This problem doesn’t happen with numbers that have to be positive, like revenues.

Consider growth in income from 2009 to 2010, says Ellenberg. 93% of additional income went to the top 1% of taxpayers. 17% of additional income went to those in the top 10%, but not the top 1%, of taxpayers. How does that make sense? Again, because of a negative number: the bottom 90% of taxpayers saw their incomes move lower.

 

PART II: INFERENCE

SIX: THE BALTIMORE STOCKBROKER AND THE BIBLE CODE

Ellenberg observes that mathematics is used for a wide range of things, from when the next bus can be expected to what the universe looked like three trillionths of a second after the Big Bang. But what about questions concerning God and religion? Ellenberg:

Never underestimate the territorial ambitions of mathematics! You want to know about God? There are mathematicians on the case.

The rabbinical scholar focuses on the Torah, notes Ellenberg. A group of researchers at Hebrew University–senior professor of mathematics Eliyahu Rips, graduate student in computer science Yoav Rosenberg, and physicist Doron Witztum–started examining the Torah. Specifically, they looked at “equidistant letter sequence,” or ELS. The question they asked was:

Do the names of the rabbis appear in equidistant letter sequences unusually close to their birth and death dates?

Put differently: Did the Torah know the future?

Ellenberg:

First they searched the book of Genesis for ELSs spelling out the rabbis’ names and dates, and computed how close in the text the sequences yielding the names were to the ones yielding the corresponding dates. Then they shuffled the thirty-two dates, so that each one was now matched with a random rabbi, and they ran the test again. Then they did the same thing a million times. If there were no relation in the Torah’s text between the names of the rabbis and the corresponding dates, you’d expect the true matching between rabbis and dates to do about as well as one of the random shuffles. That’s not what they found. The correct association ended up very near the top of the rankings, notching the 453^rd highest score among the 1 million contenders.

American journalist Michael Drosnin heard about the Witztum paper. Drosnin started looking for ELSs, but without scientific constraint. Drosnin published The Bible Code, which claimed to predict Yitzhak Rabin’s assassination, the Gulf War, and the 1994 collision of Comet Shoemaker-Levy 9 with Jupiter. Rips, Rosenberg, and Witztum denounced Drosnin’s ad hoc method. But The Bible Code became a bestseller.

At the same time as the bible codes were being accepted by the public, the Witztum paper came under severe criticism from mathematicians including Shlomo Sternberg of Harvard University. To understand the criticism, Ellenberg tells the story of the Baltimore stockbroker.

What if you got an unsolicited letter from a Baltimore stockbroker saying that a certain stock would rise, and then it does? Further assume that each week, for ten weeks, you get a new letter predicting some stock to rise or fall, and each week, the prediction is correct. The eleventh week, the Baltimore stockbroker asks you to invest money with him.

Now, what are the odds that the Baltimore stockbroker got ten straight predictions right due to chance alone? Those odds can be computed:

(1/2) x (1/2) x (1/2) x (1/2) x (1/2) x (1/2) x (1/2) x (1/2) x (1/2) x (1/2) = 1/1024

However, what you didn’t know was that the Baltimore stockbroker mailed 10,240 letters the first week, half predicting a rise in a specific stock and half predicting a fall in that same stock. So that first week, there were 5,120 correct predictions. At that point, the stockbroker mails out 5,120 new letters, half of which predict a rise in some stock and half of which predict a fall. After that second week, there are 2,560 people who have seen two correct predictions in a row. And thus the Baltimore stockbroker continues until, after ten weeks, there are 10 people who received by mail 10 straight correction predictions.

Some companies will launch multiple mutual funds at the same time, experimenting with different strategies before advertising any fund to the public. The funds that do well during this initial period–called incubation–are then marketed to the public, while the funds that don’t do well are quietly closed (and often these funds are not noticed by the public). Subsequently, the surviving funds do not perform better than the median, which suggests the part of their early outperformance was simply due to chance.

(Photo by Martinlisner)

The general lesson, writes Ellenberg, is:

Improbable things happen a lot.

In other words, a great many things are due largely to chance, therefore there will always be improbable things happening.

Criticism of the Witztum paper came from Brendan McKay, an Australian computer scientist, and Dror Bar-Natan, an Israeli mathematician then at Hebrew University. They pointed out that the rabbis didn’t have birth certificates or passports, so they were known by many different names. Why did the Witztum paper, for each rabbi studied, use specific appellations but not others? Ellenberg:

McKay and Bar-Natan found that wiggle room in the choices of names led to drastic changes in the quality of the results. They made a different set of choices about the appellations of the rabbis; their choices, according to biblical scholars, make just as much sense as the ones picked by Witztum… And they found that with the new list of names, something quite amazing transpired. The Torah no longer seemed to detect the birth and death dates of the rabbinic notables. But the Hebrew edition of War and Peace nailed it, identifying the rabbis with their correct dates about as well as the book of Genesis did in the Witztum paper.

What’s going on? Ellenberg:

It is very unlikely that any given set of rabbinic appellations is well matched to birth and death dates in the book of Genesis. But with so many ways of choosing the names, it’s not at all improbable that among all the choices there would be one that made the Torah look uncannily prescient.

Obviously it’s also not the case that Tolstoy composed his novel with the names of rabbis concealed in it, to be later revealed when modern Hebrew was invented and the novel was translated into it.

 

SEVEN: DEAD FISH DON’T READ MINDS

At the 2009 Organization for Human Brain Mapping conference in San Francisco, UC Santa Barbara neuroscientist Craig Bennett presented his finding that a dead salmon was able to read human emotions.

A dead fish, scanned in an fMRI device, was shown a series of photographs of human beings, and was found to have a surprisingly strong ability to correctly assess the emotions the people in the pictures displayed.

What?! Here’s what’s going on:

…the nervous system is a big place, with tens of thousands of voxels to choose from. The odds that one of those voxels provides data matching up well with the photos is pretty good… The point of Bennett’s paper is to warn that the standard methods of assessing results, the way we draw our thresholds between a real phenomenon and random static, come under dangerous pressure in this era of massive data sets, effortlessly obtained. We need to think very carefully about whether our standards for evidence are strict enough, if the empathetic salmon makes the cut.

Ellenberg adds:

The really surprising result of Bennett’s paper isn’t that one or two voxels in a dead fish passed a statistical test; it’s that a substantial proportion of the neuroimaging articles he surveyed didn’t use statistical safeguards (known as “multiple comparisons correction”) that take into account the ubiquity of the improbable. Without those corrections, scientists are at serious risk of running the Baltimore stockbroker con, not only on their colleagues but on themselves.

The null hypothesis is the hypothesis that the intervention you’re studying has no effect, notes Ellenberg.

To test your intervention, you have to run a null hypothesis significance test. Ellenberg:

It goes like this. First, you have to run an experiment. You might start with a hundred subjects, then randomly select half to receive your proposed wonder drug while the other half gets a placebo…

From here, the protocol might seem simple: if you observe fewer deaths among the drug patients than the placebo patients, declare victory and file a marketing application with the FDA. But that’s wrong. It’s not enough that the data be consistent with your theory; they have to be inconsistent with the negation of your theory, the dreaded null hypothesis.

Assuming the null hypothesis, the chance of death (assume 10%) is exactly the same for the patients who got the drug and the patients who got the placebo. That doesn’t mean that exactly five patients will die in each category. Under the null hypothesis, Ellenberg says there is:

• • 13.3% chance equally many drug and placebo patients die
  • 43.3% chance fewer placebo patients than drug patients die
  • 43.3% chance fewer drug patients than placebo patients die

So drug patients doing better than placebo patients is not necessarily significant. But what if drug patients do A LOT better, asks Ellenberg. Assume, for example, that none of the drug patients die. What are the odds that the could happen under the null hypothesis?

Under the null hypothesis, here’s a 90% chance that a patient will survive. What are the odds that all fifty survive?

0.9 x 0.9 x 0.9 x … fifty times! … x 0.9 x 0.9 = 0.00515…

Under the null hypothesis, there is only one chance in two hundred of getting results this good, observes Ellenberg.

So here’s the procedure for ruling out the null hypothesis…:

• • Run an experiment.
  • Suppose the null hypothesis is true, and let p be the probability (under that hypothesis) of getting results as extreme as those observed.
  • The number p is called the p-value. If it is very small, rejoice; you get to say your results are statistically significant. If it is large, concede that the null hypothesis has not been ruled out.

 

EIGHT: REDUCTIO AD UNLIKELY

Ellenberg points out that assuming the null hypothesis, which we believe is false, might seem questionable. But the reductio ad absurdum goes all the way back to Aristotle. If a hypothesis implies a falsehood, then the hypothesis must be false. The reduction ad absurdum looks like this:

• • Suppose the hypothesis H is true.
  • It follows from H that a certain fact F cannot be the case.
  • But F is the case.
  • Therefore, H is false.

Ellenberg then describes what he calls a reductio ad unlikely:

• • Suppose the null hypothesis H is true.
  • It follows from H that a certain outcome O is very improbable (say, less than Fisher’s 0.05 threshold).
  • But O was actually observed.
  • Therefore, H is very improbable.

 

NINE: THE INTERNATIONAL JOURNAL OF HARUSPICY

Ellenberg writes about haruspicy:

…If the null hypothesis is always true–that is, if haruspicy is undiluted hocus-pocus–then only 1 in 20 experiments will be publishable.

And yet there are hundreds of haruspices, and thousands of ripped-open sheep, and even one in twenty divinations provides plenty of material to fill each issue of the journal with novel results, demonstrating the efficacy of the methods and the wisdom of the gods. A protocol that worked in one case and gets published usually fails when another haruspex tries it; but experiments without statistically significant results don’t get published, so no one ever finds out about the failure to replicate. And even if word starts getting around, there are always small differences the experts can point to that explain why the follow-up study didn’t succeed; after all, we know the protocol works, because we tested it and it had a statistically significant effect!

Ellenberg then makes his main point:

Modern medicine and social science are not haruspicy. But a steadily louder drum circle of dissident scientists has been pounding out an uncomfortable message in recent years: there’s probably a lot more entrail reading in the sciences than we’d like to admit.

The loudest drummer is John Ioannidis, a Greek high school math star turned biomedical researcher whose 2005 paper “Why Most Published Research Findings Are False” touched off a fierce bout of self-criticism (and a second wave of self-defense) in the clinical sciences… Ioannidis takes seriously the idea that entire specialties of medical research are “null fields,” like haruspicy, in which there are simply no actual effects to be found. “It can be proven,” he writes, “that most claimed research findings are false.”

Ellenberg:

…In a 2012 study, scientists at the California biotech company Amgen set out to replicate some of the most famous experimental results in the biology of cancer, fifty-three studies in all. In their independent trials, they were able to reproduce only six.

How can this have happened? It’s not because genomicists and cancer researchers are dopes. In part, the replicability crisis is simply a reflection of the fact that science is hard and that most ideas we have are wrong–even most of those ideas that survive a first round of prodding.

Ellenberg again:

Suppose you tested twenty genetic markers to see whether they were associated with some disorder of interest, and you found just one result that achieved p < .05 significance. Being a mathematical sophisticate, you’d recognize that one success in twenty is exactly what you’d expect if none of the markers had any effect…

All the more so if you tested the same gene, or the green jelly bean, twenty times and got a statistically significant effect just once.

But what if the green jelly bean were tested twenty times by twenty different research groups in twenty different labs? Nineteen of the labs find no significant statistical effect. They don’t write up their results… The scientists in the twentieth lab, the lucky ones, find a statistically significant effect, because they got lucky–but they don’t know they got lucky.

It can be difficult for scientists when the results seem to be statistically insignificant. Ellenberg:

If you run your analysis and get a p-value of .06, you’re supposed to conclude that your results are statistically insignificant. But it takes a lot of mental strength to stuff years of work in the file drawer… Give yourself license to tweak and shade the statistical tests you carry out on your results, and you can often get that .06 down to a .04. Uri Simonsohn, a professor at Penn who’s a leader in the study of replicability, calls these practices “p-hacking.” Hacking the p isn’t usually as crude as I’ve made it out to be, and it’s seldom malicious. The p-hackers truly believe in their hypotheses, just as the Bible coders do, and when you’re a believer, it’s easy to come up with reasons that the analysis that gives a publishable p-value is the one you should have done in the first place.

But everybody knows it’s not really right.

Replication is central to science. Ellenberg comments:

But even studies that could be replicated often aren’t. Every journal wants to publish a breakthrough finding, but who wants to publish the paper that does the same experiment a year later and gets the same result? Even worse, what happens to papers that carry out the same experiment and don’t find a significant result? For the system to work, those experiments need to be made public. Too often they end up in the file drawer instead.

But the culture is changing. Reformers with loud voices like Ioannidis and Simonsohn, who speak both to the scientific community and to the broader public, have generated a new sense of urgency about the danger of descent into large-scale haruspicy. In 2013, the Association for Psychological Science announced that they would start publishing a new genre of article, called Registered Replication Reports. These reports, aimed at reproducing the effects reported in widely cited studies, are treated differently from usual papers in a crucial way: the proposed experiment is accepted for publication before the study is carried out.

 

TEN: ARE YOU THERE GOD? IT’S ME, BAYESIAN INFERENCE

While we can predict the course of an asteroid better as we get ever more data, there may be hard limits on how far into the future meteorologists can predict the weather. The weather is chaotic, and a sea gull flapping its wings could alter the weather forever. Ellenberg:

Is human behavior more like an asteroid or more like the weather? It surely depends on what aspect of human behavior you’re talking about. In at least one respect, human behavior ought to be even harder to predict than the weather. We have a very good mathematical model for weather, which allows us at least to get better at short-range predictions when given access to more data, even if the inherent chaos of the system inevitably wins out. For human action we have no such model and may never have one. That makes the prediction problem massively harder.

Ellenberg asks whether Facebook can predict that someone is a terrorist.

	on Facebook list	Not on list
terrorist	10	9,990
not terrorist	99,990	199,890,010

What if your neighbor ends up on the Facebook list? Ellenberg:

The null hypothesis is that your neighbor is not a terrorist. Under that hypothesis–that is, presuming his innocence–the chance of him showing up on the Facebook red list is a mere 0.05%, well below the 1-in-20 threshold of statistical significance. In other words, under the rules that govern the majority of contemporary science, you’d be justified in rejecting the null hypothesis and declaring your neighbor a terrorist.

Except there’s a 99.99% chance he’s not a terrorist.

Ellenberg continues:

On the one hand, there’s hardly any chance that an innocent person will be flagged by the algorithm. At the same time, the people the algorithm points to are almost all innocent. It seems like a paradox, but it’s not. It’s just how things are…

Here’s the crux. There are really two questions you can ask. They sound kind of the same, but they’re not.

Question 1: What’s the chance that a person gets put on Facebook’s list, given that they’re not a terrorist?

Question 2: What’s the chance that a person’s not a terrorist, given that they’re on Facebook’s list?

One way you can tell these two questions are different is that they have different answers. Really different answers. We’ve already seen that the answer to the first question is about 1 in 2,000, while the answer to the second is 99.99%. And it’s the answer to the second question that you really want.

Ellenberg:

The p-value is the answer to the question

“The chance that the observed experimental result would occur, given that the null hypothesis is correct.”

But what we want to know is the other conditional probability:

“The chance that the null hypothesis is correct, given that we observed a certain experimental result.”

Ellenberg adds:

The danger arises precisely when we confuse the second quantity for the first. And this confusion is everywhere, not just in scientific studies. When the district attorney leans into the jury box and announces, “There is only a one in five million, I repeat, a ONE IN FIVE MILLLLLLLION CHANCE that an INNOCENT MAN would match the DNA sample found at the scene,” he is answering question 1, How likely would an innocent person be to look guilty? But the jury’s job is to answer question 2, How likely is this guilty-looking defendant to be innocent?

In Bayesian inference, you start out with your best guess–the a priori probability that something is true–and then, once you get further evidence, you end up with a posterior probability. Consider the example of the neighbor being on Facebook’s terrorist list.

The neighbor’s presence on the list really does offer some evidence that he’s a potential terrorist. But your prior for that hypothesis ought to be very small, because most people aren’t terrorists. So, despite the evidence, your posterior probability remains small as well, and you don’t–or at least shouldn’t–worry.

Ellenberg writes:

For those who are willing to adopt the view of probability as degree of belief, Bayes’s theorem can be seen not as a mere mathematical equation but as a form of numerically flavored advice. It gives us a rule, which we may choose to follow or not, for how we should update our beliefs about things in the light of new observations. In this new, more general form, it is naturally the subject of much fiercer disputation. There are hard-core Bayesians who think that all our beliefs should be formed by strict Bayesian computations, or at least as strict as our limited cognition can make them; others think of Bayes’s rule as more of a loose qualitative guideline.

Here is Bayes’s rule:

(Photo by mattbuck, via Wikimedia Commons)

Ellenberg quotes Sherlock Holmes:

“It is an old maxim of mine that when you have excluded the impossible, whatever remains, however improbable, must be the truth.”

Ellenberg says Holmes should have said the following:

“It is an old maxim of mine that when you have excluded the impossible, whatever remains, however improbable, must be the truth, unless the truth is a hypothesis it didn’t occur to you to consider.”

Ellenberg gives the example of GOD vs. NO GOD. There are other possibilities, like GODS (plural), which could help explain creation. Ellenberg:

…Another theory with some adherents is SIMS, where we’re not actually people at all, but simulations running on an ultracomputer built by other people. That sounds bizarre, but plenty of people take the idea seriously (most famously, the Oxford philosopher Nick Bostrom), and on Bayesian grounds, it’s hard to see why you shouldn’t. People like to build simulations of real-world events; surely, if the human race doesn’t extinguish itself, our power to simulate will only increase, and it doesn’t seem crazy to imagine that those simulations might one day include conscious entities that believe themselves to be people.

All that said, Ellenberg notes that it’s probably best to arrive at faith–or to discard it–in a non-quantitative way. And people should stick to, “I do believe in God,” or “I don’t believe in God,” or “I’m not sure.”

 

PART III: EXPECTATION

ELEVEN: WHAT TO EXPECT WHEN YOU’RE EXPECTING TO WIN THE LOTTERY

Ellenberg notes:

During the Revolutionary War, both the Continental Congress and the governments of the states established lotteries to fund the fight against the British. Harvard, back in the days before it enjoyed a nine-figure endowment, ran lotteries in 1794 and 1810 to fund two new college buildings.

Like “statistical significance,” the term “expected value” is misleading. If you bet $10 on a dog that has a 10% chance of winning, then the expected value is:

(10% x $100) + (90% x $0) = $10

The expected value for the $10 bet is $10. But that’s not what you expect. You expect either $0 or $100. Ellenberg states that “average value” might be a better term than “expected value.” If you make one thousand $10 bets–with each bet having a 10% chance of winning $100 and a 90% chance of winning $0–then you would expect to make about $10,000, which equals the total amount that you bet. Over time, if you keep repeating this bet, you expect to come out even.

What is your expected value from playing Powerball? Assuming the jackpot is $100 million, your expected value is:

100 million / 175 million + 1 million / 5 million + 10,000 / 650,000 + 100 / 19,000 + 100 / 12,000 + 7 / 700 + 7 / 360 + 4 / 110 + 4 / 55

All the different terms represent different amounts that you can win playing Powerball with the jackpot at $100 million. (These smaller prizes keep people feeling that the game is worth playing.) If you work out that math, your expected value from playing is just under 94 cents per $2 ticket. For each $2 ticket you buy, on average you expect to get back just under 94 cents. It would seem that the game is not worth playing, but what if the jackpot, call it J, is higher? Your expected value is:

J / 175 million + 1 million / 5 million + 10,000 / 650,000 + 100 / 19,000 + 100 / 12,000 + 7 / 700 + 7 / 360 + 4 / 110 + 4 / 55

This simplifies to:

J / 175 million + 36.7 cents

The breakeven threshold is a bit over J = $285 million. So if the jackpot is greater than $285 million, then you expect to win more than $2 for each $2 bet. It makes sense not only to play, but to buy as many $2 tickets as you can reasonably afford.

But this assumes that all the other players fail to win the jackpot. Assume there are 75 million players total. Assume you win and that the jackpot is $337 million. What are the odds that everyone else loses? It is (174,999,999 / 175,000,000) multiplied by itself 75 million times, which is 0.651… (65.1%). This means there’s about a 35% chance that someone else will win, which means you would have to share the jackpot. Your expected payoff is:

65% x $337 million + 35% x $168 million = $278 million

$278 million is below the $285 million threshold, so the possibility of sharing the jackpot means that the game is no longer worth playing (assuming a $337 million jackpot and 75 million players).

Are lotteries always bad bets? No. Consider the story of Cash WinFall in Massachusetts. Here’s the prize distribution on a normal day:

match all 6 numbers	1 in 9.3 million	variable jackpot
match 5 of 6	1 in 39,000	$4,000
match 4 of 6	1 in 800	$150
match 3 of 6	1 in 47	$5
match 2 of 6	1 in 6.8	free lottery ticket

Assume the jackpot is $1 million. Then the expected return on a $2 ticket is:

($1 million / 9.3 million) + ($4,000 / 39,000) + ($150 / 800) + ($5 / 47) + ($2 / 6.8) = 79.8 cents

Each $2 bet would return about 80 cents, which is not a good bet. However, roll-down days are different. (Roll-downs happen when nobody wins the jackpot, so the prize money is rolled down.) On February 7, 2005, nobody won the $3 million dollar jackpot. So the money was rolled down:

The state’s formula rolled $600,000 to the match-5 and match-3 prize pools and $1.4 million into the match-4s. The probability of getting 4 out of 6 WinFall numbers right is about 1 in 800, so there must have been about 600 match-4 winners that day out of the 470,000 players. That’s a lot of winners, but $1.4 million dollars is a lot of money… In fact, you’d expect the payout for matching 4 out of 6 numbers that day to be around $2,385. That’s a much more attractive proposition than the measly $150 you’d win on a normal day. A 1-in-800 chance of a $2,385 payoff has an expected value of

$2385 / 800 = $2.98

The match-4 prize alone makes the game profitable. If you add in the other payoffs, the ticket is worth

$50,000 / 39,000 + $2385 / 800 + $60 / 47 = $5.53

So for each $2 you invest, you expect to get back $5.53 on average. To be clear: If you only bought one ticket, you probably wouldn’t win even though the average payoff is positive. However, if you bought one thousand tickets, or ten thousand, then you would almost certainly earn a profitable return of about $5.53 per $2 ticket purchased.

February 7, 2005, is when James Harvey, an MIT senior doing an independent study on the merits of various state lottery games, realized that Massachusetts had accidentally created a highly profitable investment opportunity. Harvey got a group of MIT friends together, and they purchased a thousand tickets. Overall, they tripled their investment. Ellenberg:

It won’t surprise you to hear that Harvey and his co-investors didn’t stop playing Cash WinFall. Or that he never did get around to finishing that independent study–at least not for course credit. In fact, his research project quickly developed into a thriving business. By summer, Harvey’s confederates were buying tens of thousands of tickets at a time… They called their team Random Strategies, though their approach was anything but scattershot; the name referred to Random Hall, the MIT dorm where Harvey had originally cooked up his plan to make money on WinFall.

And the MIT students weren’t alone. At least two more betting clubs formed up to take advantage of the WinFall windfall. Ying Zhang, a medical researcher in Boston with a PhD from Northeastern, formed the Doctor Zhang Lottery Club… Before long, the group was buying $300,000 worth of tickets for each roll-down. In 2006, Doctor Zhang quit doctoring to devote himself full-time to Cash WinFall.

Still another betting group was led by Gerald Selbee, a retiree in his seventies with a bachelor’s degree in math.

 

TWELVE: MISS MORE PLANES!

Assume that if you arrive at the airport 2 hours early, you have a 2% chance of missing the plane; if you arrive 1.5 hours early, you have a 5% chance of missing the plane; and if you arrive 1 hour early, you have a 15% chance of missing the plane. Then, if you think of missing a plane as costing six hours of time, you can calculate the expected cost in utils of arriving at the airport 2 hours, 1.5 hours, and 1 hour early.

Option 1	–2 + 2% x (–6) = –2.12 utils
Option 2	–1.5 + 5% x (–6) = –1.8 utils
Option 3	–1 + 15% x (–6) = –1.9 utils

Under these assumptions, you should arrive at the airport 1.5 hours early. Of course, maybe you really hate missing a plane. Perhaps the cost of missing a plane is –20 utils. In that case, if you redo the expected value, then Option 1 works out to be your best choice.

Ellenberg discusses Pascal’s wager. Say that living a life of piety costs 100 utils. But if the God of Christianity is real, and if you believe it and live accordingly, then the payoff is infinite joy. Say there’s a 5% chance that the God of Christianity is real. Then Pascal’s wager can be written:

(5%) x infinity + (95%) x (–100) = infinity

No matter how small the odds of the Christian God’s existence, a tiny number times infinity is still infinity. Ellenberg comments:

Pascal’s argument has serious flaws. The gravest is that it suffers from… failing to consider all possible hypotheses. In Pascal’s setup, there are only two options: that the God of Christianity is real and will reward that particular sector of the faithful, or that God doesn’t exist. But what if there’s a God who damns Christians eternally? Such a God is surely possible too, and this possibility alone suffices to kill the argument…

Utils can be useful for problems that don’t have well-defined dollar values. But they can also be useful for problems that are stated in dollar values. In 1738, Daniel Bernoulli put forward the St. Petersburg Paradox: “Peter tosses a coin and continues to do so until it should land ‘heads’ when it comes to the ground. He agrees to give Paul one ducat if he gets ‘heads’ on the very first throw, two ducats if he gets it on the second, four if on the third, eight if on the fourth, and so on, so that with each additional throw the number of ducats he must pay is doubled.” The question is: How much should Paul pay in order to play this game? Paul’s expected return is:

(1/2) x 1 + (1/4) x 2 + (1/8) x 4 + (1/16) x 8 + (1/32) x 16 + …

This can be written as:

(1/2) + (1/2) + (1/2) + (1/2) + (1/2) + …

It would seem that the expected dollar value of playing the game is infinite, so Paul should be willing to spend any number of ducats in order to play. Ellenberg:

The mistake, Bernoulli said, is to say that a ducat is a ducat is a ducat… having two thousand ducats isn’t twice as good as having one thousand; it is less than twice as good, because a thousand ducats is worth less to a person who already has a thousand ducats than it is to the person who has none…

Bernoulli thought that utility grew like the logarithm, so that the kth prize of 2^k ducats was worth just k utils…

In Bernoulli’s formulation, the expected utility of the St. Petersburg game is the sum

(1/2) x 1 + (1/4) x 2 + (1/8) x 3 + (1/16) x 4 + ….

This series equals 2. To see this, consider:

(1/2) + (1/4) + (1/8) + (1/16) + (1/32) + … = 1

(1/4) + (1/8) + (1/16) + (1/32) + … = 1/2

(1/8) + (1/16) + (1/32) + … = 1/4

(1/16) + (1/32) + … = 1/8

(1/32) + … = 1/16

The top row is Zeno’s paradox. The series converges to 1. The second row is just the top row, but without the 1/2 at the beginning, so it must equal 1/2. The third row is the same as the second, but without the 1/4 at the beginning, so it must equal 1/4. And so forth. If you look at the series of what each row equals, you get:

1 + (1/2) + (1/4) + (1/8) + (1/16) + (1/32) + … = 2

If you sum up the columns of the five rows above, you get:

1/2 + 2/4 + 3/8 + 4/16 + 5/32 + … = 2

This series converges to 2. So the expected utility of the St. Petersburg game is 2. Paul should be willing to pay 2 ducats in order to play this game. 2 is substantially less than infinity.

 

THIRTEEN: WHERE THE TRAIN TRACKS MEET

Ellenberg writes:

Mathematical elegance and practical utility are close companions, as the history of science has shown again and again. Sometimes scientists discover the theory and leave it to mathematicians to figure out why it’s elegant, and other times mathematicians develop an elegant theory and leave it to scientists to figure out what it’s good for.

Ellenberg introduces the projective plane, which is governed by two axioms:

• • Every pair of points is contained in exactly one common line.
  • Every pair of lines contains exactly one common point.

If you trace out two parallel lines traveling away from you, those two lines meet on the horizon. Call that point P. But that vanishing point is defined to be the same point P if you turned around and looked the opposite direction. In this way, if you think about a graph, the y-axis is a circle going vertically, while the x-axis is a circle going horizontally.

Are there other geometries besides the projective plane that satisfy the two axioms? Yes. For example, the Fano plane:

 

Ellenberg comments:

For Fano and his intellectual heirs, it doesn’t matter whether a line “looks like” a line, a circle, a mallard duck, or anything else–all that matters is that lines obey the laws of lines, set down by Euclid and his successors. If it walks like geometry, and it quacks like geometry, we call it geometry. To one way of thinking, this move constitutes a rupture between mathematics and reality, and is to be resisted. But that view is too conservative. The bold idea that we can think geometrically about systems that don’t look like Euclidean space, and even call these systems “geometries” with head held high, turned out to be critical to understanding the geometry of relativistic space-time we live in; and nowadays we use generalized geometric ideas to map Internet landscapes, which are even further removed from anything Euclid would recognize. That’s part of the glory of math; we develop a body of ideas, and once they’re correct, they’re correct, even when applied far, far outside the context in which they were first conceived.

Looking at Fano’s plane again, there are seven lines (one of which is the circle), each of which has three points on it.

• • 124
  • 135
  • 167
  • 257
  • 347
  • 236
  • 456

Look familiar? Ellenberg:

This is none other than the seven-ticket combo we saw in the last section, the one that hits each pair of numbers exactly once, guaranteeing a minimum payoff…

…it’s simply geometry. Each pair of numbers appears on exactly one ticket, because each pair of points appears on exactly one line. It’s just Euclid, even though we’re speaking now of points and lines Euclid would not have recognized as such.

What about the Massachusetts lottery? There is no geometry that fits the precise requirements. Ellenberg says we should consider the theory of digital signal processing. Say we’re trying to send the following digital message:

1110101…

Assume this is a communication to a satellite that says, “Turn on right thruster.” But what if the message get garbled and sends the following instead:

1010101…

This could mean, “Turn on the left thruster.” That would be a serious problem. A solution is to send each digit twice. So the original message looks like:

11 11 11 00 11 00 11…

However, this still doesn’t solve the issue of potential garbling because if 11 gets turned into 01, we don’t know if 01 is supposed to be 11 or 00. This can be solved by repeating each digit three times. So the message looks like:

111 111 111 000 111 000 111…

Now if 111 gets garbled and turns into 101, the satellite knows it’s supposed to be 111. That’s not a guarantee, of course, but there’s at least a high probability that the original message was 111. Ellenberg asks whether there can really be a mathematical theory of communication. Ellenberg comments:

Understand this: I warmly endorse, in fact highly recommend, a bristly skepticism in the face of all claims that such-and-such an entity can be explained, or tamed, or fully understood, by mathematical means.

And yet the history of mathematics is a history of aggressive territorial expansion, as mathematical techniques get broader and richer, and mathematicians find ways to address questions previously thought of as outside their domain. “A mathematical theory of probability” sounds unexceptional now, but once it would have seemed a massive overreach; math was about the certain and the true, not the random and the maybe-so! All that changed when Pascal, Bernoulli, and others found mathematical laws that governed the workings of chance. A mathematical theory of infinity? Before the work of Georg Cantor in the nineteenth century, the study of the infinite was as much theology as science; now we understand Cantor’s theory of multiple infinities, each one infinitely larger than the last, well enough to teach it to first-year math majors.

These mathematical formalisms don’t capture every detail of the phenomena they describe, and aren’t intended to. There are questions about randomness, for instance, about which probability theory is silent…

People are working on mathematical theories that can explain and describe consciousness, society, aesthetics, and other areas. Though success has thus far been limited, mathematics may end up getting some important points right, notes Ellenberg.

Regarding information theory, a colleague of Claude Shannon, Richard Hamming, was trying to run his programs on the weekend, but any error would halt the computation, with no one to get the machine running again until Monday morning. So Hamming thought of a way for the machine to correct its own errors. First he broke the message into blocks of three symbols. Then he invented a way for each three-digit block to be transformed into a seven-digit string. This is the Hamming code:

• • 000 -> 0000000
  • 001 -> 0010111
  • 010 -> 0101011
  • 011 -> 0111100
  • 101 -> 1011010
  • 110 -> 1100110
  • 100 -> 1001101
  • 111 -> 1110001

If the receiver gets anything that isn’t a code word, something has gone wrong. Also, if a message is only different from a code word by one digit, you can safely infer which code word was intended.

Look back at the lines in the Fano plane.

…the seven nonzero code words in the Hamming code match up exactly to the seven lines in the Fano plane [124 is 0010111, for instance, while 135 is 0101011]. The Hamming code and the Fano plane… are exactly the same mathematical object in two different outfits!

Note that the Hamming code sends just seven bits for every three bits of your original message, a more efficient ratio of 2.33 (vs. 3 for the repeat-three-times code presented earlier). There’s also the idea of Hamming distance, which is the number of bits you need to alter to go from one code word to another. Two different code words are at a Hamming distance of at least 4 from each other.

Ellenberg continues:

Hamming’s notion of “distance” follows Fano’s philosophy… But why stop there? The set of points at distance less than or equal to 1 from a given central point… is called a circle, or, if we are in higher dimensions, a sphere. So we’re compelled to call the set of strings at Hamming distance at most 1 from a code word a “Hamming sphere,” with the code word at the center. For a code to be an error-correcting code, no string–no point, if we’re to take this geometric analogy seriously–can be within distance 1 of two different code words; in other words, we ask that no two of the Hamming spheres centered at the code words share any points.

So the problem of constructing error-correcting codes has the same structure as a classical geometric problem, that of sphere packing: how do we fit a bunch of equal-sized spheres as tightly as possible into a small space, in such a way that no two spheres overlap? More succinctly, how many oranges can you stuff into a box?

If playing the lottery is fun, then it’s OK to spend a few dollars regularly playing. Also, you won’t end up in a lower class by playing, but if you get lucky and win, you could end up in a higher class. The bottom line, as Ellenberg says, is that mathematics gives you permission to go ahead and play Powerball if it’s fun for you.

Ellenberg concludes by comparing an entrepreneur to someone who plays the lottery:

…That’s the nature of entrepreneurship: you balance a very, very small probability of making a fortune against a modest probability of eking out a living against a substantially larger probability of losing your pile, and for a large proportion of potential entrepreneurs, when you crunch the numbers, the expected financial value, like that of a lottery ticket, is less than zero… And yet society benefits from a world in which people, against their wiser judgment, launch businesses.

Perhaps the biggest part of the utility of running a business, notes Ellenberg, is the act of realizing a dream, or even trying to realize it.

 

PART IV: REGRESSION

FOURTEEN: THE TRIUMPH OF MEDIOCRITY

Horace Secrist was a professor of statistics and director of the Bureau for Business Research at Northwestern. Since 1920 and into the great crash, Secrist had been tracking a number of statistics on various businesses. What he found was reversion to the mean, i.e., regression to mediocrity:

Secrist found the same phenomenon in every kind of business. Hardware stores regressed to mediocrity; so did grocery stores. And it didn’t matter what metric you used. Secrist tried measuring his companies by the ratio of wages to sales, the ratio of rent to sales, and whatever other economic stat he could put his hands on. It didn’t matter. With time, the top performers started to look and behave just like the members of the common mass.

Similarly, Secrist found that the bottom performers improved and became more like the average. Secrist’s views descend from those of the nineteenth-century British scientist, Francis Galton.

Galton had a peripatetic education; he tried studying mathematics at Cambridge but was defeated by the brutal Tripos exam, and devoted himself intermittently to the study of medicine, the line of work his parents had planned for him. But after his father died in 1844, leaving him a substantial fortune, he found himself suddenly less motivated to pursue a traditional career. For a while Galton was an explorer, leading expeditions into the African interior. But the epochal publication of The Origin of Species in 1859 catalyzed a drastic shift in his interests: …from then on, the greater share of Galton’s work was devoted to the heredity of human characteristics, both physical and mental.

(Sir Francis Galton in the 1850s or early 1860s, scanned from Karl Pearson’s biography, via Wikimedia Commons)

Galton discovered regression to the mean. For example, tall parents were likely to have tall children, but those children were usually not as tall as the parents. Similarly, short parents were likely to have short children, but those children were usually not as share as the parents. In both cases, there is regression to the mean. Ellenberg:

So, too, Galton reasoned, must it be for mental achievement. And this conforms with common experience; the children of a great composer, or scientist, or political leader, often excel in the same field, but seldom so much so as their illustrious parent. Galton was observing the same phenomenon that Secrist would uncover in the operations of business. Excellence doesn’t persist; time passes, and mediocrity asserts itself.

People are tall due to a combination of genetics and chance. Genetics persist, but chance does not persist. That’s why the children of tall parents tend to be tall, but not as tall as their parents: the factor of chance does not persist. Ellenberg explains:

It’s just the same for businesses. Secrist wasn’t wrong about the firms that had the fattest profits in 1922; it’s likely that they ranked among the most well-managed companies in their sectors. But they were lucky, too. As time went by, their management might well have remained superior in wisdom and judgment. But the companies that were lucky in 1922 were no more likely than any other companies to be lucky ten years later.

 

FIFTEEN: GALTON’S ELLIPSE

A scatterplot of the heights of fathers versus the heights of sons arranges itself in an elliptical pattern, where the heights of sons are closer to the mean than the heights of fathers. Ellenberg:

…we have ellipses of various levels of skinniness. That skinniness, which the classical geometers call the eccentricity of the ellipse, is a measure of the extent to which the height of the father determines that of the son. High eccentricity means that heredity is powerful and regression to the mean is weak; low eccentricity means the opposite, that regression to the mean holds sway. Galton called his measure correlation, the term we still use today. If Galton’s ellipse is almost round, the correlation is near 0; when the ellipse is skinny, lined up along the northeast-southwest axis, the correlation comes close to 1.

It’s important to note that correlation is not transitive. A may be correlated with B, and B with C, but that doesn’t mean A is correlated with C. Ellenberg:

If correlation were transitive, medical research would be a lot easier than it actually is. Decades of observation and data collection have given us lots of known correlations to work with. If we had transitivity, doctors could just chain these together into reliable interventions. We know that women’s estrogen levels are correlated with lower risk of heart disease, and we know that hormone replacement therapy can raise those levels, so you might expect hormone replacement therapy to be protective against heart disease. And, indeed, that used to be conventional clinical wisdom. But the truth, as you’ve probably heard, is a lot more complicated. In the early 2000s, the Women’s Health Initiative, a long-term study involving a gigantic randomized clinical trial, reported that hormone replacement therapy with estrogen and progestin appeared actually to increase the risk of heart disease in the population they studied…

In the real world, it’s next to impossible to predict what effect a drug will have on a disease, even if you know a lot about how it affects biomarkers like HDL or estrogen level. The human body is an immensely complex system, and there are only a few of its features we can measure, let alone manipulate. But on the correlations we can observe, there are lots of drugs that might plausibly have a desired health effect. And so you try them out in experiments, and most of them fail dismally. To work in drug development requires a resilient psyche, not to mention a vast pool of capital.

 

SIXTEEN: DOES LUNG CANCER MAKE YOU SMOKE CIGARETTES?

Correlation is not causation:

(Photo by Alain Lacroix)

Ellenberg writes:

Teasing apart correlations that come from causal relationships from those that don’t is a maddeningly hard problem, even in cases you might think of as obvious, like the relation between smoking and lung cancer. At the turn of the twentieth century, lung cancer was an extremely rare disease. But by 1947, the disease accounted for nearly a fifth of cancer deaths among British men, killing fifteen times as many people as it had a few decades earlier. At first, many researchers thought that lung cancer was simply being diagnosed more effectively than before, but it soon became clear that the increase in cases was too big and too fast to be accounted for by any such effect. Lung cancer really was on the rise. But no one was sure what to blame. Maybe it was smoke from factories, maybe increased levels of car exhaust, or maybe some substance not even thought of as a pollutant. Or maybe it was cigarette smoking, whose popularity had exploded during the same period.

In the 1950s, there were some large studies that showed a correlation between smoking and lung cancer. That doesn’t establish causality. Perhaps lung cancer causes smoking, or perhaps there is a common cause that leads to both smoking and lung cancer. It’s not very reasonable to assert that lung cancer causes smoking, because the tumor would have to reach back years into the past to start causing smoking. But it is possible that there is some cause responsible for both smoking and lung cancer. Keep in mind, notes Ellenberg, that in the 1950s, no chemical component of tobacco had yet been shown to produce tumors in the lab. Today, things are different. We know that smoking does cause lung cancer:

We know a lot more now about cancer and how tobacco brings it about. That smoking gives you cancer is no longer in serious dispute.

Back in the 1950s, the evidence was not so clear. But this seemed to change in the 1960s. Ellenberg:

By 1964, the association between smoking and cancer had appeared consistently across study after study. Heavier smokers suffered more cancer than lighter smokers, and cancer was most likely at the point of contact between tobacco and human tissue; cigarette smokers got more lung cancer, pipe smokers more lip cancer. Ex-smokers were less prone to cancer than smokers who kept up the habit. All these factors combined to lead the surgeon general’s committee to the conclusion that smoking was not just correlated with lung cancer, but caused lung cancer, and that efforts to reduce tobacco consumption would be likely to lengthen American lives.

From a public policy point of view, whether to come out against smoking depends on the expected value of doing so:

…So we don’t and can’t know the exact expected value of launching a campaign against…tobacco. But often we can say with confidence that the expected value is positive. Again, that doesn’t mean the campaign is sure to have good effects, only that the sum total of all similar campaigns, over time, is likely to do more good than harm. The very nature of uncertainty is that we don’t know which of our choices will help, like attacking tobacco, and which will hurt, like recommending hormone replacement therapy. But one thing’s for certain: refraining from making recommendations at all, on the grounds that they might be wrong, is a losing strategy. It’s a lot like George Stigler’s advice about missing planes. If you never give advice until you’re sure it’s right, you’re not giving enough advice.

 

PART V: EXISTENCE

SEVENTEEN: THERE IS NO SUCH THING AS PUBLIC OPINION

Americans report that they would rather cut government programs–and make government smaller–than pay more taxes. Ellenberg:

But which government programs? That’s where things get sticky. It turns out the things the U.S. government spends money on are things people kind of like. A Pew Research poll from February 2011 asked Americans about thirteen categories of government spending: in eleven of those categories, deficit or no deficit, more people wanted to increase spending than dial it down. Only foreign aid and unemployment insurance–which, combined, accounted for under 5% of 2010 spending–got the ax. That, too, agrees with years of data; the average American is always eager to slash foreign aid, occasionally tolerant of cuts of welfare or defense, and pretty gung ho for increased spending on every single other program our taxes fund.

Oh, yeah, and we want small government.

(Photo by David Watmough)

Ellenberg again:

The average American thinks there are plenty of non-worthwhile federal programs that are wasting our money and is ready and willing to put them on the chopping block to make ends meet. The problem is, there’s no consensus on which programs are the worthless ones. In large part, that’s because most Americans think the programs that benefit them personally are the ones that must, at all costs, be preserved…

Ellenberg continues:

The “majority rules” system is simple and elegant and feels fair, but it’s at its best when deciding between just two options. Any more than two, and contradictions start to seep into the majority’s preferences. As I write this, Americans are sharply divided over President Obama’s signature domestic policy accomplishment, the Affordable Care Act. In an October 2010 poll of likely voters, 52% of respondents said they opposed the law, while only 41% supported it. Bad news for Obama? Not once you break down the numbers. Outright repeal of health care reform was favored by 37%, with another 10% saying the law should be weakened; but 15% preferred to leave it as is, and 36% said the ACA should be expanded to change the current health care system more than it currently does. That suggests that many of the law’s opponents are to Obama’s left, not his right. There are (at least) three choices here: leave the health care law alone, kill it, or make it stronger. And each of the three choices is opposed by most Americans.

Ellenberg adds:

The incoherence of the majority creates plentiful opportunities to mislead. Here’s how Fox News might report the poll results above:

Majority of Americans oppose Obamacare!

And this is how it might look to MSNBC:

Majority of Americans want to preserve or strengthen Obamacare!

These two headlines tell very different stories about public opinion. Annoyingly enough, both are true.

But both are incomplete. The poll watcher who aspires not to be wrong has to test each of the poll’s options, to see whether it might break down into different-colored pieces. Fifty-six percent of the population disapproves of President Obama’s policy in the Middle East? That impressive figure might include people from both the no-blood-for-oil left and the nuke-’em-all right, with a few Pat Buchananists and devoted libertarians in the mix. By itself, it tells us just about nothing about what the people really want.

Ellenberg later writes about experiments studying the decision making of slime mold. Note that slime mold likes to eat oats and likes to avoid light. (Note also that you can train a slime mold to navigate through a maze by using oats.) In one experiment, the slime mold is faced with a choice: 3 grams of oats in the dark (3-dark) versus 5 grams of oats in the light (5-light). In this scenario, the slime mold picks 3-dark half the time and 5-light half the time.

Now, if you replace the 5 grams of oats with 10 grams (10-light), the slime mold chooses 10-light every time.

However, something strange happens if you have 3-dark and 5-light options, but then add a third option: 1-dark (1 gram of oats in the dark). You might think that the slime mold would never pick 1-dark. That’s true. But you might also think that the slime mold continues to pick 3-dark half the time and 5-light half the time. But that’s not what the slime mold does. Faced with 1-dark, 3-dark, and 5-light, the slime mold picks 3-dark more than three times as often as 5-light. Ellenberg:

The mathematical buzzword in play here is “independence of irrelevant alternatives.” That’s a rule that says, whether you’re a slime mold, a human being, or a democratic nation, if you have a choice between two options, A and B, the presence of a third option, C, shouldn’t affect which of A and B you like better.

Ellenberg gives an example. In Florida in the 2000 election, the majority preferred Gore over Bush. However, the presence of an irrelevant alternative–Ralph Nader–tipped the election to Bush. Bush got 48.85% of the vote, Gore got 48.84%, while Nader got 1.6%. Back to the slime mold:

…the slime mold likes the small, unlit pile of oats about as much as it likes the big, brightly lit one. But if you introduce a really small unlit pile of oats, the small dark pile looks better by comparison; so much so that the slime mold decides to choose it over the big bright pile almost all the time.

This phenomenon is called the “asymmetric domination effect,” and slime molds are not the only creatures subject to it. Biologists have found jays, honeybees, and hummingbirds acting in the same seemingly irrational way.

Not to mention humans!

Ellenberg tries to explain human irrationality:

Maybe individual people seem irrational because they aren’t really individuals! Each one of us is a little nation-state, doing our best to settle disputes and broker compromises between the squabbling voices that drive us. The results don’t always make sense. But they somehow allow us, like the slime molds, to shamble along without making too many terrible mistakes. Democracy is a mess–but it kind of works.

 

EIGHTEEN: “OUT OF NOTHING I HAVE CREATED A STRANGE NEW UNIVERSE”

Ellenberg poses an interesting question:

Are we trying to figure out what’s true, or are we trying to figure out what conclusions are licensed by our rules and procedures? Hopefully the two notions frequently agree; but all the difficulty, and all the conceptually interesting stuff, happens at the points where they diverge.

You might think it’s obvious that figuring out what’s true is always our proper business. But that’s not always the case in criminal law, where the difference presents itself quite starkly in the form of defendants who committed the crime but who cannot be convicted (say, because evidence was obtained improperly) or who are innocent of the crime but are convicted anyway. What’s justice here–to punish the guilty and free the innocent, or to follow criminal procedure wherever it leads us? In experimental science, we’ve already seen the dispute with R.A. Fisher on one side and Jerzy Neyman and Egon Pearson on the other. Are we, as Fisher thought, trying to figure out which hypotheses we should actually believe are true? Or are we to follow the Neyman-Pearson philosophy, under which we resist thinking about the truth of hypotheses at all and merely ask: Which hypotheses are we to certify as correct, whether they’re really true or not, according to our chosen rules of inference?

You might think that mathematics itself doesn’t have such problems. But it does. Consider the parallel postulate, Euclid’s fifth axiom: “If P is a point and L is a line not passing through P, there is exactly one line through P parallel to L.” Even Euclid was thought to dislike his fifth axiom. He proved the first twenty-eight propositions in the Elements using only the first four axioms.

In 1820, the Hungarian noble Farkas Bolyai, who had given years of his life to the problem, wrote a letter to his son trying to dissuade him from attempting to solve the same problem. However, Janos Bolyai ignored his father’s advice. By 1823, he had an outline of the solution. He wrote to his father, “out of nothing I have created a strange new universe.”

Jonas Bolyai inverted the problem and asked: If the parallel axiom were false, would a contradiction follow? Bolyai realized the answer was no:

…there was another geometry, not Euclid’s but something else, in which the first four axioms were correct but the parallel postulate was not. Thus, there can be no proof of the parallel postulate from the other axioms; such a proof would rule out the possibility of Bolyai’s geometry. But there it was.

Sometimes, a mathematical development is “in the air”–for reasons only poorly understood, the community is ready for a certain advance to come, and it comes from several sources at once. Just as Bolyai was constructing his non-Euclidean geometry in Austria-Hungary, Nikolai Lobachevskii was doing the same in Russia. And the great Carl Friedrich Gauss, an old friend of the senior Bolyai, had formulated many of the same ideas in work that had not yet seen print…

A few decades later, Bernhard Riemann pointed out that there’s a simpler non-Euclidean geometry: the geometry of the sphere. Consider Euclid’s first four axioms:

• • There is a Line joining any two Points.
  • Any Line segment can be extended to a Line segment of any desired length.
  • For every Line segment L, there is a Circle which has L as a radius.
  • All Right Angles are congruent to each other.

Ellenberg on Riemann’s spherical geometry:

A Point is a pair of points on the sphere which are antipodal, or diametrically opposite each other. A Line is a “great circle”–that is, a circle on the sphere’s surface–and a Line segment is a segment of such a circle. A Circle is a circle, now allowed to be of any size.

Using these definitions, Euclid’s first four axioms are true. Ellenberg comments:

Here’s the thing; once you understand that the first four axioms apply to many different geometries, then any theorem Euclid proves from only those axioms must be true, not only in Euclid’s geometry, but in all the geometries where those axioms hold. It’s a kind of mathematical force multiplier; from one proof, you get many theorems.

And these theorems are not just about abstract geometries made up to prove a point. Post-Einstein, we understand that non-Euclidean geometry is not just a game; like it or not, it’s the way space-time actually looks.

This is a story told in mathematics again and again: we develop a method that works for one problem, and if it is a good method, one that really contains a new idea, we typically find that the same proof works in many different contexts, which may be as different from the original as a sphere is from a plane, or more so.

Ellenberg continues:

The tradition is called “formalism.” It’s what G. H. Hardy was talking about when he remarked, admiringly, that mathematicians of the nineteenth century finally began to ask what things like

1 – 1 + 1 – 1 + …

should be defined to be, rather than what they were… In the purest version of this view, mathematics becomes a kind of game played with symbols and words. A statement is a theorem precisely if it follows by logical steps from the axioms. But what the axioms and theorems refer to, what they mean, is up for grabs. What is a Point, or a Line…? It can be anything that behaves the way the axioms demand, and the meaning we should choose is whichever one suits our present needs. A purely formal geometry is a geometry you can in principle do without ever having seen or imagined a point or a line; it is a geometry in which it’s irrelevant what points and lines, understood in the usual way, are actually like.

Mathematical formalism shares similarities with legal formalism.

In Scalia’s view, when judges try to understand what the law intends–its spirit–they’re inevitably bamboozled by their own prejudices and desires. Better to stick to the words of the Constitution and the statutes, treating them as axioms from which judgments can be derived by something like logical deduction.

Ellenberg continues:

Formalism has an austere elegance. It appeals to people like G. H. Hardy, Antonin Scalia, and me, who relish that feeling of a nice rigid theory shut tight against contradiction. But it’s not easy to hold to principles like this consistently, and it’s not clear it’s even wise. Even Justice Scalia has occasionally conceded that when the literal words of the law seem to require an absurd judgment, the literal words have to be set aside in favor of a reasonable guess as to what Congress must have meant. In just the same way, no scientist really wants to be bound strictly by the rules of significance, no matter what they say their principles are. When you run two experiments, one testing a clinical treatment that seems theoretically promising and the other testing whether dead salmon respond emotionally to romantic photos, and both experiments succeed with p-values of .03, you don’t really want to treat the two hypotheses the same.

Ellenberg then notes that the German mathematician David Hilbert was formalism’s greatest champion in mathematics.

(David Hilbert before 1912, via Wikimedia Commons)

Hilbert wanted to create a purely formal mathematics. To say that a statement was true was to say that it could be derived logically from the axioms. However, says Ellenberg:

Mathematics has a nasty habit of showing that, sometimes, what’s obviously true is absolutely wrong.

For an example, consider set theory. An ouroboric set has itself as a member.

Let NO be the set of all non-ouroboric sets…

Is NO ouroboric or not? That is, is NO an element of NO? By definition, if NO is ouroboric, then NO cannot be in NO, which consists only of non-ouroboric sets. But to say NO is not an element of NO is precisely to say NO is non-ouroboric; it does not contain itself.

But wait a minute–if NO is non-ouroboric, then it is an element of NO, which is the set of all non-ouroboric sets. Now NO is an element of NO after all, which is to say that NO is ouroboric.

If NO is ouroboric, it isn’t, and if it isn’t, it is.

But could finite arithmetic be proved consistent? Ellenberg:

Hilbert sought a finitary proof of consistency, one that did not make reference to any infinite sets, one that a rational mind couldn’t help but wholly believe.

But Hilbert was to be disappointed. In 1931, Kurt Godel proved in his famous second incompleteness theorem that there could be no finite proof of the consistency of arithmetic. He had killed Hilbert’s program with a single stroke.

Nonetheless:

Hilbert’s style of mathematics survived the death of his formalist program.

 

HOW TO BE RIGHT

Nearly everyone has heard of Theodore Roosevelt’s speech “Citizenship in a Republic,” which he delivered in Paris in 1910. Here’s the part people love to quote:

It is not the critic who counts; not the man who points out how the strong man stumbles, or where the doer of deeds could have done them better. The credit belongs to the man who is actually in the arena, whose faced is marred by dust and sweat and blood; who strives valiantly; who errs, who comes short again and again, because there is no effort without error and shortcoming; but who does actually strive to do the deeds; who knows great enthusiasms, the great devotions; who spends himself in a worthy cause; who at the best knows in the end the triumph of high achievement, and who at the worst, if he fails, at least fails while daring greatly, so that his place shall never be with those cold and timid souls who neither know victory nor defeat.

Ellenberg writes:

And yet, when Roosevelt says, “The closet philosopher, the refined and cultured individual who from his library tells how men ought to be governed under ideal conditions, is of no use in actual governmental work,” I think of Condorcet, who spent his time in the library doing just that, and who contributed more to the French state than most of his time’s more practical men. And when Roosevelt sneers at the cold and timid souls who sit on the sidelines and second-guess the warriors, I come back to Abraham Wald, who as far as I know went his whole life without lifting a weapon in anger, but who nonetheless played a serious part in the American war effort, precisely by counseling the doers of deeds how to do them better. He was unsweaty, undusty, and unbloody, but he was right. He was a critic who counted.

Mathematics not only deals with certainties, but also allows us to deal with uncertainty. Ellenberg:

Math gives us a way of being unsure in a principled way: not just throwing up our hands and saying “huh,” but rather making a firm assertion: “I’m not sure, this is why I’m not sure, and this is roughly how not-sure I am.” Or even more: “I’m unsure, and you should be too.”

Ellenberg comments:

The paladin of principled uncertainty in our time is Nate Silver, the online-poker-player-turned-baseball-statistics-maven-turned-political analyst…

What made Silver so good? In large part, it’s that he was willing to talk about uncertainty, willing to treat uncertainty not as a sign of weakness but as a real thing in the world, a thing that can be studied with scientific rigor and employed to good effect. If it’s September 2012 and you ask a bunch of political pundits, “Who’s going to be elected president in November?” a bunch of them are going to say, “Obama is,” and a somewhat smaller bunch are going to say, “Romney is,” and the point is that all of those people are wrong, because the right answer is the kind of answer that Silver, almost alone in the broad-reach media, was willing to give: “Either one might win, but Obama is substantially more likely to win.”

One important piece of advice for mathematicians who are trying to prove a theorem is to divide your time between trying to prove the theorem and trying to disprove it. First, the theorem could be wrong, in which case the sooner you realize that, the better. Second, if the theorem is true and you try to disprove it, eventually you will get a better idea of how to prove that the theorem is true.

This self-critical attitude applies to other areas besides mathematics, says Ellenberg:

Proving by day and disproving by might is not just for mathematics. I find it’s a good habit to put pressure on all your beliefs, social, political, scientific, and philosophical. Believe whatever you believe by day; but at night, argue against the propositions you hold most dear. Don’t cheat! To the greatest extent possible you have to think as though you believe what you don’t believe. And if you can’t talk yourself out of your existing beliefs, you’ll know a lot more about why you believe what you believe. You’ll have come a little closer to a proof.

Ellenberg concludes:

What’s true is that the sensation of mathematical understanding–of suddenly knowing what’s going on, with total certainty, all the way to the bottom–is a special thing, attainable in few if any other places in life. You feel you’ve reached in to the universe’s guts and put your hand on the wire. It’s hard to describe to people who haven’t experienced it.

We are not free to say whatever we like about the wild entities we make up. They require definition, and having been defined, they are no more psychedelic than trees and fish; they are what they are. To do mathematics is to be, at once, touched by fire and bound by reason. This is no contradiction. Logic forms a narrow channel through which intuition flows with vastly augmented force.

The lessons of mathematics are simple ones and there are no numbers in them: that there is structure in the world; that we can hope to understand some of it and not just gape at what our senses present to us; that our intuition is stronger with a formal exoskeleton than without one. And that mathematical certainty is one thing, the softer convictions we find attached to us in everyday life another, and we should keep track of the difference if we can.

Every time you observe that more of a good thing is not always better; or you remember that improbable things happen a lot, given enough chances, and resist the lure of the Baltimore stockbroker; or you make a decision based not just on the most likely future, but on the cloud of all possible futures, with attention to which ones are likely and which ones are not; or you let go of the idea that the beliefs of groups should be subject to the same rules as beliefs of individuals; or, simply, you find that cognitive sweet spot where you can let your intuition run wild on the network of tracks formal reasoning makes for it; without writing down an equation or drawing a graph, you are doing mathematics, the extension of common sense by other means. When are you going to use it? You’ve been using mathematics since you were born and you’ll probably never stop. Use it well.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time. See the historical chart here: https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps. Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals. We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio. The size of each position is determined by its rank. Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost). Positions are held for 3 to 5 years unless a stock approachesintrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods. We also aim to outpace the Russell Microcap Index by at least 2% per year (net). The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: [email protected]

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

September 4, 2022

A wise long-term investment for most investors is an S&P 500 index fund. It’s just simple arithmetic, as Warren Buffett and Jack Bogle frequently observe: https://boolefund.com/warren-buffett-jack-bogle/

But you can do significantly better–roughly 18% per year (instead of 10% per year)–by systematically investing in cheap, solid microcap stocks.The mission of the Boole Microcap Fund is to help you do just that.

Most professional investors never consider microcaps because their assets under management are too large. Microcaps aren’t as profitable for them. That’s why there continues to be a compelling opportunity for savvy investors. Because microcaps are largely ignored, many get quite cheap on occasion.

Warren Buffett earned the highest returns of his career when he could invest in microcap stocks. Buffett says he’d do the same today if he were managing small sums:https://boolefund.com/buffetts-best-microcap-cigar-butts/

Look at this summary of the CRSP Decile-Based Size and Return Data from 1927 to 2020:

Decile	Market Cap-Weighted Returns	Equal Weighted Returns	Number of Firms (year-end 2020)	Mean Firm Size (in millions)
1	9.67%	9.47%	179	145,103
2	10.68%	10.63%	173	25,405
3	11.38%	11.17%	187	12,600
4	11.53%	11.29%	203	6,807
5	12.12%	12.03%	217	4,199
6	11.75%	11.60%	255	2,771
7	12.01%	11.99%	297	1,706
8	12.03%	12.33%	387	888
9	11.55%	12.51%	471	417
10	12.41%	17.27%	1,023	99
9+10	11.71%	15.77%	1,494	199

(CRSP is the Center for Research in Security Prices at the University of Chicago. You can find the data for various deciles here: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html)

The smallest two deciles–9+10–comprise microcap stocks, which typically are stocks with market caps below $500 million. What stands out is the equal weighted returns of the 9^th and 10^th size deciles from 1927 to 2020:

Microcap equal weighted returns = 15.8% per year

Large-cap equal weighted returns = ~10% per year

In practice, the annual returns from microcap stocks will be 1-2% lower because of the difficulty (due to illiquidity) of entering and exiting positions. So we should say that an equal weighted microcap approach has returned 14% per year from 1927 to 2020, versus 10% per year for an equal weighted large-cap approach.

Still, if you can do 4% better per year than the S&P 500 index (on average)–even with only a part of your total portfolio–that really adds up after a couple of decades.

 

VALUE SCREEN: +2-3%

By systematically implementing a value screen–e.g., low EV/EBITDA or low P/E–to a microcap strategy, you can add 2-3% per year.

IMPROVING FUNDAMENTALS: +2-3%

You can further boost performance by screening for improving fundamentals. One excellent way to do this is using the Piotroski F_Score, which works best for cheap micro caps. See: https://boolefund.com/joseph-piotroski-value-investing/

 

BOTTOM LINE

If you invest in microcap stocks, you can get about 14% a year. If you also use a simple screen for value, that adds at least 2% a year. If, in addition, you screen for improving fundamentals, that adds at least another 2% a year. So that takes you to 18% a year, which compares quite well to the 10% a year you could get from an S&P 500 index fund.

What’s the difference between 18% a year and 10% a year? If you invest $50,000 at 10% a year for 30 years, you end up with $872,000, which is good. If you invest $50,000 at 18% a year for 30 years, you end up with $7.17 million, which is much better.

Please contact me if you would like to learn more.

• • My email: [email protected].
  • My cell: 206.518.2519

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time. See the historical chart here: https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps. Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals. We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio. The size of each position is determined by its rank. Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost). Positions are held for 3 to 5 years unless a stock approachesintrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods. We also aim to outpace the Russell Microcap Index by at least 2% per year (net). The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: [email protected]

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed.No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

August 28, 2022

Philip Tetlock is the world expert on the topic of making forecasts.  Tetlock and Dan Gardner have written an excellent book, Superforecasting: The Art and Science of Prediction, which summarizes the mental habits of people who have consistently been able to make useful predictions about the future.  Everyone would benefit if we could predict the future better.  This book teaches us how we can do this if we’re willing to put in the hard work required.

Here’s an outline:

• • An Optimistic Skeptic
  • Illusions of Knowledge
  • Keeping Score
  • Superforecasters
  • Supersmart?
  • Superquants?
  • Supernewsjunkies?
  • Perpetual Beta
  • Superteams
  • The Leader’s Dilemma
  • Are They Really So Super?
  • What’s Next?
  • Ten Commandments for Aspiring Superforecasters

 

AN OPTIMISTIC SKEPTIC

Tetlock writes that we are all forecasters.  Whenever we think about making an important decision””changing jobs, getting married, buying a home, making an investment, launching a product, or retiring””we make forecasts about the future.  Tetlock:

Often we do our own forecasting.  But when big events happen””markets crash, wars loom, leaders tremble””we turn to the experts, those in the know.  We look to people like Tom Friedman.

If you are a White House staffer, you might find him in the Oval Office with the president of the United States, talking about the Middle East.  If you are a Fortune 500 CEO, you might spot him in Davos, chatting in the lounge with hedge fund billionaires and Saudi princes.  And if you don’t frequent the White House or swanky Swiss hotels, you can read his New York Times columns and bestselling books that tell you what’s happening now, why, and what will happen next.  Millions do.

Tetlock compares Friedman to Bill Flack, who forecasts global events.  Bill recently retired from the Department of Agriculture.  He is fifty-five and spends some of his time forecasting.

Bill has answered roughly three hundred questions like “Will Russia officially annex additional Ukrainian territory in the next three months?” and “In the next year, will any country withdraw from the eurozone?”  They are questions that matter.  And they’re difficult.  Corporations, banks, embassies, and intelligence agencies struggle to answer such questions all the time.  “Will North Korea detonate a nuclear device before the end of this year?”  “How many additional countries will report cases of the Ebola virus in the next eight months?”  “Will India or Brazil become a permanent member of the UN Security Council in the next two years?”  Some of the questions are downright obscure, at least for most of us.  “Will NATO invite new countries to join the Membership Action Plan (MAP) in the next nine months?”  “Will the Kurdistan Regional Government hold a referendum on national independence this year?”  “If a non-Chinese telecommunications firm wins a contract to provide Internet services in the Shanghai Free Trade Zone in the next two years, will Chinese citizens have access to Facebook and/or Twitter?”  When Bill first sees one of these questions, he may have no clue how to answer it.  “What on earth is the Shanghai Free Trade Zone?” he may think.  But he does his homework.  He gathers facts, balances clashing arguments, and settles on an answer.

Tetlock continues:

No one bases decisions on Bill Flack’s forecasts, or asks Bill to share his thoughts on CNN.  He has never been invited to Davos to sit on a panel with Tom Friedman.  And that’s unfortunate.  Because Bill Flack is a remarkable forecaster.  We know that because each one of Bill’s predictions has been dated, recorded, and assessed for accuracy by independent scientific observers.  His track record is excellent.

Bill is not alone.  There are thousands of others answering the same questions.  All are volunteers.  Most aren’t as good as Bill, but about 2% are.  They include engineers and lawyers, artists and scientists, Wall Streeters and Main Streeters, professors and students… I call them superforecasters because that is what they are.  Reliable evidence proves it.  Explaining why they’re so good, and how others can learn to do what they do, is my goal in this book.

Tetlock points out that it would be interesting to compare superforecasters to an expert like Tom Friedman.  However, Friedman’s track record has never been rigorously tested.  Of course, there are endless opinions about Friedman’s track record.  Tetlock:

Every day, the news media deliver forecasts without reporting, or even asking, how good the forecasters who made the forecasts really are.  Every day, corporations and governments pay for forecasts that may be prescient or worthless or something in between.  And every day, all of us””leaders of nations, corporate executives, investors, and voters””make critical decisions on the basis of forecasts whose quality is unknown.  Baseball managers wouldn’t dream of getting out the checkbook to hire a player without consulting performance statistics… And yet when it comes to the forecasters who help us make decisions that matter far more than any baseball game, we’re content to be ignorant.

That said, forecasting is a skill.  This book, says Tetlock, will show you how.

Prior to his work with superforecasters, Tetlock conducted a 20-year research project in which close to 300 experts made predictions about the economy, stocks, elections, wars, and other issues.  The experts made roughly 28,000 predictions.  On the whole, the experts were no better than chance.  The average expert was no better than a dart-throwing chimpanzee.  Tetlock says he doesn’t mind the joke about the dart-throwing chimpanzee because it makes a valid point:

Open any newspaper, watch any TV news show, and you find experts who forecast what’s coming.  Some are cautious.  More are bold and confident.  A handful claim to be Olympian visionaries able to see decades into the future.  With few exceptions, they are not in front of the cameras because they possess any proven skill at forecasting.  Accuracy is seldom even mentioned.  Old forecasts are like old news””soon forgotten””and pundits are almost never asked to reconcile what they said with what actually happened.  The one undeniable talent that talking heads have is their skill at telling a compelling story with conviction, and that is enough.  Many have become wealthy peddling forecasting of untested value to corporate executives, government officials, and ordinary people who would never think of swallowing medicine of unknown efficacy and safety but who routinely pay for forecasts that are as dubious as elixirs sold from the back of a wagon.

Tetlock is optimistic about the ability of people to learn to be superforecasters.  But he’s also a “skeptic” when it comes to how precisely and how far into the future people can predict.  Tetlock explains:

In a world where a butterfly in Brazil can make the difference between just another sunny day in Texas and a tornado tearing through a town, it’s misguided to think anyone can see very far into the future.

Tetlock on why he’s optimistic:

We know that in so much of what people want to predict””politics, economics, finance, business, technology, daily life””predictability exists, to some degree, in some circumstances.  But there is so much else we do not know.  For scientists, not knowing is exciting.  It’s an opportunity to discover; the more that is unknown, the greater the opportunity.  Thanks to the frankly quite amazing lack of rigor in so many forecasting domains, this opportunity is huge.  And to seize it, all we have to do is set a clear goal””accuracy!””and get serious about measuring.

Tetlock and his research (and life) partner Barbara Mellers launched the Good Judgment Project (GJP) in 2011.  Tetlock:

Cumulatively, more than twenty thousand intellectually curious laypeople tried to figure out if protests in Russia would spread, the price of gold would plummet, the Nikkei would close above 9,500, war would erupt on the Korean peninsula, and many other questions about complex, challenging global issues.  By varying the experimental conditions, we could gauge which factors improved foresight, by how much, over which time frames, and how good forecasts could become if best practices were layered on each other.

The GJP was part of a much larger research effort sponsored by the Intelligence Advanced Research Projects Activity (IARPA).

IARPA is an agency within the intelligence community that reports to the director of National Intelligence and its job is to support daring research that promises to make American intelligence better at what it does.  And a big part of what American intelligence does is forecast global political and economic trends.

(IARPA logo via Wikimedia Commons)

Tetlock continues:

…IARPA created a forecasting tournament in which five scientific teams led by top researchers in the field would compete to generate accurate forecasts on the sorts of tough questions intelligence analysts deal with every day.  The Good Judgment Project was one of those five teams… By requiring teams to forecast the same questions at the same time, the tournament created a level playing field””and a rich treasure trove of data about what works, how well, and when.  Over four years, IARPA posed nearly five hundred questions about world affairs… In all, we gathered over one million individual judgments about the future.

In year 1, GJP beat the official control group by 60%.  In year 2, we beat the control group by 78%.  GJP also beat its university-affiliated competitors, including the University of Michigan and MIT, by hefty margins, from 30% to 70%, and even outperformed professional intelligence analysts with access to classified data.  After two years, GJP was doing so much better than its academic competitors that IARPA dropped the other teams.

What Tetlock learned was two key things:  some people clearly can predict certain events; and the habits of thought of these forecasters can be learned and cultivated “by any intelligent, thoughtful, determined person.”

There’s a question about whether computers can be trained to outpredict superforecasters.  Probably not for some time.  It’s more likely, says Tetlock, the superforecasters working with computers will outperform computers alone and superforecasters alone.  Think of freestyle chess where chess experts using computers are often stronger than computers alone and than human experts alone.

 

ILLUSIONS OF KNOWLEDGE

Archie Cochrane was supposed to die from cancer.  A specialist had done surgery to remove a lump.  During the surgery, it appeared that the cancer had spread.  So the specialist removed the pectoralis minor during the surgery.  He then told Archie Cochrane that he didn’t have long to live.  However, when a pathologist later examined the tissue that had been removed during the surgery, he learned that Cochrane didn’t have cancer at all.  This was a good thing because Archie Cochrane eventually became a revered figure in medicine.  Tetlock comments:

We have all been too quick to make up our minds and too slow to change them.  And if we don’t examine how we make these mistakes, we will keep making them.  This stagnation can go on for years.  Or a lifetime.  It can even last centuries, as the long and wretched history of medicine illustrates.

(Photograph from 1898, via Wikimedia Commons)

Tetlock continues:

When George Washington fell ill in 1799, his esteemed physicians bled  him relentlessly, doused him with mercury to cause diarrhea, induced vomiting, and raised blood-filled blisters by applying hot cups to the old man’s skin.  A physician in Aristotle’s Athens, or Nero’s Rome, or medieval Paris, or Elizabethan London would have nodded at much of that hideous regime.

Washington died… It’s possible that the treatments helped but not enough to overcome the disease that took Washington’s life, or that they didn’t help at all, or that the treatments even hastened Washington’s death.  It’s impossible to know which of these conclusions is true merely by observing that one outcome.  Even with many such observations, the truth can be difficult or impossible to tease out.  There are just too many factors involved, too many possible explanations, too many unknowns.  And if physicians are already inclined to think the treatments work””which they are, or they wouldn’t prescribe them””all that ambiguity is likely to be read in favor of the happy conclusion that the treatments really are effective.

Rigorous experimentation was needed.  But that was never done.  Tetlock brings up the example of Galen, the second-century physician to Roman emperors.  Galen’s writings were “the indisputable source of medical authority for more than a thousand years.”  But Galen never did any experiments.  Galen was a tad overconfident, writing: “All who drink of this treatment recover in a short time, except those whom it does not help, who all die.  It is obvious, therefore, that it fails only in incurable cases.”  Tetlock comments:

Galen was an extreme example but he is the sort of figure who pops up repeatedly in the history of medicine.  They are men (always men) of strong conviction and profound trust in their own judgment.  They embrace treatments, develop bold theories for why they work, denounce rivals as quacks and charlatans, and spread their insights with evangelical passion.

Tetlock again:

Not until the twentieth century did the idea of randomized trial experiments, careful measurement, and statistical power take hold… Randomly assigning people to one group or the other would mean whatever differences there are among them should balance out if enough people participated in the experiment.  Then we can confidently conclude that the treatment caused any differences in observed outcomes.  It isn’t perfect.  There is no perfection in our messy world.  But it beats wise men stroking their chins.

The first serious trials were attempted only after World War II.

But still the physicians and scientists who promoted the modernization of medicine routinely found that the medical establishment wasn’t interested, or was even hostile to their efforts.  “Too much that was being done in the name of health care lacked scientific validation,” Archie Cochrane complained about medicine in the 1950s and 1960s… Physicians and the institutions they controlled didn’t want to let go of the idea that their judgment alone revealed the truth, so they kept doing what they did because they had always done it that way””and they were backed up by respected authority.  They didn’t need scientific validation.  They just knew.  Cochrane despised this attitude.  He called it “the God complex.”

Tetlock describes the two systems we have in our brains: System 1 and System 2.

System 2 is the familiar realm of conscious thought.  It consists of everything we choose to focus on.  By contrast, System 1 is largely a stranger to us.  It is the realm of automatic perceptual and cognitive operations””like those you are running right now to transform the print on this page into a meaningful sentence or to hold the book while reaching for a glass and taking a sip.  We have no awareness of these rapid-fire processes but we could not function without them.  We would shut down.

The numbering of the two systems is not arbitrary.  System 1 comes first.  It is fast and constantly running in the background.  If a question is asked and you instantly know the answer, it sprang from System 1.  System 2 is charged with interrogating that answer.  Does it stand up to scrutiny?  Is it backed by evidence?  This process takes time and effort, which is why the standard routine in decision making is this: System 1 delivers an answer, and only then can System 2 get involved, starting with an examination of what System 1 decided.

Tetlock notes that in the Paleolithic world in which our brains evolved, System 1’s ability to make quick decisions helped us to survive.  A shadow in the grass was immediately assumed to be dangerous.  There was no time for System 2 to second guess.  If System 1 was functioning properly, we would already be running by the time we were consciously aware of the shadow in the grass.

Although System 2 can be trained to think rationally, mathematically, and in terms of statistics, both System 1 and System 2 naturally look first for evidence that confirms a given hypothesis.  The tendency to look for and see only confirming evidence, while disregarding potentially disconfirming evidence, is called confirmation bias.

Confirmation bias is one reason we tend to be overconfident.  Tetlock quotes Daniel Kahneman:

“It is wise to take admissions of uncertainty seriously, but declarations of high confidence mainly tell you that an individual has constructed a coherent story in his mind, not necessarily that the story is true.”

One thing that System 1 does if it faces a question is to substitute an easier question and answer that.  Tetlock calls this bait and switch.  Tetlock gives an example.  The first question is, “Should I worry about the shadow in the long grass?”  System 1 automatically looks for an easier question, like, “Can I easily recall a lion attacking someone from the long grass?”  If the answer to the easier question is “yes,” then the answer to the original question is also “yes.”  Tetlock calls our reliance on the automatic operations of System 1 tip-of-your-nose perspective.

That said, there are areas in life where a human can develop expertise to the point where System 1 intuition can be trusted.  Examples include fire fighting and chess.  Tetlock:

It’s pattern recognition.  With training or experience, people can encode patterns deep in their memories in vast number and intricate detail””such as the estimated fifty thousand to one hundred thousand chess positions that top players have in their repertoire.  If something doesn’t fit a pattern””like a kitchen fire giving off more heat than a kitchen fire should””a competent expert senses it immediately.

Note that developing such expertise requires working in a world with valid cues.  Valid cues exist in the world of firefighting and chess, but much less so in the world of stocks, for example.  If there’s a lot of randomness and it takes a long time to get valid feedback from decisions””which is the case in the world of stocks””then it can take much longer to develop true expertise.

 

KEEPING SCORE

In order to tell whether someone is able to make forecasts with some accuracy, the forecasts must be specific enough to be measured.  Also, by measuring forecasts, it becomes possible for people to improve.

Tetlock writes:

In 1984, with grants from the Carnegie and MacArthur foundations, the National Research Council””the research arm of the United States National Academy of Sciences””convened a distinguished panel charged with nothing less than “preventing nuclear war.”…

The panel did its due diligence.  It invited a range of experts””intelligence analysts, military officers, government officials, arms control experts, and Sovietologists””to discuss the issues.  They… were an impressive bunch.  Deeply informed, intelligent, articulate.  And pretty confident that they knew what was happening and where we were heading.

Both liberals and conservatives agreed that the next Soviet leader would be a Communist Party man.  Both liberals and conservative were confident of their views.

But then something unexpected happened.  The Politburo appointed Mikhail Gorbachev as the next general secretary of the Communist Party of the Soviet Union.  Tetlock:

Gorbachev changed direction swiftly and abruptly.  His policies of glasnost (openness) and perestroika (restructuring) liberalized the Soviet Union.  Gorbachev also sought to normalize relations with the United States and reverse the arms race…

Few experts saw this coming.  And yet it wasn’t long before most of those who didn’t see it coming grew convinced that they knew exactly why it had happened, and what was coming next.

Tetlock comments:

My inner cynic started to suspect that no matter what had happened the experts would have been just as adept at downplaying their predictive failures and sketching an arc of history that made it appear that they saw it coming all along.  After all, the world had just witnessed a huge surprise involving one of the most consequential matters imaginable.  If this didn’t induce a shiver of doubt, what would?  I was not questioning the intelligence or integrity of these experts, many of whom had won big scientific prizes or held high government offices… But intelligence and integrity are not enough.  The national security elites looked a lot like the renowned physicians from the prescientific era… But tip-of-your-nose delusions can fool anyone, even the best and the brightest””perhaps especially the best and the brightest.

In order for forecasts to be measurable, they have to have a specific time frame and what is expected to happen must be clearly defined.  Then there’s the issue of probability.  It’s easy to judge a forecast that something will definitely happen by some specific time.  Jonathan Schell predicted, in his influential book The Fate of the Earth, that a nuclear war would happen by 1985.  Since that didn’t happen, obviously Schell’s prediction was wrong.  But Tetlock asks, what if Schell had said that a nuclear war was “very likely”?  Tetlock:

The only way to settle this definitively would be to rerun history hundreds of times, and if civilization ends in piles of irradiated rubble in most of those reruns, we would know Schell was right.  But we can’t do that, so we can’t know.

Furthermore, phrases such as “very likely” are a problem.  Consider the experience of Sherman Kent.

In intelligence circles, Sherman Kent is a legend.  With a PhD in history, Kent left a faculty position at Yale to join the Research and Analysis Branch of the newly created Coordinator of Information (COI) in 1941.  The COI became the Office of Strategic Services (OSS).  The OSS became the Central Intelligence Agency (CIA).  By the time Kent retired from the CIA in 1967, he had profoundly shaped how the American intelligence community does what it calls intelligence analysis””the methodological examination of the information collected by spies and surveillance to figure out what it means, and what will happen next.

“¦forecasting is all about estimating the likelihood of something happening, which Kent and his colleagues did for many years at the Office of National Estimates””an obscure but extraordinarily influential bureau whose job was to draw on all information available to the CIA, synthesize it, and forecast anything and everything that might help top officeholders in the US government decide what to do next.

The stakes were high, and Kent weighted each word carefully.  Nonetheless, there was some confusion because they used phrases like “very likely” and “a serious possibility” instead of assigning precise probabilities.  When Kent asked each team member what they had in mind as a specific probability, it turned out that each person had a different probability in mind even though they had all agreed on “a serious possibility.”  The opinions ranged from 20 percent to 80 percent.  Kent grew concerned.

Kent was right to worry.  In 1961, when the CIA was planning to topple the Castro government by landing a small army of Cuban expatriates at the Bay of Pigs, President John F. Kennedy turned to the military for an unbiased assessment.  The Joint Chiefs of Staff concluded that the plan had a “fair chance” of success.  The man who wrote the words “fair chance” later said he had in mind odds of 3 to 1 against success.  But Kennedy was never told precisely what “fair chance” meant and, not unreasonably, he took it to be a much more positive assessment.  Of course we can’t be sure that if the Chiefs had said “We feel it’s 3 to 1 the invasion will fail” that Kennedy would have called it off, but it surely would have made him think harder about authorizing what turned out to be an unmitigated disaster.

In order to solve this problem, Kent suggested specific probabilities be associated with specific phrases.  For instance, “almost certain” would mean a 93% chance, plus or minus 6%, while “probable” would mean a 75% chance, plus or minus 12%.

Unfortunately, Kent’s scheme was never adopted.  Some felt specific probabilities were unnatural.  Others thought it made you sound like a bookie.  (Kent’s famous response: “I’d rather be a bookie than a goddamn poet.”)  Still others objected that specific probabilities seemed too much like objective facts instead of subjective judgments, which they were.  The answer to that objection was simply to make it understood that the estimates were just guesses””opinions””and nothing more.

Tetlock observes that there’s a fundamental obstacle to adopting specific probabilities.  The obstacle has to do with what Tetlock calls the wrong-side-of-maybe fallacy.

If a meteorologist says there’s a 70% chance of rain, is she wrong if it doesn’t rain?  Not necessarily.  To see how right or wrong the estimate is, we would have to rerun the day a hundred times.  If it rained in 70% of the reruns, then the meteorologist would have been exactly right with her 70% estimate.  Unfortunately, however, that’s not how people judge it.  If the meteorologist estimates a 70% chance of rain, then if it rains, she was right, but if it doesn’t rain, she was wrong, according to how most people think about it.  Tetlock writes:

The prevalence of this elementary error has a terrible consequence.  Consider that if an intelligence agency says there is a 65% chance that an event will happen, it risks being pilloried if it does not””and because the forecast itself says there is a 35% chance it will not happen, that’s a big risk.  So what’s the safe thing to do?  Stick with elastic language”¦ If the event happens, “a fair chance” can retroactively be stretched to mean something considerably bigger than 50%””so the forecaster nailed it.  If it doesn’t happen, it can be shrunk to something much smaller than 50%””and again the forecaster nailed it.  With perverse incentives like these, it’s no wonder people prefer rubbery words over firm numbers.

Tetlock observes that it wasn’t until after the debacle regarding Saddam Hussein’s purported weapons of mass destruction, and the reforms that followed, that expressing probabilities with numbers became more accepted.  But that’s within the intelligence community.  In the broader community, especially in the media, very vague language is still common.  Tetlock:

If we are serious about measuring and improving, this won’t do.  Forecasts must have clearly defined terms and timelines.  They must use numbers.  And one more thing is essential: we must have lots of forecasts.

Consider the meteorologist again.  If she makes a new forecast each day, then over time her track record can be determined.  If she’s perfect, then 70% of the time she says there’s a 70% chance of rain, it rains, and so forth.  That would be perfect calibration.  If it only rains 40% of the time she says there’s a 70% chance of rain, then she’s overconfident.  If it rains 80% of the time she says there’s a 30% chance of rain, then she’s underconfident.

Of course, when you consider something like presidential elections, which happen every four years, it could take a long time to build about enough predictions for testing purposes.  And some events are even rarer than every four years.

Besides calibration, there is also “resolution.”  If someone assigns very high probabilities””80% to 100%””to things that happen, and very low probabilities””0% to 20%””to things that don’t happen, then they have good resolution.

The math behind this system was developed by Glenn W. Brier in 1950, hence results are called Brier scores.  In effect, Brier scores measure the distance between what you forecast and what actually happened”¦ Perfection is 0.  A hedged fifty-fifty call, or random guessing in the aggregate, will produce a Brier score of 0.5.  A forecast that is wrong to the greatest possible extent””saying there is a 100% chance that something will happen and it doesn’t, every time””scores a disastrous 2.0, as far from The Truth as it is possible to get.

Tetlock writes about the 20 years he spent gathering roughly 28,000 predictions by 284 experts.  This was the Expert Political Judgment (EPJ) project.  Tetlock:

If you didn’t know the punch line of EPJ before you read this book, you do now: the average expert was roughly as accurate as a dart-throwing chimpanzee.

Tetlock then notes that there were two statistically distinguishable groups.  One group failed to do better than random guessing.  The second group beat the chimp, but not by much.

So why did one group do better than the other?  It wasn’t whether they had PhDs or access to classified information.  Nor was it what they thought“”whether they were liberals or conservatives, optimists or pessimists.  The critical factor was how they thought.

Tetlock explains:

One group tended to organize their thinking around Big Ideas, although they didn’t agree on which Big Ideas were true or false”¦ As ideologically diverse as they were, they were united by the fact that their thinking was so ideological.  They sought to squeeze complex problems into the preferred cause-effect templates and treated what did not fit as irrelevant distractions.  Allergic to wishy-washy answers, they kept pushing their analyses to the limit (and then some), using terms like “furthermore” and “moreover” while piling up reasons why they were right and others wrong.  As a result, they were unusually confident and likelier to declare things “impossible” or “certain.”  Committed to their conclusions, they were reluctant to change their minds even when their predictions clearly failed.  They would tell us, “Just wait.”

The other group consisted of more pragmatic experts who drew on many analytical tools, with the choice of tool hinging on the particular problem they faced.  These experts gathered as much information from as many sources as they could.  When thinking, they often shifted mental gears, sprinkling their speech with transition markers such as “however,” “but,” “although,” and “on the other hand.”  They talked about possibilities and probabilities, not certainties.  And while no one likes to say “I was wrong,” these experts more readily admitted it and changed their minds.

The first group above are called hedgehogs, while the second group are called foxes.  Hedgehogs know one big thing, while foxes know many things.  Foxes beat hedgehogs on both calibration and resolution.  Moreover, hedgehogs actually did slightly worse than random guessing.

Tetlock compares the hedgehog’s Big Idea to a pair of green-tinted glasses that he never takes off.

So the hedgehog’s one Big Idea doesn’t improve his foresight.  It distorts it.  And more information doesn’t help because it’s all seen through the same tinted glasses.  It may increase the hedgehog’s confidence, but not his accuracy.  That’s a bad combination.  The predictable result?  When hedgehogs in the EPJ research made forecasts on the subjects they knew the most about””their own specialties””their accuracy declined.

Perhaps not surprisingly, the more famous an expert in EPJ was, the less accurate.

That’s not because editors, producers, and the public go looking for bad forecasters.  They go looking for hedgehogs, who just happen to be bad forecasters.  Animated by a Big Idea, hedgehogs tell tight, simple, clear stories that grab and hold audiences… Better still, hedgehogs are confident… The simplicity and confidence of the hedgehog impairs foresight, but it calms nerves””which is good for the careers of hedgehogs.

What about foxes in the media?

Foxes don’t fare so well in the media.  They’re less confident, less likely to say something is “certain” or “impossible,” and are likelier to settle on shades of “maybe.”  And their stories are complex, full of “howevers” and “on the other hands,” because they look at problems one way, then another, and another.  This aggregation of many perspectives is bad TV.  But it’s good forecasting.  Indeed, it’s essential.

 

SUPERFORECASTERS

Tetlock writes:

After invading in 2003, the United States turned Iraq upside down looking for WMDs but found nothing.  It was one of the worst””arguably the worst””intelligence failure in modern history.

The question, however, is not whether the Intelligence Community’s conclusion was correct, but whether it was reasonable on the basis of known information.  Was it reasonable?  Yes.  It sure looked like Saddam was hiding something.  Else why play hide-and-seek with UN arms inspectors that risks triggering an invasion and your own downfall?

It’s difficult to evaluate whether the conclusion was reasonable because, looking back, we know that it was wrong.

This particular bait and switch””replacing “Was it a good decision?” with “Did it have a good outcome?”””is both popular and pernicious.

(Illustration by Alain Lacroix)

Think of it in terms of poker.  A beginner may overestimate his odds, bet big, get lucky and win.  But that doesn’t mean the bet was a good decision.  Similarly, a good poker pro may correctly estimate her odds, bet big, get unlucky and lose.  But that doesn’t mean the bet was a bad decision.

In this case, the evidence seems to show that the conclusion of the Intelligence Community (IC) regarding Iraq’s WMDs was reasonable on the basis of known information, even though it turned out later to be wrong.

However, even though the IC’s conclusion was reasonable, it could have been better because it would have expressed less certainty had all the information been carefully considered.  In other words, the IC would have reached the same conclusion, but it wouldn’t have been associated with a probability so close to 100%.  Tetlock:

The congressional resolution authorizing the use of force might not have passed and the United States might not have invaded.  Stakes rarely get much higher than thousands of lives and trillions of dollars.

The IC didn’t even admit the possibility that they could be wrong about Iraq’s WMDs.  Normally, if there was any doubt at all, you’d have some analysts required to present an opposing view.  But nothing like that happened because the IC was so certain of its conclusion.

In 2006 the Intelligence Advanced Research Projects Activity (IARPA) was created.  Its mission is to fund cutting-edge research with the potential to make the intelligence community smarter and more effective”¦

In 2008 the Office of the Director of National Intelligence””which sits atop the entire network of sixteen intelligence agencies””asked the National Research Council to form a committee.  The task was to synthesize research on good judgment and help the IC put that research to good use.  By Washington’s standards, it was a bold (or rash) thing to do.  It’s not every day that a bureaucracy pays one of the world’s most respected scientific institutions to produce an objective report that might conclude that the bureaucracy is clueless.

The report delivered was delivered two years later.

“The IC should not rely on analytical methods that violate well-documented behavioral principles or that have no evidence of efficacy beyond their intuitive appeal,” the report noted.  The IC should “rigorously test current and proposed methods under conditions that are as realistic as possible.  Such an evidence-based approach to analysis will promote the continuous learning need to keep the IC smarter and more agile than the nation’s adversaries.”

The IC does a good job teaching its analysts the correct process for doing research and reaching judgments.  The IC also does a good job holding analysts accountable for following the correct process.  However, the IC doesn’t hold analysts accountable for the accuracy of their judgments.  There’s no systematic tracking of the accuracy of judgments.  That’s the biggest problem.  IARPA decided to do something about this.

IARPA would sponsor a massive tournament to see who could invent the best methods of making the sorts of forecasts that intelligence analysts make every day.  Will the president of Tunisia flee to a cushy exile in the next month?  Will an outbreak of H5N1 in China kill more than ten in the next six months?  Will the euro fall below $1.20 in the next twelve months?

[“¦]

The research teams would compete against one another and an independent control group.  Teams had to beat the combined forecast””the “wisdom of the crowd”””of the control group, and by margins we all saw as intimidating.  In the first year, IARPA wanted teams to beat that standard by 20%””and it wanted that margin of victory to grow to 50% by the fourth year.

But that was only part of IARPA’s plan.  Within each team, researchers could run Archie Cochrane-style experiments to assess what really works against internal control groups.  Researchers might think, for example, that giving forecasters a basic training exercise would improve their accuracy”¦ Give the training to one randomly chosen group of forecasters but not another.  Keep all else constant.  Compare results.

Tetlock put together a team of 3,200 forecasters.  His team was called the Good Judgment Project (GJP).  Tetlock called IARPA’s tournament “gutsy” because of what it could reveal.

Here’s one possible revelation: Imagine you get a couple of hundred ordinary people to forecast geopolitical events.  You see how often they revise their forecasts and how accurate those forecasts prove to be and use that information to identify the forty or so who are the best.  Then you have everyone makes lots more forecasts.  This time, you calculate the average forecast of the whole group”””the wisdom of the crowd”””but with extra weight given to those forty top forecasters.  Then you give the forecast a final tweak: You “extremize” it, meaning you push it closer to 100% or zero.  If the forecast is 70% you might bump it up to, say, 85%.  If it’s 30%, you might reduce it to 15%.

Now imagine that the forecasts you produce this way beat those of every other group and method available, often by large margins.  Your forecasts even beat those of professional intelligence analysts inside the government who have access to classified information””by margins that remain classified.

Think how shocking it would be to the intelligence professionals who have spent their lives forecasting geopolitical events””to be beaten by a few hundred ordinary people and some simple algorithms.

It actually happened.  What I’ve described is the method we used to win IARPA’s tournament.  There is nothing dazzlingly innovative about it.  Even the extremizing tweak is based on a pretty simple insight: When you combine the judgments of a large group of people to calculate the “wisdom of the crowd” you collect all the relevant information that is dispersed among all those people.  But none of those people has access to all that information”¦ What would happen if every one of those people were given all the information?  They would become more confident””raising their forecasts closer to 100% or zero.  If you then calculated the “wisdom of the crowd” it too would be more extreme.  Of course it’s impossible to give every person all the relevant information””so we extremize to simulate what would happen if we could.

Tetlock continues:

Thanks to IARPA, we now know a few hundred ordinary people and some simple math can not only compete with professionals supported by a multibillion-dollar apparatus but also beat them.

And that’s just one of the unsettling revelations IARPA’s decision made possible.  What if the tournament discovered ordinary people who could””without the assistance of any algorithmic magic””beat the IC?  Imagine how threatening that would be.

Tetlock introduces one of the best forecasters on GJP: Doug Lorch, a retired computer programmer who “doesn’t look like a threat to anyone.”  Out of intellectual curiosity, Lorch joined GJP.

Note that in the IARPA tournament, a forecaster could update her forecast in real time.  She may have thought there was a 60% chance some event would happen by the six-month deadline, and then read something convincing her to update her forecast to 75%.  For scoring purposes, each update counts as a separate forecast.  Tetlock:

Over four years, nearly five hundred questions about international affairs were asked of thousands of GJP’s forecasters, generating well over one million judgments about the future.  But even at the individual level, the numbers quickly added up.  In year 1 alone, Doug Lorch made roughly one thousand separate forecasts.

Doug’s accuracy was as impressive as his volume.  At the end of the first year, Doug’s overall Brier score was 0.22, putting him in the fifth spot among the 2,800 competitors in the Good Judgment Project”¦

In year 2, Doug joined a superforecaster team and did even better, with a final Brier score of 0.14, making him the best forecaster of the 2,800 GJP volunteers.  He also beat by 40% a prediction market in which traders bought and sold futures contracts on the outcomes of the same questions.  He was the only person to beat the extremizing algorithm.  And Doug not only beat the control group’s “wisdom of the crowd,” he surpassed it by more than 60%, meaning that he single-handedly exceeded the fourth-year performance target that IARPA set for multimillion-dollar research programs that were free to use every trick in the forecasting textbook for improving accuracy.

Tetlock points out that Doug Lorch was not uniquely gifted:

There were 58 others among the 2,800 volunteers who scored at the top of the charts in year 1.  They were our first class of superforecasters.  At the end of year 1, their collective Brier score was 0.25, compared with 0.37 for all the other forecasters””and that gap grew in later years so that by the end of the four-year tournament, superforecasters had outperformed regulars by 60%.  Another gauge of how good superforecasters were is how much further they could see into the future.  Across all four years of the tournament, superforecasters looking out three hundred days were more accurate than regular forecasters looking out one hundred days.  In other words, regular forecasters needed to triple their foresight to see as far as superforecasters.

What if the superforecasters’ performance was due to luck?  After all, if you start with 2,800 people flipping coins, some of those people by sheer luck will flip a high proportion of heads.  If luck was a significant factor, then we should expect regression to the mean: the superforecasters in year 1 should perform less well, on the whole, in year 2.  But that didn’t happen.  That doesn’t mean luck isn’t a factor, because it is a factor in many of the questions that were asked.  Tetlock writes:

So we have a mystery.  If chance is playing a significant role, why aren’t we observing significant regression of superforecasters as a whole toward the overall mean?  An offsetting process must be pushing up superforecasters’ performance numbers.  And it’s not hard to guess what that was: after year 1, when the first cohort of superforecasters was identified, we congratulated them, anointed them “super,” and put them on teams with fellow superforecasters.  Instead of regressing toward the mean, their scores got even better.  This suggests that being recognized as “super” and placed on teams of intellectually stimulating colleagues improved their performance enough to erase regression to the mean we would otherwise have seen.  In years 3 and 4, we harvested fresh crops of superforecasters and put them to work in elite teams.  That gave us more apples-to-apples comparisons.  The next cohorts continued to do as well or better than they did in the previous year, again contrary to the regression hypothesis.

That’s not to say there was no regression to the mean.  Each year, roughly 30% of the individual superforecasters fall from the top 2% the next year.  This confirms that luck is a factor.  As Tetlock points out, even superstar athletes occasionally look less than stellar.

 

SUPERSMART?

Tetlock introduces Sanford “Sandy” Sillman.  In 2008, he was diagnosed with multiple sclerosis, which was debilitating.  Walking was difficult.  Even typing was a challenge.  Sandy had to retire from his job as an atmospheric scientist.

How smart is Sandy?  He earned a double major in math and physics from Brown University, plus a master of science degree from MIT’s technology and policy program, along with a second master’s degree, in applied mathematics, from Harvard, and finally a PhD in applied physics from Harvard.  Furthermore, Sandy’s intelligence isn’t confined to math and physics.  He is fluent in French, Russian, Italian, and Spanish.

In year 1 of GJP, Sandy finished with an overall Brier score of 0.19, which put him in a tie for overall champion.

There’s an obvious question about whether superforecasters are simply more knowledgeable and intelligent than others.  Tetlock and Barbara Mellers tested forecasters.  It turns out that although superforecasters have well above average intelligence, they did not score off-the-charts high and most fall well short of so-called genius territory, which if often defined as the top 1%, or an IQ of 135 and up.

Tetlock concludes that knowledge and intelligence help, but they add little beyond a certain threshold.

But having the requisite knowledge and intelligence is not enough.  Many clever and informed forecasters in the tournament fell far short of superforecaster accuracy.  And history is replete with brilliant people who made forecasts that proved considerably less than prescient.

For someone to become a superforecaster, she must develop the right habits of thinking.   Tetlock gives an example.

On October 12, 2004, Yasser Arafat became severely ill with vomiting and abdominal pain.  On November 11, 2004, Arafat was pronounced dead.  There was speculation that he had been poisoned.  In July 2012, scientists at Switzerland’s Lausanne University Institute of Radiation Physics announced that they had tested some of Arafat’s belonging and found high levels of Polonium-210, a radioactive element that can be deadly if ingested.

Two separate agencies, one in France and one in Switzerland, decided to test Arafat’s body.  So IARPA asked forecasters the following question: “Will either the French or Swiss inquiries find elevated levels of polonium in the remains of Yasser Arafat’s body?”

How would someone answer this question?  Most people would follow their hunch about the matter.  Some people might feel that “Israel would never do that!”, while others might feel that “Of course Israel did it!”  Following your hunch is not the right way to approach the question.  How did the superforecaster Bill Flack answer the question?

“¦Bill asked himself how Arafat’s remains could have been contaminated with enough polonium to trigger a positive result.  Obviously, “Israel poisoned Arafat” was one way.  But because Bill carefully broke the question down, he realized there were others.  Arafat had many Palestinian enemies.  They could have poisoned him.  It was also possible that there had been “intentional postmortem contamination by some Palestinian faction looking to give the appearance that Israel had done a Litvinenko on Arafat,” Bill told me later.  These alternatives mattered because each additional way Arafat’s body could have been contaminated with polonium increased the probability that it was.

What’s the next step?  Before getting to this, Tetlock describes in detail an American family, the Renzettis, who have one child and asks how likely it is that they have a pet.  The first step in answering that question is to learn what percentage of American households have a pet.

Statisticians call that the best rate””how common something is within a broader class.  Daniel Kahneman has a much more evocative visual term for it.  He calls it the “outside view”””in contrast to the “inside view,” which is the specifics of the particular case.  A few minutes with Google tells me about 62% of American households own pets.  That’s the outside view here.  Starting with the outside view means I will start by estimating that there is a 62% chance the Renzettis have a pet.  Then I will turn to the inside view””all those details about the Renzettis””and use them to adjust that initial 62% up or down.

Tetlock comments:

It’s natural to be drawn to the inside view.  It’s usually concrete and filled with engaging detail we can use to craft a story about what’s going on.  The outside view is typically abstract, bare, and doesn’t lend itself so readily to storytelling.  So even smart, accomplished people routinely fail to consider the outside view.

Tetlock writes:

Here we have a famous person who is dead.  Major investigative bodies think there is enough reason for suspicion that they are exhuming the body.  Under those circumstances, how often would the investigation turn up evidence of poisoning?  I don’t know and there is no way to find out.  But I do know there is at least a prima facie case that persuades courts and medical investigators that this is worth looking into.  It has to be considerably above zero.  So let’s say it’s at least 20%.  But the probability can’t be 100% because if it were that clear and certain the evidence would have been uncovered before burial.  So let’s so the probability cannot be higher than 80%.  That’s a big range.  The midpoint is 50%.  So that outside view can serve as our starting point.

Someone might wonder why you couldn’t start with the inside view and then consider the outside view.  This wouldn’t work, however, due to anchoring.  When we make estimates, we tend to start with some number and adjust.  If we start with the outside view, we have reasonable starting point, but if we start with the inside view, we may end up anchoring on a number that is not a reasonable starting point.

Having established the outside view on the Arafat question, we next turn to the inside view.  What are the hypotheses?  Israel could have poisoned Arafat.  Arafat’s Palestinian enemies could have poisoned him.  Arafat’s remains could have been contaminated to make it look like he was poisoned.  Tetlock:

Start with the first hypothesis: Israel poisoned Yassar Arafat with polonium.  What would it take for that to be true?

• • Israel had, or could obtain, polonium.
  • Israel wanted Arafat dead badly enough to take a big risk.
  • Israel had the ability to poison Arafar with polonium.

Each of these elements could then be researched””looking for evidence pro and con””to get a sense of how likely they are to be true, and therefore how likely the hypothesis is to be true.  Then it’s on to the next hypothesis.  And the next.

This sounds like detective work because it is””or to be precise, it is detective work as real investigators do it, not the detectives on TV shows.  It’s methodical, slow, and demanding.

Tetlock concludes:

A brilliant puzzle solver may have the raw material for forecasting, but if he doesn’t also have an appetite for questioning basic, emotionally charged beliefs he will often be at a disadvantage relative to a less intelligent person who has a greater capacity for self-critical thinking.  It’s not the raw crunching power you have that matters most.  It’s what you do with it.

[“¦]

For superforecasters, beliefs are hypotheses to be tested, not treasures to be guarded.

 

SUPERQUANTS?

Perhaps superforecasters are exceptionally good using math to make their forecasts?  Although occasionally a superforecaster will consult a mathematical model, the vast majority of the time a superforecaster uses very little math.

That said, superforecasters tend to be granular in their probability estimates.  Is this justified?  Tetlock:

So how can we know that the granularity we see among superforecasters is meaningful?… The answer lies in the tournament data.  Barbara Mellers has shown that granularity predicts accuracy: the average forecaster who sticks with the tens””20%, 30%, 40%””is less accurate than the finer-grained forecaster who uses fives””20%, 25%, 30%””and still less accurate than the even finer-grained forecaster who uses ones””20%, 21%, 22%.

 

SUPERNEWSJUNKIES?

Tetlock:

Superforecasting isn’t a paint-by-numbers method but superforecasters often tackle questions in a roughly similar way””one that any of us can follow: Unpack the question into components.  Distinguish as sharply as you can between the known and unknown and leave no assumptions unscrutinized.  Adopt the outside view and put the problem into a comparative perspective that downplays its uniqueness and treats it as a special case of a wider class of phenomena.  Then adopt the inside view that plays up the uniqueness of the problem.  Also explore the similarities and differences between your views and those of others””and pay special attention to prediction markets and other methods of extracting wisdom from crowds.  Synthesize all these different views into a single vision as acute as that of a dragonfly.  Finally, express your judgment as precisely as you can, using a finely grained scale of probability.

This is just the beginning.  The next step, which is typically repeated many times, is to update your predictions as you get new information.

Superforecasters update their forecasts much more frequently than regular forecasters.  One might think that superforecasters are super because they are news junkies.  However, their initial forecasts were at least 50% more accurate than those of regular forecasters.  Furthermore, properly updating forecasts on the basis of new information requires the same skills used in making the initial forecasts.   Tetlock:

“¦there are two dangers a forecaster faces after making the initial call.  One is not giving enough weight to new information.  That’s underreaction.  The other danger is overreacting to new information, seeing it as more meaningful than it is, and adjusting a forecast too radically.

Both under- and overreaction can diminish accuracy.  Both can also, in extreme cases, destroy a perfectly good forecast.

One typical reason for underreaction to new information is that people often become committed to their beliefs.  Especially when they have publicly committed to their beliefs and when the have an ego investment.  Superforecasters, by contrast, have no trouble changing their minds on the basis of new information.

While superforecasters update their forecasts much more often than regular forecasters, they do so in small increments.  They tend not to overreact.

 

PERPETUAL BETA

Superforecasters have a growth mindset.  They believe they can get better with work, and they do.

(Illustration by Tereza Paskova)

Tetlock notes that John Maynard Keynes failed twice as an investor, and was almost wiped out during the first failure, before he settled on a value investing approach.  Keynes came to understand value investing through his own reflection.  He didn’t learn about it from Ben Graham, who is regarded as the father of value investing.  In any case, Keynes turned out to be enormously successful as an investor despite investing during the Great Depression of the 1930s.

Keynes had a growth mindset: try, fail, analyze, adjust, try again.

Improving requires getting good feedback.  Meteorologists get feedback on whether their forecasts were correct on a daily basis.  Bridge players also get fairly immediate feedback on how well they’re playing.  The trouble for forecasters is twofold:  first, the forecasts must be specific enough to be testable; and second, the time lag between the forecast and the result is often long, unlike for meteorologists or bridge players.

Pulling It All Together””Portrait of a Superforecaster

In philosophic outlook, they tend to be:

• • Cautious: Nothing is certain.
  • Humble: Reality is infinitely complex.
  • Nondeterministic: What happens is not meant to be and does not have to happen.

In their abilities and thinking styles, they tend to be:

• • Actively Open-minded: Beliefs are hypotheses to be tested, not treasures to be protected.
  • Intelligent and Knowledgeable, with a “Need for Cognition”: Intellectually curious, enjoy puzzles and mental challenges.
  • Reflective: Introspective and self-critical.
  • Numerate: Comfortable with numbers.

In their methods of forecasting, they tend to be:

• • Pragmatic: Not wedded to any idea or agenda.
  • Analytical: Capable of stepping back from the tip-of-your-nose perspective and considering other views.
  • Dragonfly-eyed: Value diverse views and synthesize them into their own.
  • Probabilistic: Judge using many grades of maybe.
  • Thoughtful Updaters: When facts change, they change their minds.
  • Good Intuitive Psychologists: Aware of the value of checking thinking for cognitive and emotional biases.

In their work ethic, they tend to have:

• • A Growth Mindset: Believe it’s possible to get better.
  • Grit: Determined to keep at it however long it takes.

Tetlock concludes by noting that the strongest predictor of a forecaster becoming a superforecaster is perpetual beta, the degree to which one is committed to belief updating and self-improvement.  Perpetual beta is three times as powerful a predictor as its closet rival, intelligence.

 

SUPERTEAMS

(Photo by Chrisharvey)

Tetlock writes:

[Groups] let people share information and perspectives.  That’s good.  It helps make dragonfly eye work, and aggregation is critical to accuracy.  Of course aggregation can only do its magic when people form judgments independently, like the fairgoers guessing the weight of the ox.  The independence of judgments ensures that errors are more or less random, so they cancel each other out.  When people gather and discuss in a group, independence of thought and expression can be lost.  Maybe one person is a loudmouth who dominates the discussion, or a bully, or a superficially impressive talker, or someone with credentials that cow others into line.  In so many ways, a group can get people to abandon independent judgment and buy into errors.  When that happens, the mistakes will pile up, not cancel out.

The GJP randomly assigned several hundred forecasters to work alone and several hundred to work in teams.  At the end of the first year, the teams had been 23% more accurate than the individuals.  GJP kept experimenting.

The results speak for themselves.  On average, when a forecaster did well enough in year 1 to become a superforecaster, and was put on a superforecaster team in year 2, that person became 50% more accurate.  An analysis in year 3 got the same result.  Given that these were collections of strangers tenuously connected in cyberspace, we found that result startling.

Tetlock adds:

How did superteams do so well?  By avoiding the extremes of groupthink and Internet flame wars.  And by fostering minicultures that encouraged people to challenge each other respectfully, admit ignorance, and request help.

 

THE LEADER’S DILEMMA

Good leaders have to be confident and decisive.  But good leaders also need good forecasts in order to make good decisions.  We’ve seen that effective forecasting requires self-critical questioning and doubt.  How can a leader be both a forecaster and a leader?  Tetlock:

Fortunately, the contradiction between being a superforecaster and a superleader is more apparent than real.  In fact, the superforecaster model can help make good leaders superb and the organizations they lead smart, adaptable, and effective.  The key is an approach to leadership and organization first articulated by a nineteenth-century Prussian general, perfected by the German army of World War II, made foundational doctrine by the modern American military, and deployed by many successful corporations today.  You might even find it at your neighborhood Walmart.

Helmuth von Moltke was a famous nineteenth-century Prussian general.  One of Moltke’s axioms is: “In war, everything is uncertain.”  While having a plan is important, you can never entirely trust your plan.  Moltke: “No plan of operations extends with certainty beyond the first encounter with the enemy’s main strength.”  Moltke also wrote that, “It is impossible to lay down binding rules” that apply in all circumstances.  “Two cases will never be exactly the same” in war.  Improvisation is essential.  Tetlock:

Moltke trusted that his officers were up to the task”¦ In Germany’s war academies, scenarios were laid out and students were invited to suggest solutions and discuss them collectively.  Disagreement was not only permitted, it was expected, and even the instructor’s views could be challenged”¦ Even the views of generals were subject to scrutiny.

Tetlock continues:

What ties all of this together”¦ is the command principle of Auftragstaktik.  Usually translated today as “mission command,” the basic idea is simple. “War cannot be conducted from the green table,” Moltke wrote, using an expression that referred to top commanders at headquarters.  “Frequent and rapid decisions can be shaped only on the spot according to estimates of local conditions.”  Decision-making power must be pushed down the hierarchy so that those on the ground””the first to encounter surprises on the evolving battlefield””can respond quickly.  Of course those on the ground don’t see the bigger picture.  If they made strategic decisions the army would lose coherence and become a collection of tiny units, each seeking its own ends.  Auftragstaktik blended strategic coherence and decentralized decision making with a simple principle: commanders were to tell subordinates what their goal is but not how to achieve it.

 

ARE THEY REALLY SO SUPER?

Are the superforecasters super primarily due to innate abilities or primarily due to specific habits they’ve developed?   Tetlock answers:

They score higher than average on measures of intelligence and open-mindedness, although they are not off the charts.  What makes them so good is less what they are than what they do””the hard work of research, the careful thought and self-criticism, the gathering and synthesizing of other perspectives, the granular judgments and relentless updating.

Tetlock again:

My sense is that some superforecasters are so well practiced in System 2 corrections””such as stepping back to take the outside view””that these techniques have become habitual.  In effect, they are now part of their System 1″¦ No matter how physically or cognitively demanding a task may be””cooking, sailing, surgery, operatic singing, flying fighter jets””deliberative practice can make it second nature.

Very little is predictable five or more years into the future.  Tetlock confirmed this fact in his EPJ research: Expert predictions declined towards chance five years out.  And yet governments need to make plans that extend five or more years into the future.  Tetlock comments:

Probability judgments should be explicit so we can consider whether they are as accurate as they can be.  And if they are nothing but a guess, because that’s the best we can do, we should say so.  Knowing what we don’t know is better than thinking we know what we don’t.

Tetlock adds:

Kahneman and other pioneers of modern psychology have revealed that our minds crave certainty and when they don’t find it, they impose it.  In forecasting, hindsight bias is the cardinal sin.  Recall how experts stunned by the Gorbachev surprise quickly became convinced it was perfectly explicable, even predictable, although they hadn’t predicted it.

Brushing off surprises makes the past look more predictable than it was””and this encourages the belief that the future is much more predictable that it is.

Tetlock concludes:

Savoring how history could have generated an infinite array of alternative outcomes and could now generate a similar array of alternative futures, is like contemplating the one hundred billion known stars in our galaxy and the one hundred billion known galaxies.  It instills profound humility.

“¦But I also believe that humility should not obscure the fact that people can, with considerable effort, make accurate forecasts about at least some developments that really do matter.

 

WHAT’S NEXT?

Tetlock hopes that the forecasting lessons discussed in his book will be widely adopted:

Consumer of forecasts will stop being gulled by pundits with good stories and start asking pundits how their past predictions fared””and reject answers that consist of nothing but anecdotes and credentials.  Just as we now expect a pill to have been tested in peer-reviewed experiments before we swallow it, we will expect forecasters to establish the accuracy of their forecasting with rigorous testing before we heed their advice.  And forecasters themselves will realize”¦ that these higher expectations will ultimately benefit them, because it is only with the clear feedback that comes from rigorous testing that they can improve their foresight.  It could be huge””an “evidence-based forecasting” revolution similar to the “evidence-based medicine” revolution, with consequences every bit as significant.

Or nothing may change.  Revolutionaries aren’t supposed to say failure is possible, but let’s think like superforecasters here and acknowledge that things may go either way.

Change

Tetlock writes about a Boston doctor named Ernest Amory Codman who proposed an idea he called the End Result System.

Hospitals should record what ailments incoming patients had, how they were treated, and””most important””the end result of each case.  These records should be compiled and statistics released so consumers could choose hospitals on the basis of good evidence.  Hospitals would respond to consumer pressure by hiring and promoting doctors on the same basis.  Medicine would improve, to the benefit of all.

Today, hospitals do much of what Codman suggested, and physicians have embraced such measurement.  But when Codman first put forth his idea, the medical establishment rejected it.  Hospitals didn’t want to pay for record keepers.  And doctors were already respected.  Keeping score could only hurt their reputations.   Codman was fired from Massachusetts General Hospital.  And Codman lost his teaching post at Harvard.  Eventually, however, his core idea was accepted.

Other areas of society are following the example of evidence-based medicine.  There’s evidence-based government policy.  There’s evidence-based philanthropy, led by the Gates Foundation.  There’s evidence-based management of sports teams.

One possible criticism of superforecasting is that it doesn’t deal with big enough questions.  Tetlock responds that a lot of smaller questions can ultimately shed light on bigger questions.  He calls this Bayesian question clustering.  For instance, in considering a big question like whether there will be another Korean war, you can focus on smaller questions related to missile launches, nuclear tests, cyber attacks, and artillery shelling.  The answers are cumulative.  The more yeses, the more likely the overall situation will end badly.

It’s interesting that hedgehog-minded experts are often good at coming up with big questions even though they’re usually not good at making forecasts.  Tetlock writes:

While we may assume that a superforecaster would also be a superquestioner, we don’t actually know that.  Indeed, my best scientific guess is that they often are not.  The psychological recipe for the ideal superforecaster may prove to be quite different from that for the ideal superquestioner, as superb question generation often seems to accompany a hedgehog-like incisiveness and confidence that one has a Big Idea grasp of the deep drivers of an event.  That’s quite a different mindset from the foxy eclecticism and sensitivity to uncertainty that characterizes superb forecasting.

Tetlock continues:

Superforecasters and superquestioners need to acknowledge each other’s complementary strengths, not dwell on each other’s alleged weaknesses.  Friedman poses provocative questions that superforecasters should use to sharpen their foresight; superforecasters generate well-calibrated answers that superquestioners should use to fine-tune and occasionally overhaul their mental models of reality”¦

“¦But there’s a much bigger collaboration I’d like to see.  It would be the Holy Grail of my research program: using forecasting tournaments to depolarize unnecessarily polarized policy debates and make us collectively smarter.

 

TEN COMMANDMENTS FOR ASPIRING SUPERFORECASTERS

Triage.

Focus on questions where your hard work is likely to pay off.  Don’t waste time either on easy “clocklike” questions (where simple rules of thumb can get you close to the right answer) or on impenetrable “cloud-like” questions (where even fancy statistical models can’t beat the dart-throwing chimp).  Concentrate on questions in the Goldilocks zone of difficulty, where effort pays off the most.

Break seemingly intractable problems into tractable sub-problems.

Decompose the problem into its knowable and unknowable parts.  Flush ignorance into the open.  Expose and examine your assumptions.  Dare to be wrong by making your best guesses.  Better to discover errors quickly than to hide them behind vague verbiage”¦ The surprise is now often remarkably good probability estimates arise from a remarkably crude series of assumptions and guesstimates.

Strike the right balance between inside and outside views.

Superforecasters know that there is nothing new under the sun.  Nothing is 100% “unique.””¦ Superforecasters are in the habit of posing the outside-view question: How often do things of this sort happen in situations of this sort?

Strike the right balance between under- and overreacting to evidence.

The best forecasters tend to be incremental belief updaters, often moving from probabilities of, say, 0.4 to 0.35 or from 0.6 to 0.65, distinctions too subtle to capture with vague verbiage, like “might” or “maybe,” but distinctions that, in the long run, define the difference between good and great forecasters.

Yet superforecasters also know how to jump, to move their probability estimates fast in response to diagnostic signals.  Superforecasters are not perfect Bayesian updaters but they are better than most of us.  And that is largely because they value this skill and work hard at cultivating it.

Look for the clashing causal forces at work in each problem.

For every good policy argument, there is typically a counterargument that is at least worth acknowledging.  For instance, if you are a devout dove who believes that threatening military action never brings peace, be open to the possibility that you might be wrong about Iran.  And the same advice applies if you are a devout hawk who believes that soft “appeasement” policies never pay off.  Each side should list, in advance, the signs that would nudge them toward the other.

Now here comes the really hard part.  In classical dialectics, thesis meets antithesis, producing synthesis.  In dragonfly eye, one view meets another and another and another””all of which must be synthesized into a single image.  There are no paint-by-number rules here.  Synthesis is an art that requires reconciling irreducibly subjective judgments.  If you do it well, engaging in this process of synthesizing should transform you from a cookie-cutter dove or hawk into an odd hybrid creature, a dove-hawk, with a nuanced view of when tougher or softer policies are likelier to work.

Strive to distinguish as many degrees of doubt as the problem permits but no more.

Few things are either certain or impossible.  And “maybe” isn’t all that informative.  So your uncertainty dial needs more than three settings.  Nuance matters.  The more degrees of uncertainty you can distinguish, the better a forecaster you are likely to be.  As in poker, you have an advantage if you are better than your competitors at separating 60/40 bets from 40/60″”or 55/45 from 45/55.  Translating vague-verbiage hunches into numeric probabilities feels unnatural at first but it can be done.  It just requires patience and practice.  The superforecasters have shown what is possible.

Strike the right balance between under- and overconfidence, between prudence and decisiveness.

Superforecasters understand the risks both of rushing to judgment and of dawdling too long near “maybe.””¦They realize that long-term accuracy requires getting good scores on both calibration and resolution”¦ It is not enough just to avoid the most recent mistake.  They have to find creative ways to tamp down both types of forecasting errors””misses and false alarms””to the degree a fickle world permits such uncontroversial improvements in accuracy.

Look for the errors behind your mistakes but beware of rearview-mirror hindsight biases.

Don’t try to justify or excuse your failures.  Own them!  Conduct unflinching postmortems: Where exactly did I go wrong?  And remember that although the more common error is to learn too little from failure and to overlook flaws in your basic assumptions, it is also possible to learn too much (you may have been basically on the right track but made a minor technical mistake that had big ramifications).  Also don’t forget to do postmortems on your successes too.  Not all successes imply that your reasoning was right.  You may have just lucked out by making offsetting errors.  And if you keep confidently reasoning along the same lines, you are setting yourself up for a nasty surprise.

Bring out the best in others and let others bring out the best in you.

Master the fine arts of team management, especially perspective taking (understanding the arguments of the other side so well that you can reproduce them to the other’s satisfaction), precision questioning (helping others to clarify their arguments so they are not misunderstood), and constructive confrontation (learning to disagree without being disagreeable).  Wise leaders know how fine the line can be between a helpful suggestion and micromanagerial meddling or between a rigid group and a decisive one or between a scatterbrained group and an open-minded one.

Master the error-balancing bicycle.

Implementing each commandment requires balancing opposing errors”¦ Learning requires doing, with good feedback that leaves no ambiguity about whether you are succeeding”¦ or whether you are failing”¦ Also remember that practice is not just going through the motions of making forecasts, or casually reading the news and tossing out probabilities.  Like all other known forms of expertise, superforecasting is the product of deep, deliberative practice.

Don’t treat commandments as commandments.

Guidelines are the best we can do in a world where nothing is certain or exactly repeatable.  Superforecasting requires constant mindfulness, even when””perhaps especially when””you are dutifully trying to follow these commandments.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time.  See the historical chart here:  https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps.  Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals.  We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio.  The size of each position is determined by its rank.  Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost).  Positions are held for 3 to 5 years unless a stock approaches intrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods.  We also aim to outpace the Russell Microcap Index by at least 2% per year (net).  The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: [email protected]

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

August 21, 2022

In his pursuit of wisdom, Peter Bevelin was inspired by Charlie Munger’s idea:

I believe in the discipline of mastering the best of what other people have ever figured out.

Bevelin was also influenced by Munger’s statement that Charles Darwin was one of the best thinkers who ever lived. Despite the fact that many others had much higher IQ’s. Bevelin:

Darwin’s lesson is that even people who aren’t geniuses can outthink the rest of mankind if they develop certain thinking habits.

(Photo by Maull and Polyblank (1855), via Wikimedia Commons)

In the spirit of Darwin and Munger, and with the goal of gaining a better understanding of human behavior, Bevelin read books in biology, psychology, neuroscience, physics, and mathematics. Bevelin took extensive notes. The result is the book,Seeking Wisdom: From Darwin to Munger.

Here’s the outline:

PART ONE: WHAT INFLUENCES OUR THINKING

• Our anatomy sets the limits for our behavior
• Evolution selected the connections that produce useful behavior for survival and reproduction
• Adaptive behavior for survival and reproduction

PART TWO: THE PSYCHOLOGY OF MISJUDGMENTS

• Misjudgments explained by psychology
• Psychological reasons for mistakes

PART THREE: THE PHYSICS AND MATHEMATICS OF MISJUDGMENTS

• Systems thinking
• Scale and limits
• Causes
• Numbers and their meaning
• Probabilities and number of possible outcomes
• Scenarios
• Coincidences and miracles
• Reliability of case evidence
• Misrepresentative evidence

PART FOUR: GUIDELINES TO BETTER THINKING

• Models of reality
• Meaning
• Simplification
• Rules and filters
• Goals
• Alternatives
• Consequences
• Quantification
• Evidence
• Backward thinking
• Risk
• Attitudes

(Photo by Nick Webb)

 

Part One: What Influences Our Thinking

OUR ANATOMY SETS THE LIMITS FOR OUR BEHAVIOR

Bevelin quotes Nobel Laureate Dr. Gerald Edelman:

The brain is the most complicated material object in the known universe. If you attempted to count the number of connections, one per second, in the mantle of the brain (the cerebral cortex), you would finish counting 32 million years later. But that is not the whole story. The way the brain is connected–its neuroanatomical pattern–is enormously intricate. Within this anatomy a remarkable set of dynamic events take place in hundredths of a second and the number of levels controlling these events, from molecules to behavior, is quite large.

Neurons can send signals–electrochemical pulses–to specific target cells over long distances. These signals are sent by axons, thin fibers that extend from neurons to other parts of the brain. Axons can be quite long.

(Illustration by ustas)

Some neurons emit electrochemical pulses constantly while other neurons are quiet most of the time. A single axon can have several thousand synaptic connections. When an electrochemical pulse travels along an axon and reaches a synapse, it causes a neurotransmitter (a chemical) to be released.

The human brain contains approximately 100 trillion synapses. From wikipedia:

The functions of these synapses are very diverse: some are excitatory (exciting the target cell); others are inhibitory; others work by activatingsecond messenger systemsthat change the internal chemistry of their target cells in complex ways. Alarge number of synapses are dynamically modifiable; that is, they are capable of changing strength in a way that is controlled by the patterns of signals that pass through them. It is widely believed thatactivity-dependent modification of synapsesis the brain’s primary mechanism for learning and memory.

Most of the space in the brain is taken up by axons, which are often bundled together in what are callednerve fiber tracts. A myelinated axon is wrapped in a fatty insulating sheath ofmyelin, which serves to greatly increase the speed of signal propagation. (There are also unmyelinated axons). Myelin is white, making parts of the brain filled exclusively with nerve fibers appear as light-coloredwhite matter, in contrast to the darker-coloredgrey matterthat marks areas with high densities of neuron cell bodies.

Genes, life experiences, and randomness determine how neurons connect.

Also, everything that happens in the brain involves many areas at once (the left brain versus right brain distinction is not strictly accurate). This is part of why the brain is so flexible. There are different ways for the brain to achieve the same result.

 

EVOLUTION SELECTED THE CONNECTIONS THAT PRODUCE USEFUL BEHAVIOR FOR SURVIVAL AND REPRODUCTION

Bevelin writes:

If certain connections help us interact with our environment, we use them more often than connections that don’t help us. Since we use them more often, they become strengthened.

Evolution has given us preferences that help us classify what is good or bad. When these values are satisfied (causing either pleasure or less pain) through the interaction with our environment, these neural connections are strengthened. These values are reinforced over time because they give humans advantages for survival and reproduction in dealing with their environment.

(Illustration by goce risteski)

If a certain behavior is rewarding, the neural connections associated with that behavior get strengthened. The next time the same situation is encountered, we feel motivated to respond in the way that we’ve learned brings pleasure (or reduces pain). Bevelin:

We do things that we associate with pleasure and avoid things that we associate with pain.

 

ADAPTIVE BEHAVIOR FOR SURVIVAL AND REPRODUCTION

Bevelin:

The consequences of our actions reinforce certain behavior. If the consequences were rewarding, our behavior is likely to be repeated. What we consider rewarding is individual specific. Rewards can be anything from health, money, job, reputation, family, status, or power. In all of these activities, we do what works. This is how we adapt. The environment selects our future behavior.

Especially in a random environment like the stock market, it can be difficult to figure out what works and what doesn’t. We may make a good decision based on the odds, but get a poor outcome. Or we may make a bad decision based on the odds, but get a good outcome. Only over the course of many decisions can we tell if our investment process is probably working.

 

Part Two: The Psychology of Misjudgments

Bevelin quotes the Greek philosopher and orator, Dio Chrysostom:

“Why oh why are human beings so hard to teach, but so easy to deceive.”

MISJUDGMENTS EXPLAINED BY PSYCHOLOGY

Bevelin lists 28 reasons for misjudgments and mistakes:

1. Bias from mere association–automatically connecting a stimulus with pain or pleasure; including liking or disliking something associated with something bad or good. Includes seeing situations as identical because they seem similar. Also bias from Persian Messenger Syndrome–not wanting to be the carrier of bad news.
2. Underestimating the power of incentives (rewards and punishment)–people repeat actions that result in rewards and avoid actions that they are punished for.
3. Underestimating bias from own self-interest and incentives.
4. Self-serving bias–overly positive view of our abilities and future. Includes over-optimism.
5. Self-deception and denial–distortion of reality to reduce pain or increase pleasure. Includes wishful thinking.
6. Bias from consistency tendency–being consistent with our prior commitments and ideas even when acting against our best interest or in the face of disconfirming evidence. Includes Confirmation Bias–looking for evidence that confirms our actions and beliefs and ignoring or distorting disconfirming evidence.
7. Bias from deprival syndrome–strongly reacting (including desiring and valuing more) when something we like and have (or almost have) is (or threatens to be) taken away or “lost.” Includes desiring and valuing more what we can’t have or what is (or threatens to be) less available.
8. Status quo bias and do-nothing syndrome–keeping things the way they are. Includes minimizing effort and a preference for default options.
9. Impatience–valuing the present more highly than the future.
10. Bias from envy and jealousy.
11. Distortion by contrast comparison–judging and perceiving the absolute magnitude of something not by itself but based only on its difference to something else presented closely in time or space or to some earlier adaptation level. Also underestimating the consequences over time of gradual changes.
12. The anchoring effect–People tend to use any random number as a baseline for estimating an unknown quantity, despite the fact that the unknown quantity is totally unrelated to the random number. (People also overweigh initial information that is non-quantitative.)
13. Over-influence by vivid or the most recent information.
14. Omission and abstract blindness–only seeing stimuli we encounter or that grabs our attention, and neglecting important missing information or the abstract. Includes inattentional blindness.
15. Bias from reciprocation tendency–repaying in kind what others have done for or to us like favors, concessions, information, and attitudes.
16. Bias from over-influence by liking tendency–believing, trusting, and agreeing with people we know and like. Includes bias from over-desire for liking and social acceptance and for avoiding social disapproval. Also bias from disliking–our tendency to avoid and disagree with people we don’t like.
17. Bias from over-influence by social proof–imitating the behavior of many others or similar others. Includes crowd folly.
18. Bias from over-influence by authority–trusting and obeying a perceived authority or expert.
19. The Narrative Fallacy (Bevelin uses the term “Sensemaking”)–constructing explanations that fit an outcome. Includes being too quick in drawing conclusions. Also Hindsight Bias: Thinking events that have happened were more predictable than they were.
20. Reason-respecting–complying with requests merely because we’ve been given a reason. Includes underestimating the power in giving people reasons.
21. Believing first and doubting later–believing what is not true, especially when distracted.
22. Memory limitations–remembering selectively and wrong. Includes influence by suggestions.
23. Do-something syndrome–acting without a sensible reason.
24. Mental confusion from say-something syndrome–feeling a need to say something when we have nothing to say.
25. Emotional arousal–making hasty judgments under the influence of intense emotions. Includes exaggerating the emotional impact of future events.
26. Mental confusion from stress.
27. Mental confusion from physical or psychological pain, and the influence of chemicals or diseases.
28. Bias from over-influence by the combined effect of many psychological tendencies operating together.

 

PSYCHOLOGICAL REASONS FOR MISTAKES

Bevelin notes that his explanations for the 28 reasons for misjudgments is based on work by Charles Munger, Robert Cialdini, Richard Thaler, Robyn Dawes, Daniel Gilbert, Daniel Kahneman, and Amos Tversky. All are psychologists except for Thaler (economist) and Munger (investor).

1. Mere Association

Bevelin:

Association can influence the immune system. One experiment studied food aversion in mice. Mice got saccharin-flavored water (saccharin has incentive value due to its sweet taste) along with a nausea-producing drug. Would the mice show signs of nausea the next time they got saccharin water alone? Yes, but the mice also developed infections. It was known that the drug in addition to producing nausea, weakened the immune system, but why would saccharin alone have this effect? The mere paring of the saccharin with the drug caused the mouse immune system to learn the association. Therefore, every time the mouse encountered the saccharin, its immune system weakened making the mouse more vulnerable to infections.

If someone brings us bad news, we tend to associate that person with the bad news–and dislike them–even if the person didn’t cause the bad news.

2. Incentives (Reward and Punishment)

Incentives are extremely important. Charlie Munger:

I think I’ve been in the top 5% of my age cohort all my life in understanding the power of incentives, and all my life I’ve underestimated it. Never a year passes that I don’t get some surprise that pushes my limit a little farther.

Munger again:

From all business, my favorite case on incentives is Federal Express. The heart and soul of their system–which creates the integrity of the product–is having all their airplanes come to one place in the middle of the night and shift all the packages from plane to plane. If there are delays, the whole operation can’t deliver a product full of integrity to Federal Express customers. And it was always screwed up. They could never get it done on time. They tried everything–moral suasion, threats, you name it. And nothing worked. Finally, somebody got the idea to pay all these people not so much an hour, but so much a shift–and when it’s all done, they can all go home. Well, their problems cleared up over night.

People can learn the wrong incentives in a random environment like the stock market. A good decision based on the odds may yield a bad result, while a bad decision based on the odds may yield a good result. People tend to become overly optimistic after a success (even if it was good luck) and overly pessimistic after a failure (even if it was bad luck).

3. Self-interest and Incentives

“Never ask the barber if you need a haircut.”

Munger has commented that commissioned sales people, consultants, and lawyers have a tendency to serve the transaction rather than the truth. Many others–including bankers and doctors–are in the same category. Bevelin quotes the American actor Walter Matthau:

“My doctor gave me six months to live. When I told him I couldn’t pay the bill, he gave me six more months.”

If they make unprofitable loans, bankers may be rewarded for many years while the consequences of the bad loans may not occur for a long time.

When designing a system, careful attention must be paid to incentives. Bevelin notes that a new program was put in place in New Orleans: districts that showed improvement in crime statistics would receive rewards, while districts that didn’t faced cutbacks and firings. As a result, in one district, nearly half of all serious crimes were re-classified as minor offences and never fully investigated.

4. Self-serving Tendencies and Overoptimism

We tend to overestimate our abilities and future prospects when we are knowledgeable on a subject, feel in control, or after we’ve been successful.

Bevelin again:

When we fail, we blame external circumstances or bad luck. When others are successful, we tend to credit their success to luck and blame their failures on foolishness. When our investments turn into losers, we had bad luck. When they turn into winners, we are geniuses. This way we draw the wrong conclusions and don’t learn from our mistakes. We also underestimate luck and randomness in outcomes.

5. Self-deception and Denial

Munger likes to quote Demosthenes:

Nothing is easier than self-deceit. For what each man wishes, that he also believes to be true.

People have a strong tendency to believe what they want to believe. People prefer comforting illusions to painful truths.

Richard Feynman:

The first principle is that you must not fool yourself–and you are the easiest person to fool.

6. Consistency

Bevelin:

Once we’ve made a commitment–a promise, a choice, taken a stand, invested time, money, or effort–we want to remain consistent. We want to feel that we’ve made the right decision. And the more we have invested in our behavior the harder it is to change.

The more time, money, effort, and pain we invest in something, the more difficulty we have at recognizing a mistaken commitment. We don’t want to face the prospect of a big mistake.

For instance, as the Vietnam War became more and more a colossal mistake, key leaders found it more and more difficult to recognize the mistake and walk away. The U.S. could have walked away years earlier than it did, which would have saved a great deal of money and thousands of lives.

Bevelin quotes Warren Buffett:

What the human being is best at doing is interpreting all new information so that their prior conclusions remain intact.

Even scientists, whose job is to be as objective as possible, have a hard time changing their minds after they’ve accepted the existing theory for a long time. Physicist Max Planck:

A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die and a new generation grows up that is familiar with it.

7. Deprival Syndrome

Bevelin:

When something we like is (or threatens to be) taken away, we often value it higher. Take away people’s freedom, status, reputation, money, or anything they value, and they get upset… The more we like what is taken away or the larger the commitment we’ve made, the more upset we become. This can create hatreds, revolts, violence, and retaliations.

Fearing deprival, people will be overly conservative or will engage in cover-ups.

A good value investor is wrong roughly 40 percent of the time. However, due to deprival syndrome and loss aversion–the pain of a loss is about 2 to 2.5 times greater than the pleasure of an equivalent gain–investors have a hard time admitting their mistakes and moving on. Admitting a mistake means accepting a loss of money and also recognizing our own fallibility.

Furthermore, deprival syndrome makes us keep trying something if we’ve just experienced a series of near misses. We feel that “we were so close” to getting some reward that we can’t give up now, even if the reward may not be worth the expected cost.

Finally, the harder it is to get something, the more value we tend to place on it.

8. Status Quo and Do-Nothing Syndrome

We feel worse about a harm or loss if it results from our action than if it results from our inaction. We prefer the default option–what is selected automatically unless we change it. However, as Bevelin points out, doing nothing is still a decision and the cost of doing nothing could be greater than the cost of taking an action.

In countries where being an organ donor is the default choice, people strongly prefer to be organ donors. But in countries where not being an organ donor is the default choice, people prefer not to be organ donors. In each case, most people simply go with the default option–the status quo. But society is better off if most people are organ donors.

9. Impatience

We value the present more than the future. We often seek pleasure today at the cost of a potentially better future. It’s important to understand that pain and sacrifice today–if done for the right reasons–can lead to greater happiness in the future.

10. Envy and Jealousy

Charlie Munger and Warren Buffett often point out that envy is a stupid sin because–unlike other sins like gluttony–there’s no upside. Also, jealousy is among the top three motives for murder.

It’s best to set goals and work towards them without comparing ourselves to others. Partly by chance, there are always some people doing better and some people doing worse.

11. Contrast Comparison

The classic demonstration of contrast comparison is to stick one hand in cold water and the other hand in warm water. Then put both hands in a buck with room temperature water. Your cold hand will feel warm while your warm hand will feel cold.

Bevelin writes:

We judge stimuli by differences and changes and not absolute magnitudes. For example, we evaluate stimuli like temperature, loudness, brightness, health, status, or prices based on their contrast or difference from a reference point (the prior or concurrent stimuli or what we have become used to). This reference point changes with new experiences and context.

How we value things depends on what we compare them with.

Salespeople, after selling the main item, often try to sell add-ons, which seem cheap by comparison. If you buy a car for $50,000, then adding an extra $1,000 for leather doesn’t seem like much. If you buy a computer for $1,500, then adding an extra $50 seems inconsequential.

Bevelin observes:

The same thing may appear attractive when compared to less attractive things and unattractive when compared to more attractive things. For example, studies show that a person of average attractiveness is seen as less attractive when compared to more attractive others.

One trick some real estate agents use is to show the client a terrible house at an absurdly high price first, and then show them a merely mediocre house at a somewhat high price. The agent often makes the sale.

Munger has remarked that some people enter into a bad marriage because their previous marriage was terrible. These folks make the mistake of thinking that what is better based on their own limited experience is the same as what is better based on the experience of many different people.

Another issue is that something can gradually get much worse over time, but we don’t notice it because each increment is small. It’s like the frog in water where the water is slowly brought to the boiling point. For instance, the behavior of some people may get worse and worse and worse. But we fail to notice because the change is too gradual.

12. Anchoring

The anchoring effect: People tend to use any random number as a baseline for estimating an unknown quantity, despite the fact that the unknown quantity is totally unrelated to the random number. (People also overweigh initial information that is non-quantitative.)

Daniel Kahneman and Amos Tversky did one experiment where they spun a wheel of fortune, but they had secretly programmed the wheel so that it would stop on 10 or 65. After the wheel stopped, participants were asked to estimate the percentage of African countries in the UN. Participants who saw “10” on the wheel guessed 25% on average, while participants who saw “65” on the wheel guessed 45% on average, ahuge difference.

Behavioral finance expert James Montier has run his own experiment on anchoring. People are asked to write down the last four digits of their phone number. Then they are asked whether the number of doctors in their capital city is higher or lower than the last four digits of their phone number. Results: Those whose last four digits were greater than 7,000 on average report 6,762 doctors, while those with telephone numbers below 2,000 arrived at an average 2,270 doctors. (James Montier, Behavioural Investing, Wiley 2007, page 120)

Those are just two experiments out of many. Theanchoring effectis “one of the most reliable and robust results of experimental psychology,” says Kahneman. Furthermore, Montier observes that the anchoring effect is one reason why people cling to financial forecasts, despite the fact that most financial forecasts are either wrong, useless, or impossible to time.

When faced with the unknown, people will grasp onto almost anything. So it is little wonder that an investor will cling to forecasts, despite their uselessness.

13. Vividness and Recency

Bevelin explains:

The more dramatic, salient, personal, entertaining, or emotional some information, event, or experience is, the more influenced we are. For example, the easier it is to imagine an event, the more likely we are to think that it will happen.

We are easily influenced when we are told stories because we relate to stories better than to logic or fact. We love to be entertained. Information we receive directly, through our eyes or ears has more impact than information that may have more evidential value. A vivid description from a friend or family member is more believable than true evidence. Statistical data is often overlooked. Studies show that jurors are influenced by vivid descriptions. Lawyers try to present dramatic and memorable testimony.

The media capitalizes on negative events–especially if they are vivid–because negative news sells. For instance, even though the odds of being in a plane crash are infinitesimally low–one in 11 million–people become very fearful when a plane crash is reported in the news. Many people continue to think that a car is safer than a plane, but you are over 2,000 times more likely to be in a car crash than a plane crash. (The odds of being in a car crash are one in 5,000.)

14. Omission and Abstract Blindness

We see the available information. We don’t see what isn’t reported. Missing information doesn’t draw our attention. We tend not to think about other possibilities, alternatives, explanations, outcomes, or attributes. When we try to find out if one thing causes another, we only see what happened, not what didn’t happen. We see when a procedure works, not when it doesn’t work. When we use checklists to find out possible reasons for why something doesn’t work, we often don’t see that what is not on the list in the first place may be the reason for the problem.

Often we don’t see things right in front of us if our attention is focused elsewhere.

15. Reciprocation

Munger:

The automatic tendency of humans to reciprocate both favors and disfavors has long been noticed as it is in apes, monkeys, dogs, and many less cognitively gifted animals. The tendency facilitates group cooperation for the benefit of members.

Unfortunately, hostility can get extreme. But we have the ability to train ourselves. Munger:

The standard antidote to one’s overactive hostility is to train oneself to defer reaction. As my smart friend Tom Murphy so frequently says, ‘You can always tell the man off tomorrow, if it is such a good idea.’

Munger then notes that the tendency to reciprocate favor for favor is also very intense. On the whole, Munger argues, the reciprocation tendency is a positive:

Overall, both inside and outside religions, it seems clear to me that Reciprocation Tendency’s constructive contributions to man far outweigh its destructive effects…

And the very best part of human life probably lies in relationships of affection wherein parties are more interested in pleasing than being pleased–a not uncommon outcome in display of reciprocate-favor tendency.

Guilt is also a net positive, asserts Munger:

…To the extent the feeling of guilt has an evolutionary base, I believe the most plausible cause is the mental conflict triggered in one direction by reciprocate-favor tendency and in the opposite direction by reward superresponse tendency pushing one to enjoy one hundred percent of some good thing… And if you, like me… believe that, averaged out, feelings of guilt do more good than harm, you may join in my special gratitude for reciprocate-favor tendency, no matter how unpleasant you find feelings of guilt.

16. Liking and Disliking

Munger:

One very practical consequence of Liking/Loving Tendency is that it acts as a conditioning device that makes the liker or lover tend (1) to ignore faults of, and comply with wishes of, the object of his affection, (2) to favor people, products, and actions merely associated with the object of his affection [this is also due to Bias from Mere Association] and (3) to distort other facts to facilitate love.

We’re naturally biased, so we have to be careful in some situations.

On the other hand, Munger points out that loving admirable persons and ideas can be very beneficial.

…a man who is so constructed that he loves admirable persons and ideas with a special intensity has a huge advantage in life. This blessing came to both Buffett and myself in large measure, sometimes from the same persons and ideas. One common, beneficial example for us both was Warren’s uncle, Fred Buffett, who cheerfully did the endless grocery-store work that Warren and I ended up admiring from a safe distance. Even now, after I have known so many other people, I doubt if it is possible to be a nicer man than Fred Buffett was, and he changed me for the better.

Warren Buffett:

If you tell me who your heroes are, I’ll tell you how you’re gonna turn out. It’s really important in life to have the right heroes. I’ve been very lucky in that I’ve probably had a dozen or so major heroes. And none of them have ever let me down. You want to hang around with people that are better than you are. You will move in the direction of the crowd that you associate with.

Disliking: Munger notes that Switzerland and the United States have clever political arrangements to “channel” the hatreds and dislikings of individuals and groups into nonlethal patterns including elections.

But the dislikings and hatreds never go away completely… And we also get the extreme popularity of very negative political advertising in the United States.

Munger explains:

Disliking/Hating Tendency also acts as a conditioning device that makes the disliker/hater tend to (1) ignore virtues in the object of dislike, (2) dislike people, products, and actions merely associated with the object of dislike, and (3) distort other facts to facilitate hatred.

Distortion of that kind is often so extreme that miscognition is shockingly large. When the World Trade Center was destroyed, many Pakistanis immediately concluded that the Hindus did it, while many Muslims concluded that the Jews did it. Such factual distortions often make mediation between opponents locked in hatred either difficult or impossible. Mediations between Israelis and Palestinians are difficult because facts in one side’s history overlap very little with facts from the other side’s.

17. Social Proof

Munger comments:

The otherwise complex behavior of man is much simplified when he automatically thinks and does what he observes to be thought and done around him. And such followership often works fine…

Psychology professors love Social-Proof Tendency because in their experiments it causes ridiculous results. For instance, if a professor arranges for some stranger to enter an elevator wherein ten ‘compliance practitioners’ are all standing so that they face the rear of the elevator, the stranger will often turn around and do the same.

Of course, like the other tendencies, Social Proof has an evolutionary basis. If the crowd was running in one direction, typically your best response was to follow.

But, in today’s world, simply copying others often doesn’t make sense. Munger:

And in the highest reaches of business, it is not at all uncommon to find leaders who display followership akin to that of teenagers. If one oil company foolishly buys a mine, other oil companies often quickly join in buying mines. So also if the purchased company makes fertilizer. Both of these oil company buying fads actually bloomed, with bad results.

Of course, it is difficult to identify and correctly weigh all the possible ways to deploy the cash flow of an oil company. So oil company executives, like everyone else, have made many bad decisions that were triggered by discomfort from doubt. Going along with social proof provided by the action of other oil companies ends this discomfort in a natural way.

Munger points out that Social Proof can sometimes be constructive:

Because both bad and good behavior are made contagious by Social-Proof Tendency, it is highly important that human societies (1) stop any bad behavior before it spreads and (2) foster and display all good behavior.

It’s vital for investors to be able to think independently. As Ben Graham says:

You are neither right nor wrong because the crowd disagrees with you. You are right because your data and reasoning are right.

18. Authority

A disturbingly significant portion of copilots will not correct obvious errors made by the pilot during simulation exercises. There are also real world examples of copilots crashing planes because they followed the pilot mindlessly. Munger states:

…Such cases are also given attention in the simulator training of copilots who have to learn to ignore certain really foolish orders from boss pilots because boss pilots will sometimes err disastrously. Even after going through such a training regime, however, copilots in simulator exercises will too often allow the simulated plane to crash because of some extreme and perfectly obvious simulated error of the chief pilot.

Psychologist Stanley Milgram wanted to understand why so many seemingly normal and decent people engaged in horrific, unspeakable acts during World War II. Munger:

[Milgram] decided to do an experiment to determine exactly how far authority figures could lead ordinary people into gross misbehavior. In this experiment, a man posing as an authority figure, namely a professor governing a respectable experiment, was able to trick a great many ordinary people into giving what they had every reason to believe were massive electric shocks that inflicted heavy torture on innocent fellow citizens…

Almost any intelligent person with my checklist of psychological tendencies in his hand would, by simply going down the checklist, have seen that Milgram’s experiment involved about six powerful psychological tendencies acting in confluence to bring about his extreme experimental result. For instance, the person pushing Milgram’s shock lever was given much social proof from presence of inactive bystanders whose silence communicated that his behavior was okay…

Bevelin quotes the British novelist and scientist Charles Percy Snow:

When you think of the long and gloomy history of man, you will find more hideous crimes have been committed in the name of obedience than have ever been committed in the name of rebellion.

19. The Narrative Fallacy (Sensemaking)

(Bevelin uses the term “sensemaking,” but “narrative fallacy” is better, in my view.) InThe BlackSwan, Nassim Taleb writes the following about thenarrative fallacy:

The narrative fallacy addresses our limited ability to look at sequences of facts without weaving an explanation into them, or, equivalently, forcing a logical link, anarrow of relationship, upon them. Explanations bind facts together. They make them all the more easily remembered; they help themmake more sense. Where this propensity can go wrong is when it increases ourimpressionof understanding.

Thenarrative fallacyis central to many of the biases and misjudgments mentioned by Charlie Munger. (In his great book,Thinking, Fast and Slow, Daniel Kahneman discusses the narrative fallacy as a central cognitive bias.)The human brain, whether using System 1 (intuition) or System 2 (logic), always looks for or creates logical coherence among random data. Often System 1 is right when it assumes causality; thus, System 1 is generally helpful, thanks to evolution. Furthermore, System 2, by searching for underlying causes or coherence, has, through careful application of the scientific method over centuries, developed a highly useful set of scientific laws by which to explain and predict various phenomena.

The trouble comes when the data or phenomena in question are highly random–or inherently unpredictable (at least for the time being). In these areas, System 1 makes predictions that are often very wrong. And even System 2 assumes necessary logical connections when there may not be any–at least, none that can be discovered for some time.

Note: The eighteenth century Scottish philosopher (and psychologist) David Hume was one of the first to clearly recognize the human brain’s insistence on always assuming necessary logical connections in any set of data or phenomena.

If our goal is to explain certain phenomena scientifically, then we have to develop a testable hypothesis about what will happen (or what will happen with probability x) under specific, relevant conditions. If our hypothesis can’t accurately predict what will happen under specific, relevant conditions, then our hypothesis is not a valid scientific explanation.

20. Reason-respecting

We are more likely to comply with a request if people give us a reason–even if we don’t understand the reason or if it’s wrong. In one experiment, a person approaches people standing in line waiting to use a copy machine and says, “Excuse me, I have 5 pages. May I use the Xerox machine because I have to make some copies?” Nearly everyone agreed.

Bevelin notes that often the word “because” is enough to convince someone, even if no actual reason is given.

21. Believe First and Doubt Later

We are not natural skeptics. We find it easy to believe but difficult to doubt. Doubting is active and takes effort.

Bevelin continues:

Studies show that in order to understand some information, we must first accept it as true… We first believe all information we understand and only afterwards and with effort do we evaluate, and if necessary, un-believe it.

Distraction, fatigue, and stress tend to make us less likely to think things through and more likely to believe something that we normally might doubt.

When it comes to detecting lies, many (if not most) people are only slightly better than chance. Bevelin quotes Michel de Montaigne:

If falsehood, like truth, had only one face, we would be in better shape. For we would take as certain the opposite of what the liar said. But the reverse of truth has a hundred thousand shapes and a limitless field.

22. Memory Limitations

Bevelin:

Our memory is selective. We remember certain things and distort or forget others. Every time we recall an event, we reconstruct our memories. We only remember fragments of our real past experiences. Fragments influenced by what we have learned, our experiences, beliefs, mood, expectations, stress, and biases.

We remember things that are dramatic, fearful, emotional, or vivid. But when it comes to learning in general–as opposed to remembering–we learn better when we’re in a positive mood.

Human memory is flawed to the point that eyewitness identification evidence has been a significant cause of wrongful convictions. Moreover, leading and suggestive questions can cause misidentification. Bevelin:

Studies show that it is easy to get a witness to believe they saw something when they didn’t. Merely let some time pass between their observation and the questioning. Then give them false or emotional information about the event.

23. Do-something Syndrome

Activity is not the same thing as results. Most people feel impelled by boredom or hubris to be active. But many things are not worth doing.

If we’re long-term investors, then nearly all of the time the best thing for us to do is nothing at all (other than learn). This is especially true if we’re tired, stressed, or emotional.

24. Say-something Syndrome

Many people have a hard time either saying nothing or saying, “I don’t know.” But it’s better for us to say nothing if we have nothing to say. It’s better to admit “I don’t know” rather than pretend to know.

25. Emotions

Bevelin writes:

We saw under loss aversion and deprival that we put a higher value on things we already own than on the same things if we don’t own them. Sadness reverses this effect, making us willing to accept less money to sell something than we would pay to buy it.

It’s also worth repeating: If we feel emotional, it’s best to defer important decisions whenever possible.

26. Stress

A study showed that business executives who are committed to their work and who have a positive attitude towards challenges–viewing them as opportunities for growth–do not get sick from stress. Business executives who lack such commitment or who lack a positive attitude towards challenges are more likely to get sick from stress.

Stress itself is essential to life. We need challenges. What harms us is not stress but distress–unnecessary anxiety and unhelpful trains of thought. Bevelin quotes the stoic philosopher Epictetus:

Happiness and freedom begin with a clear understanding of one principle: Some things are within our control, and some things are not. It is only after you have faced up to this fundamental rule and learned to distinguish between what you can and can’t control that inner tranquility and outer effectiveness become possible.

27. Pain and Chemicals

People struggle to think clearly when they are in pain or when they’re drunk or high.

Munger argues that if we want to live a good life, first we should list the things that can ruin a life. Alcohol and drugs are near the top of the list. Self-pity and a poor mental attitude will also be on that list. We can’t control everything that happens, but we can always control our mental attitude. As the Austrian psychiatrist and Holocaust survivor Viktor Frankl said:

Everything can be taken from a man but one thing: the last of the human freedoms–to choose one’s attitude in any given set of circumstances, to choose one’s own way.

28. Multiple Tendencies

Often multiple psychological tendencies operate at the same time. Bevelin gives an example where the CEO makes a decision and expects the board of directors to go along without any real questions. Bevelin explains:

Apart from incentive-caused bias, liking, and social approval, what are some other tendencies that operate here? Authority–the CEO is the authority figure whom directors tend to trust and obey. He may also make it difficult for those who question him. Social proof–the CEO is doing dumb things but no one else is objecting so all directors collectively stay quiet–silence equals consent; illusions of the group as invulnerable and group pressure (loyalty) may also contribute. Reciprocation–unwelcome information is withheld since the CEO is raising the director fees, giving them perks, taking them on trips or letting them use the corporate jet. Association and Persian Messenger Syndrome–a single director doesn’t want to be the carrier of bad news. Self-serving tendencies and optimism–feelings of confidence and optimism: many boards also select new directors who are much like themselves; that share similar ideological viewpoints. Deprival–directors don’t want to lose income and status. Respecting reasons no matter how illogical–the CEO gives them reasons. Believing first and doubting later–believing what the CEO says even if not true, especially when distracted. Consistency–directors want to be consistent with earlier decisions–dumb or not.

 

Part Three: The Physics and Mathematics of Misjudgments

SYSTEMS THINKING

• Failing to consider that actions have both intended and unintended consequences. Includes failing to consider secondary and higher order consequences and inevitable implications.
• Failing to consider the whole system in which actions and reactions take place, the important factors that make up the system, their relationships and effects of changes on system outcome.
• Failing to consider the likely reaction of others–what is best to do may depend on what others do.
• Failing to consider the implications of winning a bid–overestimating value and paying too much.
• Overestimating predictive ability or using unknowable factors in making predictions.

 

SCALE AND LIMITS

• Failing to consider that changes in size or time influence form, function, and behavior.
• Failing to consider breakpoints, critical thresholds, or limits.
• Failing to consider constraints–that a system’s performance is constrained by its weakest link.

 

CAUSES

• Not understanding what causes desired results.
• Believing cause resembles its effect–that a big effect must have a big or complicated cause.
• Underestimating the influence of randomness in bad or good outcomes.
• Mistaking an effect for its cause. Includes failing to consider that many effects may originate from one common root cause.
• Attributing outcome to a single cause when there are multiple causes.
• Mistaking correlation for cause.
• Failing to consider that an outcome may be consistent with alternative explanations.
• Drawing conclusions about causes from selective data. Includes identifying the wrong cause because it seems the obvious one based on a single observed effect. Also failing to consider information or evidence that is missing.
• Not comparing the difference in conditions, behavior, and factors between negative and positive outcomes in similar situations when explaining an outcome.

 

NUMBERS AND THEIR MEANING

• Looking at isolated numbers–failing to consider relationships and magnitudes. Includes not using basic math to count and quantify. Also not differentiating between relative and absolute risk.
• Underestimating the effect of exponential growth.
• Underestimating the time value of money.

 

PROBABILITIES AND NUMBER OF POSSIBLE OUTCOMES

• Underestimating risk exposure in situations where relative frequency (or comparable data) and/or magnitude of consequences is unknown or changing over time.
• Underestimating the number of possible outcomes for unwanted events. Includes underestimating the probability and severity of rate or extreme events.
• Overestimating the chance of rare but widely publicized and highly emotional events and underestimating the chance of common but less publicized events.
• Failing to consider both probabilities and consequences (expected value).
• Believing events where chance plays a role are self-correcting–that previous outcomes of independent events have predictive value in determining future outcomes.
• Believing one can control the outcome of events where chance is involved.
• Judging financial decisions by evaluating gains and losses instead of final state of wealth and personal value.
• Failing to consider the consequences of being wrong.

 

SCENARIOS

• Overestimating the probability of scenarios where all of a series of steps must be achieved for a wanted outcome. Also underestimating opportunities for failure and what normally happens in similar situations.
• Underestimating the probability of systems failure–scenarios composed of many parts where system failure can happen one way or another. Includes failing to consider that time horizon changes probabilities. Also assuming independence when it is not present and/or assuming events are equally likely when they are not.
• Not adding a factor of safety for known and unknown risks. Size of factor depends on the consequences of failure, how well the risks are understood, systems characteristics, and degree of control.

 

COINCIDENCES AND MIRACLES

• Underestimating that surprises and improbable events happen, somewhere, sometime, to someone, if they have enough opportunities (large enough size or time) to happen.
• Looking for meaning, searching for causes, and making up patterns for chance events, especially events that have emotional implications.
• Failing to consider cases involving the absence of a cause or effect.

 

RELIABILITY OF CASE EVIDENCE

• Overweighing individual case evidence and under-weighing the prior probability (probability estimate of an event before considering new evidence that might change it) considering for example, the base rate (relative frequency of an attribute or event in a representative comparison group), or evidence from many similar cases. Includes failing to consider the probability of a random match, and the probability of a false positive and a false negative. Also failing to consider a relevant comparison population that bears the characteristic we are seeking.

 

MISREPRESENTATIVE EVIDENCE

• Failing to consider changes in factors, context, or conditions when using past evidence to predict likely future outcomes. Includes not searching for explanations to why a past outcome happened, what is required to make the past record continue, and what forces can change it.
• Overestimating evidence from a single case or small or unrepresentative samples.
• Underestimating the influence of chance in performance (success and failure)
• Only seeing positive outcomes–paying little or no attention to negative outcomes and prior probabilities.
• Failing to consider variability of outcomes and their frequency.
• Failing to consider regression–in any series of events where chance is involved, unique outcomes tend to regress back to the average outcome.

 

Part Four: Guidelines to Better Thinking

Bevelin explains: “The purpose of this part is to explore tools that provide a foundation for rational thinking. Ideas that help us when achieving goals, explaining ‘why,’ preventing and reducing mistakes, solving problems, and evaluating statements.”

Bevelin lists 12 tools that he discusses:

• Models of reality
• Meaning
• Simplification
• Rules and filters
• Goals
• Alternatives
• Consequences
• Quantification
• Evidence
• Backward thinking
• Risk
• Attitudes

 

MODELS OF REALITY

Bevelin:

A model is an idea that helps us better understand how the world works. Models illustrate consequences and answer questions like ‘why’ and ‘how.’ Take the model of social proof as an example. What happens? When people are uncertain they often automatically do what others do without thinking about the correct thing to do. This idea helps explain ‘why’ and predict ‘how’ people are likely to behave in certain situations.

Bevelin continues:

Ask: What is the underlying big idea? Do I understand its application in practical life? Does it help me understand the world? How does it work? Why does it work? Under what conditions does it work? How reliable is it? What are its limitations? How does it relate to other models?

What models are most reliable? Bevelin quotes Munger:

“The models that come from hard science and engineering are the most reliable models on this Earth. And engineering quality control–at least the guts of it that matters to you and me and people who are not professional engineers–is very much based on the elementary mathematics of Fermat and Pascal: It costs so much and you get so much less likelihood of it breaking if you spend this much…

And, of course, the engineering idea of a backup system is a very powerful idea. The engineering idea of breakpoints–that’s a very powerful model, too. The notion of a critical mass–that comes out of physics–is a very powerful model.”

Bevelin adds:

A valuable model produces meaningful explanations and predictions of likely future consequences where the cost of being wrong is high.

A model should be easy to use. If it is complicated, we don’t use it.

It is useful on a nearly daily basis. If it is not used, we forget it.

Bevelin asks what can help us to see the big picture. Bevelin quotes Munger again:

“In most messy human problems, you have to be able to useall the big ideas and not just a few of them.”

Bevelin notes that physics does not explain everything, and neither does economics. In business, writes Bevelin, it is useful to know how scale changes behavior, how systems may break, how supply influences prices, and how incentives cause behavior.

It’s also crucial to know how different ideas interact and combine. Munger again:

“You getlollapalooza effects when two, three, or four forces are all operating in the same direction. And, frequently, you don’t get simple addition. It’s often like a critical mass in physics where you get a nuclearexplosion if you get to a certain point of mass–and you don’t get anything much worth seeing if you don’t reach the mass.

Sometimes the forces just add like ordinary quantities and sometimes they combine on a break-point or critical-mass basis… More commonly, the forces coming out of models areconflicting to some extent… So you [must] have themodels and you [must] see therelatedness and the effects from the relatedness.”

 

MEANING

Bevelin writes:

Understanding ‘meaning’ requires that we observe and ask basic questions. Examples of some questions are:

• • Meaning of words: What do the words mean? What do they imply? Do they mean anything? Can we translate words, ideas, or statements into an ordinary situation that tells us something? An expression is always relative. We have to judge and measure it against something.
  • Meaning of an event: What effect is produced? What is really happening using ordinary words? What is it doing? What is accomplished? Under what conditions does it happen? What else does it mean?
  • Causes: What is happening here and why? Is this working? Why or why not? Why did that happen? Why does it work here but not there? How can it happen? What are the mechanisms behind? What makes it happen?
  • Implications: What is the consequence of this observation, event, or experience? What does that imply?
  • Purpose: Why should we do that? Why do I want this to happen?
  • Reason: Why is this better than that?
  • Usefulness: What is the applicability of this? Does it mean anything in relation to what I want to achieve?

Turning to the field of investing, how do we decide how much to pay for a business? Buying stock is buying a fractional share of a business. Bevelin quotes Warren Buffett:

What you’re trying to do is to look at all the cash a business will produce between now and judgment day and discount it back to the present using an appropriate discount rate and buy a lot cheaper than that. Whether the money comes from a bank, an Internet company, a brick company… the money all spends the same. Why pay more for a telecom business than a brick business? Money doesn’t know where it comes from. There’s no sense in paying more for a glamorous business if you’re getting the same amount of money, but paying more for it. It’s the same money that you can get from a brick company at a lower cost. The question is what are the economic characteristics of the bank, the Internet company, or the brick company. That’s going to tell you how much cash they generate over long periods in the future.

 

SIMPLIFICATION

Bevelin quotes Munger:

We have a passion for keeping things simple.

Bevelin then quotes Buffett:

We haven’t succeeded because we have some great, complicated systems or magic formulas we apply or anything of the sort. What we have is just simplicity itself.

Munger again:

If something is too hard, we move on to something else. What could be more simple than that?

Munger:

There are things that we stay away from. We’re like the man who said he had three baskets on his desk: in, out, and too tough. We have such baskets–mental baskets–in our office. An awful lot of stuff goes in the ‘too tough’ basket.

Buffett on how he and Charlie Munger do it:

Easy does it. After 25 years of buying and supervising a great variety of businesses, Charlie and I have not learned how to solve difficult business problems. What we have learned is to avoid them. To the extent we have been successful, it is because we concentrated on identifying one-foot hurdles that we could step over rather than because we acquired any ability to clear seven-footers. The finding may seem unfair, but in both business and investments it is usually far more profitable to simply stick with the easy and obvious than it is to resolve the difficult.

It’s essential that management maintain focus. Buffett:

A… serious problem occurs when the management of a great company gets sidetracked and neglects its wonderful base business while purchasing other businesses that are so-so or worse… (Would you believe that a few decades back they wee growing shrimp at Coke and exploring for oil at Gillette?) Loss of focus is what most worries Charlie and me when we contemplate investing in businesses that in general look outstanding. All too often, we’ve seen value stagnate in the presence of hubris or of boredom that caused the attention of managers to wander.

For an investor considering an investment, it’s crucial to identify what is knowable and what is important. Buffett:

There are two questions you ask yourself as you look at the decision you’ll make. A) is it knowable? B) is it important? If it is not knowable, as you know there are all kinds of things that are important but not knowable, we forget about those. And if it’s unimportant, whether it’s knowable or not, it won’t make any difference. We don’t care.

 

RULES AND FILTERS

Bevelin writes:

When we know what we want, we need criteria to evaluate alternatives. Ask: What are the most critical (and knowable) factors that will cause what I want to achieve or avoid? Criteria must be based on evidence and be reasonably predictive… Try to use as few criteria as necessary to make your judgment. Then rank them in order of their importance and use them as filters. Set decision thresholds in a way that minimizes the likelihood of false alarms and misses (in investing, choosing a bad investment or missing a good investment). Consider the consequences of being wrong.

Bear in mind that in many fields, a relatively simple statistical prediction rule based on a few key variables will perform better than experts over time. See: https://boolefund.com/simple-quant-models-beat-experts-in-a-wide-variety-of-areas/

Bevelin gives as an example the following: a man is rushed to the hospital while having a heart attack. Is it high-risk or low-risk? If the patient’s minimum systolic blood pressure over the initial 24-hour period is less than 91, then it’s high-risk. If not, then the next question is age. If the patient is over 62.5 years old, then if he displays sinus tachycardia, he is high-risk. It turns out that this simple model–developed by Statistics Professor Leo Breiman and colleagues at the University of California, Berkeley–works better than more complex models and also than experts.

In making an investment decision, Buffett has said that he uses a 4-step filter:

• • Can I understand it?
  • Does it look like it has some kind of sustainable competitive advantage?
  • Is the management composed of able and honest people?
  • Is the price right?

If a potential investment passes all four filters, then Buffett writes a check. By “understanding,” Buffett means having a “reasonable probability” of assessing whether the business will be in 10 years.

 

GOALS

Bevelin puts forth:

Always ask: What end result do I want? What causes that? What factors have a major impact on the outcome? What single factor has the most impact? Do I have the variable(s) needed for the goal to be achieved? What is the best way to achieve my goal? Have I considered what other effects my actions will have that will influence the final outcome?

When we solve problems and know what we want to achieve, we need to prioritize or focus on the right problems. What should we do first? Ask: How serious are the problems? Are they fixable? What is the most important problem? Are the assumptions behind them correct? Did we consider the interconnectedness of the problems? The long-term consequences?

 

ALTERNATIVES

Bevelin writes:

Choices have costs. Even understanding has an opportunity cost. If we understand one thing well, we may understand other things better. The cost of using a limited resource like time, effort, and money for a specific purpose, can be measured as the value or opportunity lost by not using it in its best available alternative use…

Bevelin considers a business:

Should TransCorp take the time, money, and talent to build a market presence in Montana? The real cost of doing that is the value of the time, money, and talent used in its best alternative use. Maybe increasing their presence in a state where they already have a market share is creating more value. Sometimes it is more profitable to marginally increase a cost where a company already has an infrastructure. Where to they marginally get the most leverage on resources spent? Always ask: What is the change of value of taking a specific action? Where is it best to invest resources from a value point of view?

 

CONSEQUENCES

Bevelin writes:

Whenever we install a policy, take an action, or evaluate statements, we must trace the consequences. When doing so, we must remember four key things:

• Pay attention to the whole system. Direct and indirect effects,
• Consequences have implications or more consequences, some which may be unwanted. We can’t estimate all possible consequences but there is at least one unwanted consequence we should look out for,
• Consider the effects of feedback, time, scale, repetition, critical thresholds and limits,
• Different alternatives have different consequences in terms of costs and benefits. Estimate the net effects over time and how desirable these are compared to what we want to achieve.

We should heed Buffett’s advice: Whenever someone makes an assertion in economics, always ask, “And then what?” Very often, particularly in economics, it’s the consequences of the consequences that matter.

 

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time. See the historical chart here: https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps. Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals. We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio. The size of each position is determined by its rank. Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost). Positions are held for 3 to 5 years unless a stock approachesintrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods. We also aim to outpace the Russell Microcap Index by at least 2% per year (net). The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: [email protected]

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed.No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC

August 14, 2022

John Brooks is one of the best business writers of all time.Business Adventures may be his best book, butOnce in Golconda,The Go-Go Years, andThe Games Players are also worth reading.

I wrote aboutBusiness Adventures here: https://boolefund.com/business-adventures/

Today’s blog post deals withThe Go-Go Years.

Here’s a brief outline:

• • Climax: The Day Henry Ross Perot Lost $450 million
  • Fair Exchange: The Year the Amex Delisted the Old Guard Romans
  • The Last Gatsby: Recessional for Edward M. Gilbert
  • Palmy Days and Low Rumblings: Early Warnings Along Wall Street
  • Northern Exposure: Early Warnings Along Bay Street
  • The Birth of Go-Go: The Rise of a Proper Chinese Bostonian
  • The Conglomerateurs: Corporate Chutzpah and Creative Accounting
  • The Enormous Back Room: Drugs, Fails, and Chaos Among the Clerks
  • Go-Go at High Noon: The View from Trinity Church
  • Confrontation: Steinberg/Leasco vs. Renchard/Chemical Bank

 

CLIMAX

Henry Ross Perot from Dallas, Texas, was one of the top six richest people in America on April 22, 1970. That day, he suffered a stock market loss of $450 million, still leaving him with a billion dollars worth of stock. Brooks writes that Perot lost more than the assets of any charitable foundation in the country outside of the top five. Brooks adds:

It was also quite possibly more in actual purchasing power than any man had ever lost in a single day since the Industrial Revolution brought large private accumulations of money into being.

On May 8, 1970, schools were closed in protest of Vietnam. One of the antiwar demonstrations by students took place at Wall Street. This particular demonstration was noticeably nonviolent. Unfortunately, right before noon, a group of construction workers–carrying construction tools and wearing heavy boots–attacked the student demonstrators. Fifty (out of a thousand) students needed first-aid treatment, and twenty-three of those were hospitalized.

Brooks explains that workers on Wall Street sided with the students:

Perhaps out of common humanity, or perhaps out of class feeling, the bulls and bears felt more kinship with the doves than with the hawks. At Exchange Place, Robert A. Bernhard, a partner in the aristocratic firm of Lehman Brothers, was himself assaulted and severely cut in the head by a construction worker’s heavy pliers, after he had tried to protect a youth who was being beaten. A few blocks north, a young Wall Street lawyer was knocked down, kicked, and beaten when he protested against hardhats who were yelling, ‘Kill the Commie bastards!’

However, many on Wall Street took no part in the struggle. Brooks continues:

…there is an all too symbolic aspect to professional Wall Street’s role that day as a bystander, sympathizing, unmistakeably, with the underdogs, the unarmed, the peace-lovers, but keeping its hands clean–watching with fascination and horror from its windows…

Brooks asks:

Did it make sense any more to live–and live at the top of the heap–by playing games with paper while children screamed under the window?

Although Perot understood that Wall Street–where a company could be taken public–was the source of his wealth, he nonetheless still believed in the West–and the “frontier”–not the East. Brooks:

He believed that all things were possible in America for the man of enterprise and that the natural habitat of the man of enterprise was the “frontier.”

Perot graduated from the Naval Academy in 1953, where he was class president. After four years of active Navy duty, he went to work as a salesman on commission for IBM.

Perot was earning so much money at IBM that the company cut his commissions by 80 percent and they gave him a quota for the year, past which he wouldn’t earn anything. In 1962, Perot hit his quota on January 19, which made him essentially unemployed for the rest of the year.

Perot’s solution was to start his own company–Electronic Data Systems Corp., designers, installers, and operators of computer systems. The new company struggled for some time. Finally in 1965, federal Medicare legislation was passed. E.D.S. soon had subcontracts to administer Medicare or Medicaid in eleven states.

All told, by 1968 E.D.S. had twenty-three contracts for computer systems, 323 full-time employees, about $10 million in assets, annual net profits of over $1.5 million, and a growth curve so fantastic as to make investment bankers’ mouths water.

By early 1970, having beaten every city slicker he encountered, Perot was worth $1.5 billion. (This was a few years after E.D.S. went public.)

Perot proceeded to become what Brooks calls a “moral billionaire.” He pledged to give away nearly all of his fortune to improve people’s lives. Early on, when he started making charitable donations, he refused to take a tax write-off because he felt he owed tax money to a country that had given him such great opportunities.

Regarding the one-day stock market loss of $450 million, Brooks says:

The way Perot received the news of his monumental setback on April 22 was casual to the point of comedy.

Perot thought, correctly, that the $1.5 billion he had made over eight years wasn’t entirely real because it couldn’t be turned easily into cash. Moreover, he had plenty of money, including a billion dollars in E.D.S. stock post-crash. The bottom line, notes Brooks, was that Perot viewed the one-day swing as a non-event.

Brooks writes that E.D.S. was experiencing outstanding financial results at the time. So the stock swoon wasn’t related to company fundamentals. Many other stocks had fallen far more on a percentage basis than E.D.S. would fall on April 22, 1970.

At any rate, since the vast majority of stocks had already fallen, whereas E.D.S. stock hadn’t fallen at all, it seemed to make sense that E.D.S. stock would finally experience some downward volatility. (Brooks notes that University Computing, a stock in E.D.S’s industry, was 80 percent below its peak before E.D.S. even started falling.) Furthermore, it appeared that there was a bear raid on E.D.S. stock–the stock was vulnerable precisely because it was near its all-time highs, whereas so many other stocks were far lower than their all-time highs.

Brooks concludes:

Nor is it without symbolic importance that the larger market calamity of which E.D.S. was a part resembled in so many respects what had happened forty years before–what wise men had said, for more than a generation, over and over again as if by way of incantation, could never happen again. Ithad happened again, as history will; but (as history will) it had happened differently.

 

FAIR EXCHANGE

Brooks tells the stories of two swindlers, Lowell McAfee Birrell and Alexander Guterma. Brooks writes:

Birrell, like Richard Whitney before him, was apparently a scoundrel as much by choice as by necessity. The son of a small-town Presbyterian minister, a graduate of Syracuse University and Michigan law school, a handsome, brilliant, and charming man who began his career with the aristocratic Wall Street law firm of Cadwalader, Wickersham and Taft and soon belonged to the Union League and Metropolitan Clubs, Birrell, if he had not been Birrell, might easily have become the modern-day equivalent of a Morgan partner–above the battle and beyond reproach.

Birrell issued himself tons of unauthorized stock in corporations he controlled, and then illegally sold the shares. The S.E.C. was after Birrell in 1957. To escape prosecution, Birrell fled to Brazil.

Brooks again:

Guterma was in the mold of the traditional international cheat of spy stories–an elusive man of uncertain national origin whose speech accent sometimes suggested Old Russia, sometimes the Lower East Side of New York, sometimes the American Deep South.

Guterma made his first fortune in the Phillipines during World War II. He ran a gambling casino that catered to occupying Japanese serviceman.

In 1950, Guterma married an American woman and moved to the United States. Brooks:

During the succeeding decade he controlled, and systematically looted, more than a dozen substantial American companies…

In September 1959, Guterma was indicted for fraud, stock manipulation, violation of federal banking laws, and failure to register as the agent of a foreign government.

Brooks mentioned Birrell and Guterma as background to a story in 1961 that involved Gerard A. (Jerry) Re and his son, Gerard F. Re. The Re’s formed the Amex’s largest firm of stock specialists. (At that time, specialists maintained orderly markets in various stocks.) One problem was that specialists often have inside information about specific stocks from which they could profit. Brooks comments:

Pushed in one direction by prudent self-interest, in the other by sense of duty or fear of punishment, a specialist at such times faces a dilemma more appropriate to a hero in Corneille or Racine than to a simple businessman brought up on classic Adam Smith and the comfortable theory of the socially beneficent marketplace.

The S.E.C. finally took notice. Brooks:

Over a period of at least six years, the S.E.C. charged, the father and son had abused their fiduciary duties in just about every conceivable way, repeating a personal profit of something like $3 million. They had made special deals with unethical company heads–Lowell Birrell in particular–to distribute unregistered stock to the public in violation of the law. In order to manipulate the prices of those stocks for their private benefit and that of the executives they were in league with, they had bribed the press, given false tips by word of mouth, paid kickbacks to brokers, generated false public interest by arranging for fictitious trades to be recorded on the tape–the whole, infamous old panoply of sharp stock-jobbing practices.

Ralph S. Saul, the S.E.C.’s young assistant director of the Division of Trading and Exchanges, led the investigation against the Res. After only two hours of oral arguments, the S.E.C. permanently banned the Res from the securities business.

It turned out that the president of the Amex, Edward T. McCormick, was on the S.E.C.’s list of Re associates. This implied that the Amex, or at least its chief, knew what was going on all along.

McCormick, who held a master’s degree from the University of California and a PhD from Duke, had started working for the S.E.C. in 1934. In 1951, he left his post as S.E.C. commissioner to become head of the Amex. Brooks notes that this sort of talent drain had been the bane of the S.E.C. from its beginnings. Brooks says:

The scholar and bureaucrat had turned out to be a born salesman. But with the Amex’s growth, it began to appear toward the end of the decade, a certain laxness of administration had crept in. Restless at his desk, Ted McCormick was always out selling up-and-coming companies on listing their shares on the Amex, and while he was in Florida or at the Stork Club drumming up trade, sloppy practices were flourishing back at Trinity Place.

Many didn’t notice the Res’ misdeeds. And it seemed that those who knew didn’t care. However, a father-and-son-in-law team, David S. Jackson and Andrew Segal, were greatly disturbed.

Jackson had seen McCormick change over the years:

…Jackson had watched McCormick gradually changing from a quiet, reflective man into a wheeler-dealer who loved to be invited by big businessmen to White Sulphur Springs for golf, and the change worried him. “Ted,” he would say, when they were at dinner at one or the other’s house, “why don’t you read any more?”

“I haven’t got time,” McCormick would reply.

“But you’ll lose your perspective,” Jackson would protest, shaking his head.

Jackson and Segal eventually concluded that McCormick was not fit to be the president of Amex. Jackson met with McCormick to tell him he should resign. McCormick reacted violently, picking up a stack of papers and slamming them on to his desk, and then punching a wall of his office.

McCormick told Jackson that he had never done anything dishonest.

“No, I don’t think you have,” Jackson said, his voice shaking. “But you’ve been indiscreet.”

Roughly a dozen members of Amex, mostly under forty and nicknamed the Young Turks, sided with Jackson and Segal in calling for McCormick’s resignation. However, they were greatly outnumbered and they were harrassed and threatened.

One Young Turk, for example, was pointedly reminded of a questionable stock transaction in which he had been involved some years earlier, and of how easily the matter could be called to the S.E.C.’s attention; to another it was suggested that certain evidence at hand, if revealed, could make a shambles of his pending suit for divorce; and so on.

Soon Jackson and Segal were practically alone. Then something strange happened. The S.E.C. chose to question Jackson about an incident in which one of Jackson’s assistants, years earlier, had done a bad job of specializing. The S.E.C. was led by its top investigators, Ralph Saul, David Silver, and Edward Jaegerman. They questioned Jackson for hours with what seemed to be hostility, scorn, and sarcasm.

Jackson went home and started writing a letter to send to various public officials to complain about his poor treatment by the S.E.C. Jackson read the letter aloud over the phone to Ralph Saul, who was horrified. Saul apologized, asked Jackson not to send the letter, and said that amends would be made.

The S.E.C. sent a team to watch the Jackson and Segal operation, trade by trade. The S.E.C. concluded that Jackson and Segal were honest and asked them to become allies in the reform of the Amex. Jackson and Segal agreed.

McCormick eventually was forced to resign.

 

THE LAST GATSBY

Brooks tells the story of Edward M. Gilbert:

From the first, he was a bright but lazy student with a particular aptitude for mathematics, a talented and fanatical athlete, and something of a spoiled darling…

Matriculating at Cornell in the early stages of World War II, he made a name for himself in tennis and boxing, won the chess championship of his dormitory, and earned a reputation as a prankster, but went on neglecting his studies.

Gilbert enlisted in the Army Air Force and worked for Army newspapers. He demonstrated a talent for acquiring foreign languages.

After the war, Gilbert joined his father’s company.

During this period of his business apprenticeship he embarked on a series of personal ventures that were uniformly unsuccessful. He backed a prizefighter who turned out to be a dud. He was co-producer of a Broadway play,How Long Till Summer? that starred the black folksinger Josh White’s son… [but the play] got disastrous notices and closed [after a week.] Gilbert also dabbled in the stock market without any notable success.

Edward Gilbert’s father, Harry Gilbert, became a multi-millionaire when his company, Empire Millwork, sold stock to the public. Brooks:

He was ever ready to use his money to indulge his son, and over the years he would do so again and again… Never a corporate rainmaker, Harry Gilbert, humanly enough, yearned to appear vital, enterprising, and interesting to his friends and colleagues. The son’s deals and the electric office atmosphere they created were made possible by the father’s money. Doubtless the father on occasion did not even understand the intricate transactions his son was forever proposing–debentures and takeovers and the like. But to admit it would be to lose face… And so, again and again, he put up the money. Harry Gilbert bought commercial glamour from his son.

Brooks explains that, in 1948, Eddie Gilbert began dreaming of enlarging Empire Millwork through mergers. In 1951, he asked his father for a directorship. But Harry Gilbert turned him down. So Eddie quit and entered the hardwood-floor business on his own.

There were two versions of what happened next. In one version, Eddie Gilbert was successful and Empire bought him out in 1962. In the other version, Eddie tried and failed to corner the hardwood-floor market, and Harry bought him out to bury the big mistake. Brooks writes:

At any rate, in 1955 Eddie returned to Empire with new power and freedom to act.

Eddie wanted to buy E. L. Bruce and Company, the country’s leading hardwood-floor company.

With net sales of around $25 million a year, Bruce was considerably larger than Empire, but it was a staid firm, conservatively managed and in languid family control, of the sort that is the classic prey for an ambitious raider. In 1955, Eddie Gilbert persuaded his father to commit much of his own and the company’s resources in an attempt to take over Bruce.

Now Eddie came into his own at last. He began to make important friends in Wall Street–brokers impressed with his dash and daring, and delighted to have the considerable commissions he generated. Some of his friends came from the highest and most rarified levels of finance.

Brooks continues:

In his early thirties, a short, compact man with pale blue eyes and a sort of ferret face under thinning hair, Gilbert had a direct personal charm that compensated for his vanity and extreme competitiveness. Sometimes his newfound friends patronized him behind his back, laughing at his social pretensions and his love of ostentation, but they continued going to his parties and, above all, following his market tips. Some accused him of being a habitual liar; they forgave him because he seemed genuinely to believe his lies, especially those about himself and his past. He was a compulsive gambler–but, endearingly, a very bad one; on lucky streaks he would double bets until he lost all his winnings, or draw to inside straights for huge sums at poker, or go for broke on losing streaks; yet at all times he seemed to take large losses in the best of humor.

Eddie urged his new friends as well as his family to buy Bruce stock, which was selling around $25 a share.

All that spring, the Gilberts and their relatives and Eddie’s friends accumulated the stock, until in June it had reached the seventies and was bouncing up and down from day to day and hour to hour in an alarming way. What was in the process of developing in Bruce stock was the classically dangerous, sometimes disastrous situation called a corner.

Bruce family management had realized that a raid was developing, so they were buying as much stock as they could. At the same time, speculators began shorting the stock on the belief that the stock price would fall. Shorting involved borrowing shares and selling them, and later buying them back. The short sellers would profit if they bought it back at a price lower than where they sold it. However, they would lose money if they bought it back at a price higher than where they sold it.

The problem for short sellers was that Eddie’s friends and family, and Bruce family management, ended up owning all available shares of stock. Brooks:

The short sellers were squeezed; if called upon to deliver the stock they had borrowed and then sold, they could not do so, and those who owned it were in a position to force them to buy back what they owed at a highly inflated price.

Short sellers bought what little stock was available, sending the price up to 188.

Eddie Gilbert, coming out of the fray in the fall of 1958, seemed to have arrived at last–apparently paper-rich from his huge holdings of high-priced Bruce stock, rich in the esteem of his society backers, nationally famous from the publicity attendant on the corner he had brought about.

Gilbert was self-indulgent with his new wealth, for instance, keeping a regular Monday box at the Metropolitan Opera. Brooks:

He acquired a huge Fifth Avenue apartment and, when and as he could, filled it with French antiques, a fortune in generally almost-first-rate paintings, and a staff of six. Sometimes he lived in a mansion at Palm Beach, epitome of Real Society in faded turn-of-the-century photographs. He took an immense villa at Cap Martin on the French Riviera, where he mingled when he could with Maria Callas and Aristotle Onassis and their like, and gave huge outdoor parties with an orchestra playing beside an Olympic-size swimming pool.

Brooks explains that Gilbert was not genuinely rich:

His paper profits were built on borrowing, and he was always mortgaged right up to the hilt; to be thus mortgaged, and to remain so, was all but an article of faith with him… He was habitually so pressed for cash that on each January first he would draw his entire $50,000 empire salary for the coming year in a lump sum in advance. By the summer of 1960 he was in bad financial trouble. Empire National stock was down, Gilbert’s brokers were calling for additional margin, and Gilbert was already in debt all over New York. He owed large sums to dozens of art dealers… But he hung on gamely; when friends advised him at least to liquidate the art collection, he refused. To sell it, he explained, would be to lose face.

However, Gilbert was saved when Bruce stock increased sharply. This led Gilbert to want to have Bruce acquire Celotex Corporation, a large manufacturer of building-insulation materials. Gilbert acquired as much Celotex stock as he could. He put his friends and family into Celotex. Even his old enemies the Bruce family authorized Gilbert’s use of $1.4 million of the company’s money to buy Celotex shares.

But then Gilbert’s fortunes reversed again. The stock market started to go sour. Moreover, Gilbert’s marriage was on the rocks. Gilbert moved to Las Vegas in order to stay there the 6-week period required for a Nevada divorce.

Gilbert kept his residence in Las Vegas as much of a secret as he could. The few people from Bruce who were allowed to know where Gilbert was were sworn to secrecy.

While in Vegas, Gilbert would be up at dawn, since the markets opened at 7:00 A.M. Nevada time. In the afternoons, Gilbert went to the casinos to gamble. He later admitted that his gambling losses were heavy.

Meanwhile, the stock market continued to decline. Eddie Gilbert was in trouble. Most of Eddie’s friends–who held Celotex on margin–were also in trouble.

Gilbert himself had all but exhausted his borrowing power. His debts to brokers, to friends, to Swiss bankers, to New York loan sharks on the fringes of the underworld, all loomed over him, and the market betrayed him daily by dropping even more.

Brooks writes:

The third week of May became for Gilbert a nightmare of thwarted pleas by telephone–pleas to lenders for new loans, pleas to brokers to be patient and not sell him out, pleas with friends to stick with him just a little longer. But it was all in vain, and in desperation that same week Gilbert took the old, familiar, bad-gambler’s last bad gamble–to avoid the certainty of bankruptcy he risked the possibility of criminal charges. Gilbert ordered an official of Bruce to make out checks drawn on the Bruce treasury to a couple of companies called Rhodes Enterprises and Empire Hardwood Flooring, which were actually dummies for Gilbert himself, and he used the proceeds to shore up his personal margin calls. The checks amounted to not quite $2 million; the act amounted to grand larceny.

Gilbert hoped that the prices of Bruce and Celotex would rise, allowing him to repay Bruce for the improper loan. But Gilbert had a premonition that the stock prices of Bruce and Celotex were about to tumble more. Gilbert later told The New York Times:

“I suddenly knew that I couldn’t get through this without getting hurt and getting innocent people hurt.”

Gilbert was right, as the prices of Bruce and Celotex collapsed on what turned out to be Blue Monday, the Stock Exchange’s second worse day of the century thus far. Bruce fell to 23, down 9 3/8, while Celotex fell to 25, down 6. In total, Gilbert lost $5 million on Blue Monday. Furthermore, many of Gilbert’s friends who’d followed his advice also had huge losses.

Gilbert realized that if he could find a block buyer for his Celotex shares, that might allow him to repay loans, especially the improper loan from Bruce. But Gilbert was unable to find such a buyer. So Eddie did the last thing he felt he could–he fled to Brazil, which had no effective extradition treaty with the United States.

Suddenly Gilbert returned to the United States, despite federal and state charges against him that carried penalties adding up to 194 years in prison. Gilbert’s father had hired Arnold Bauman, a New York criminal lawyer, who had told Gilbert that he could return to the U.S. if he promised to implicate other wrongdoers. Gilbert never fulfilled these promises, however, and he ended up spending a bit over two years in prison.

Before going to prison, Gilbert was free on bail for four and a half years. During that time, with more money form his father, Gilbert started and ran a new business, the Northerlin Company, flooring brokers. He was successful for a time, allowing him to begin repaying loans. But again he was too aggressive, and he had to sell the Northerlin Company for a tax loss.

 

PALMY DAYS AND LOW RUMBLINGS

Brooks explains how William Lucius Cary came to be appointed as chairman of the S.E.C.:

A strong Report on Regulatory Agencies to the President Elect, commissioned by the President-elect himself and written late in 1960 by James M. Landis, who had been an S.E.C. chairman in New Deal days, showed that Kennedy was bent on bringing the S.E.C. back to life, and it set the stage for the Cary regime. Landis called for more funds as well as greater regulatory zeal, and Kennedy and Congress implemented the Landis conclusions with practical backing; between 1960 and 1964, the S.E.C.’s annual appropriation increased from $9.5 million to almost $14 million and its payroll from fewer than one thousand persons to almost fifteen hundred. But the change was not only quantitative. Cary concentrated on recruiting talented and enthusiastic lawyers, devoting perhaps a third of his time to the task. His base supply naturally enough consisted of his former students and their friends; the atmosphere… soon changed from one of bureaucratic somnolence to one of academic liberal activism.

Brooks gives background on Cary:

Cary in 1962 was a lawyer of fifty-one with the gentlemanly manner and the pixyish countenance of a New England professor. A late-starting family man, he had two children who were still tots; his wife, Katherine, was a great-great-granddaughter of America’s first world-famous novelist, James Fenimore Cooper. His reputation among his colleagues of the bar was, as one of them put it, for “sweetness of temperament combined with fundamental toughness of fibre.”…He had grown up in and around Columbus, the son of a lawyer and president of a small utility company; he had graduated from Yale and then from Yale Law, practiced law a couple of years in Cleveland, then done a long stretch in federal government–first as a young S.E.C. assistant counsel, later as an assistant attorney general in the tax division of the Justice Department, then as an Office of Strategic Services cloak-and-dagger functionary in wartime Roumania and Yugoslavia. In 1947 he had entered academic life, teaching law thereafter, first at Northwestern and later at Columbia. He was in the latter post, taking one day a week off to go downtown to the “real world” of Wall Street and practice law with the firm of Patterson, Belknap and Webb, when John F. Kennedy appointed him S.E.C. chairman soon after assuming the Presidency in January 1961.

Brooks then states:

Two actions during his first year in office gave the financial district an inkling of Cary’s mettle and the S.E.C.’s new mood.

The first case wasIn the Matter of Cady, Roberts and Co., which related to events that occurred two years before. A young broker of Cady, Roberts and Co., Robert M. Gintel, had received information that Curtiss-Wright Corporation was about to seriously cut its quarterly dividend. Gintel immediately sold 7,000 shares for his firm’s customers. This violated Rule 10B-5 of the S.E.C. against trading based on privileged information. Gintel was suspended from trading for twenty days. It seemed like a light sentence.

But so firmly entrenched was the Wall Street tradition of taking unfair advantage of the larger investing public, and so lax the S.E.C.’s administration of that particular part of the law between 1942 and 1961, that not a single stockbroker had ever been prosecuted for improper use of privileged information during those two decades.

The second action led by Cary involved a two-year Special Study of the securities markets. The study was released in three parts.

Specifically, the first installment said that insider-trading rules should be tightened; standards of character and competence for stockbrokers should be raised; further curbs should be put on the new-issues market; and S.E.C. surveillance should be extended to the thousands of small-company stocks traded over the counter that had previously been free of federal regulation…

The second part of the study… concentrated on stock-exchange operations, recommending that brokers’ commissions on trades be lowered, that the freedom of action of specialists be drastically curtailed, and that floor traders–those exchange members who play the market with their own money on the floor itself, deriving from their membership the unique advantages over nonmembers of being at the scene of action and of paying no commissions to brokers–be legislated right out of existence through the interdiction of their activities.

Brooks continues:

The third and final part… was probably the harshest of the three–and in view of political realities the most quixotic. Turning its attention to the wildly growing mutual-fund business, the S.E.C. now recommended outlawing of the kind of contract, called “front-end load,” under which mutual-fund buyers agreed (and still agree) to pay large sales commissions off the top of their investment. It also accused the New York Stock Exchange of leaning toward “tenderness rather than severity” in disciplining those of its members who have broken its rules.

Brooks comments:

All in all, the Special Study was a blueprint for a fair and orderly securities market, certainly the most comprehensive such blueprint ever drawn up, and if all of its recommendations had been promptly put into effect, what follows in this chronicle’s later chapters would be a different tale. But, of course, they were not.

Brooks explains:

The law that was finally passed–the Securities Acts Amendments of 1964–had two main sections, one extending S.E.C. jurisdiction to include some twenty-five hundred over-the-counter stocks (about as many as were traded on the New York and American exchanges combined), and the other giving the government authority to set standards and qualifications for securities firms and their employees.

As far as it went, it was a good law, a landmark law, a signal achievement for Cary and his egghead crew. But it fell far short of what the Special Study had asked for. Not a word, for example, about mutual-fund abuses; no new restrictions on the activities of specialists; and nothing to alter the Stock Exchange’s habit of “tenderness” toward its erring members. Those items had been edited out in the course of the political compromises that had made passage of the bill possible.

The “bitterest pill of all,” writes Brooks, was that the floor traders continued to be allowed to trade for their own accounts using privileged or inside information. The Special Study had asked that such trading be outlawed. But there were very strong objections from the Stock Exchange and then from business in general. Their arguments referred to the freedom of the marketplace and also the welfare of the investing public. The Stock Exchange commissioned the management firm of Cresap, McCormick and Paget to study the issue and determine if floor trading served the public or not.

Brooks observes:

Built into this situation was one of those moral absurdities that are so dismayingly common in American business life. The Stock Exchange, largely run by floor traders and their allies, had a vested interest in finding that floor traders serve a socially useful purpose. Cresap, McCormick and Paget, being on the Exchange payroll, had a vested interest in pleasing the Exchange…

Cresap, McCormick and Paget labored mightily. One may imagine the Exchange’s gratification when the report, finished at last, concluded that abolition of floor trading would decrease liquidity and thereby introduce a dangerous new volatility into Stock Exchange trading, doing “irreparable farm” to the free and fair operation of the auction market. But perhaps the Exchange’s gratification was less than complete. The magisterial authority of the report was somewhat sullied when James Dowd, head of the Cresap team that had compiled it, stated publicly that his actual finding had been that floor trading was far from an unmixed blessing for the public, and accused the Stock Exchange of having tampered with the report before publishing it… Cary wanted to hold S.E.C. hearings on the matter, but was voted down by his fellow commissioners.

At all events, the report as finally published did not seem to be a triumph of logical thought.

Brooks concludes:

Thus frustrated, Cary’s S.E.C. came to achieve through administration much of what it had failed to achieve through legislation.

In August 1964, the S.E.C. issued strict new rules requiring Stock Exchange members to pass an exam before being permitted to be floor traders. As well, each floor trader had to submit daily a detailed report of his or her transactions.

Shortly after imposition of the new rules, the number of floor traders on the Stock Exchange dropped from three hundred to thirty. As an important factor in the market, floor trading was finished. Cary had won through indirection.

 

NORTHERN EXPOSURE

There were hardly any blacks or women on Wall Street in the 1960’s. Brooks:

Emancipated, highly competent and successful women in other fields–the arts, publishing, real estate, retail trade–still found it consistent with their self-esteem to affect a coy bewilderment when conversation turned to the stock market or the intricacies of finance.

Brooks continues:

Liberal Democrats, many of them Jewish, were about as common as conservative Republicans in the positions of power; now, one of them, Howard Stein of Dreyfus Corporation, would be the chief fund-raiser for Eugene McCarthy’s 1968 presidential campaign…

Many of the men putting together the stock market’s new darlings, the conglomerates, were liberals–and, of course, it didn’t hurt a Wall Street analyst or salesman to be on close and sympathetic terms with such men. There were even former Communists high in the financial game.

Between 1930 and 1951, very few young people went to work on Wall Street. Brooks writes:

Indeed, by 1969, half of Wall Street’s salesmen and analysts would be persons who had come into the business since 1962, and consequently had never seen a bad market break. Probably the prototypical portfolio hotshot of 1968 entered Wall Street precisely in 1965… Portfolio management had the appeal of sports–that one cleanly wins or loses, the results are measurable in numbers; if one’s portfolio was up 30 or 50 percent for a given year one was a certified winner, so recognized and so compensated regardless of whether he was popular with his colleagues or had come from the right ancestry or the right side of the tracks.

Brooks describes:

It was open season now on Anglo-Saxon Protestants even when they stayed plausibly close to the straight and narrow. Their sins, or alleged sins, which had once been so sedulously covered up by press and even government, were now good politics for their opponents. They had become useful as scapegoats–as was perhaps shown in the poignant personal tragedy of Thomas S. Lamont. Son of Thomas W. Lamont, the Morgan partner who may well have been the most powerful man in the nation in the nineteen twenties, “Tommy” Lamont was an amiable, easygoing man. He was a high officer of the Morgan Guaranty Trust Company and a director of Texas Gulf Sulphur Company, and on the morning–April 16, 1964–when Texas Gulf publicly announced its great Timmins ore strike, he notified one of his banking colleagues of the good news at a moment when, although he had reason to believe that it was public knowledge, by the S.E.C.’s lights in fact it was not. The colleague acted quickly and forcefully on Lamont’s tip, on behalf of some of the bank’s clients; then, almost two hours later, when news of the mine was unquestionably public, Lamont bought Texas Gulf stock for himself and his family.

Lamont had known for several days earlier, and had done nothing. And when he informed his colleague about the Timmins ore strike, he believed that the information was already public knowledge. According to the S.E.C., however, the insider trading rule also required one to wait “a reasonable amount of time,” so that the news could be digested. Brooks:

In so doing, it lumped [Lamont] with flagrant violators, some Texas Gulf geologists and executives who had bought stock on the strength of their knowledge of Timmins days and months earlier, and who made up the bulk of the S.E.C.’s landmark insider case of 1966.

Could it be, then, that the S.E.C. knew well enough that it had a weak case against Lamont, and dragged him into the suit purely for the publicity value of his name? The outlandishness of the charge against him, and the frequency with which his name appeared in newspaper headlines about the case, suggest such a conclusion.

In the end, all charges against Lamont were dropped, while virtually no charges against the other defendants were dropped. Unfortunately, before this happened, Lamont’s health had declined and he had passed away.

Brooks continues:

The Texas Gulf ore strike at Timmins in early 1964 had dramatically shown Canada to United States investors as the new Golconda. Here was a great, undeveloped land with rich veins of dear metals lying almost untouched under its often-frozen soil; with stocks in companies that might soon be worth millions selling for nickels or dimes on Bay Street, the Wall Street of Toronto; and with no inconvenient Securities and Exchange Commission on hand to monitor the impulsiveness of promoters or cool the enthusiasm of investors. American money flowed to Bay Street in a torrent in 1964 and early in 1965, sending trading volume there to record heights and severely overtaxing the facilities of the Toronto Stock Exchange. Copies ofThe Northern Miner, authoritative gossip sheet of the Canadian mining industry, vanished from south-of-the-border newsstands within minutes of their arrival; some Wall Street brokers, unwilling to wait for their copies, had correspondents in Toronto telephone them theMiner‘s juicier items the moment it was off the press. And why not? Small fortunes were being made almost every week by quick-acting U.S. investors on new Canadian ore strikes, or even on rumors of strikes. It was as if the vanished western frontier, with its infinite possibilities both spiritual and material had magically reappeared, with a new orientation ninety degrees to the right of the old one.

The Canadian economy in general was growing fast along with the exploitation of the nation’s mineral resources, and among the Canadian firms that had attracted the favorable attention of U.S. investors, long before 1964, was Atlantic Acceptance Corporation, Ltd., a credit firm, specializing in real-estate and automobile loans, headed by one Campbell Powell Morgan, a former accountant with International Silver Company of Canada, with an affable manner, a vast fund of ambition, and, it would appear later, a marked weakness for shady promoters and a fatal tendency toward compulsive gambling.

In 1955, two years after founding Atlantic, Morgan sought to raise money from Wall Street at a time when some on Wall Street thought they could make profits in Toronto. Morgan knew Alan T. Christie, another Canadian. Christie was a partner in “the small but rising Wall Street concern of Lambert and Company.”

At Christie’s recommendation, Lambert and Company in 1954 put $300,000 into Atlantic Acceptance, thereby becoming Atlantic’s principal U.S. investor and chief booster in Wall Street and other points south.

Brooks again:

The years passed and Atlantic seemed to do well, its annual profits steadily mounting along with its volume of loans. Naturally, it constantly needed new money to finance its continuing expansion. Lambert and Company undertook to find the money in the coffers of U.S. investing institutions; and Jean Lambert, backed by Christie, had just the air of European elegance and respectability, spiced with a dash of mystery, to make him perfectly adapted for the task of impressing the authorities of such institutions.

Christie first contacted Harvey E. Mole, Jr., head of the U.S. Steel and Carnegie Pension Fund. Brooks:

Christie made the pitch for Steel to invest in Atlantic. Mole, born in France but out of Lawrenceville and Princeton, was no ramrod-stiff traditional trustee type; rather, he fancied himself, not without reason, as a money manager with a component of dash and daring. Atlantic Acceptance was just the kind of relatively far-out, yet apparently intrinsically sound, investment that appealed to Mole’s Continental sporting blood. The Steel fund took a bundle of Atlantic securities, including subordinate notes, convertible preferred stock, and common stock, amounting to nearly $3 million.

The following year, Lambert and Company convinced the Ford Foundation to invest in Atlantic Acceptance. Brooks comments:

After that, it was easy. With the kings of U.S. institutional investing taken into camp, the courtiers could be induced to surrender virtually without a fight. Now Lambert and Company could say to the fund managers, “If this is good enough for U.S. Steel and the Ford Foundation, how can you lose?” “We were all sheep,” one of them would admit, sheepishly, years later. Before the promotion was finished, the list of U.S. investors in Atlantic had become a kind of Burke’s Peerage of American investing institutions: the Morgan Guaranty and First National City Banks; the Chesapeake and Ohio Railway; the General Council of Congregational Churches; Pennsylvania and Princeton Universities (perhaps not coincidentally, the man in charge of Princeton’s investment program was Harvey Mole); and Kuhn, Loeb and Company, which, to the delight of Lambert, gave the enterprise its valuable imprimatur by taking over as agent for the sale of Atlantic securities in the United States. Perhaps the final turn of the screw, as the matter appears in hindsight, is the fact that the list of Atlantic investors eventually included Moody’s Investors Service, whose function is to produce statistics and reports designed specifically to help people avoid investment pitfalls of the sort of which Atlantic would turn out to be an absolutely classic case.

In the early 1960’s, Atlantic’s sales were increasing nearly 100 percent per year. It seemed that the company was exceeding what anyone could have expected. Of course, in the loan business, you can increase volumes significantly by making bad loans that are unlikely to be repaid. Brooks:

In fact, that was precisely what Atlantic was doing, intentionally and systematically.

However, having such investors as the Steel fund, the Ford Foundation, etc., made it easy to dismiss critics.

Late in 1964, Atlantic, hungry for capital as always, sold more stock; and early in 1965, Kuhn, Loeb helped place $8.5 million more in Atlantic long-term debt with U.S. institutional investors. By this time, Lambert and Company’s stake in Atlantic amounted to $7.5 million. The firm’s commitment was a do-or-die matter; it would stand or fall with Atlantic. Moreover, it is now clear that by this time Morgan and his associates were engaged in conducting a systematic fraud on a pattern not wholly dissimilar to that of Ponzi or Ivar Kreuger. Atlantic would use the new capital flowing from Wall Street to make new loans that its major officers knew to be unsound; the unsoundness would be deliberately camouflaged in the company’s reports, in order to mislead investors; the spurious growth represented by the ever-increasing loans would lure in new investment money, with which further unsound loans would be made; and so on and on. Morgan had taken to intervening personally each year in the work of his firm’s accountants–some of whom were willing enough to commit fraud at their client’s request–to ensure that a satisfactory rise in profits was shown through overstatement of assets and understatement of allowances for bad debts. For 1964, it would come out later, Atlantic’s announced $1.4 million profit, under proper accounting procedure, should have been reported as a loss of $16.6 million.

Brooks continues:

The game, like all such games, could not go on forever. By early 1965, suspicion of Atlantic’s operations was in the wind. In April, the New York Hanseatic Corporation, a $12-million investor in Atlantic paper, asked the Toronto-Dominion Bank for a credit check on Atlantic. The response–which in retrospect appears dumbfounding–was favorable. In fact, if the bank had been able to penetrate the mystifications of Powell’s accountants, it would have discovered that Atlantic was by that time actually insolvent. For several years, at the instigation of some of the various international schemers for whom Morgan had a fatal affinity, the firm had been increasingly involved in a desperate and doomed plunge in a shaky venture far from home: between 1963 and early 1965 it had committed more than $11 million to the Lucayan Beach, a hotel with a gambling casino attached, on balmy, distant Grand Bahama Island. A Royal Commission would later describe the investment as “the last throw of the dice to retrieve all the losses created by years of imprudence and impropriety.” But the Lucayan Beach venture, managed incompetently and fraudulently, did not flourish, and the losses were not to be retrieved.

On June 15, Atlantic went into default. It needed $25 million to cure the situation. “Of course, it neither had not could raise such a sum.” Brooks comments:

The Old Establishment of U.S. investing had fallen for its own fading mystique. Believing, with tribal faith that can only be called touching, that no member of the club could make a serious mistake, the members had followed each other blindly into the crudest of traps, and had paid the price for their folly.

Brooks concludes: “…the Atlantic episode neatly divides Wall Street’s drama of the decade, ending the first act, and beginning the second and climactic one.”

 

THE BIRTH OF GO-GO

Brooks defines the term “go-go” as a method of investing:

The method was characterized by rapid in-and-out trading of huge blocks of stock, with an eye to large profits taken very quickly, and the term was used specifically to apply to the operation of certain mutual funds, none of which had previously operated in anything like such a free, fast, or lively manner.

The mood and the method seem to have started, of all places, in Boston, the home of the Yankee trustee. The handling of other people’s money in the United States began in Boston, the nation’s financial center until after the Civil War. Trusteeship is by its nature conservative–its primary purpose being to conserve capital–and so indeed was the type of man it attracted in Boston. Exquisitely memorialized in the novels of John P. Marquand, for a century the Boston trustee was the very height of unassailable probity and sobriety: his white hair neatly but not too neatly combed; his blue Yankee eyes untwinkling, at least during business hours; the lines in his cheeks running from his nose to the corners of his mouth forming a reassuringly geometric isoceles triangle; his lips touching liquor only at precisely set times each day, and then in precise therapeutic dosage; his grooming impeccable (his wildest sartorial extravagance a small, neat bow tie) with a single notable exception–that he wore the same battered gray hat through his entire adult life, which, so life-preserving was his curriculum, seldom ended before he was eighty-five or ninety.

Brooks writes about the Boston-born “prudent man rule.” In 1830, Justice Samuel Putnam of the Supreme Judicial Court of Massachusetts wrote in a famous opinion:

All that can be required of a Trustee to invest is that he conduct himself faithfully and exercise a sound discretion. He is to observe how men of prudence, discretion, and intelligence manage their own affairs, not in regard to speculation, but in regard to permanent disposition of their funds, considering the probable income, as well as the probable safety of the capital to be invested.

Brooks then writes that Boston, in 1924, was the location of “another epoch-making innovation in American money management, the founding of the first two mutual funds, Massachusetts Investors Trust and State Street Investing Company.” Later, after World War II, “the go-go cult quietly originated hard by Beacon Hill under the unlikely sponsorship of a Boston Yankee named Edward Crosby Johnson II.” Brooks describes Johnson:

Although never a trustee by profession, Johnson was almost the Boston-trustee type personified.

Brooks adds:

The market bug first bit him in 1924 when he read a serialization in the old Saturday Evening Post of Edwin Lefevre’s “Reminiscences of a Stock Market Operator,” the story of the career of the famous speculator Jesse Livermore. “I’ll never forget the thrill,” he told a friend almost a half century later. “Everything was there, or else implied. Here was the picture of a world in which it was every man for himself, no favors asked or given. You were what you were, not because you were a friend of somebody, but for yourself. And Livermore–what a man, always betting his whole wad! A sure system for losing, of course, but the point was how much he loved it. Operating in the market, he was like Drake sitting on the poop of his vessel in a cannonade. Glorious!”

Eventually Johnson was asked to take over Fidelity Fund, a mutual fund with only $3 million under management. Brooks comments:

Edward Crosby Johnson II, for all of his trustee-like ways, clearly had a speculative background and temperament; after all, his stock-market idol was one of the master speculators.

What this meant in practice was that Fidelity was willing to trade in and out of stocks, often fairly rapidly, rather than buy and hold.

Then in 1952, Johnson met Gerald Tsai, Jr. Johnson, liking the young man’s looks, first hired Tsai as a junior stock analyst. Tsai was born in Shanghai in 1928 to Westernized Chinese parents. Tsai’s father had been educated at the University of Michigan, and was Shanghai district manager for the Ford Motor Company. Tsai himself got a BA and MA in economics from Boston University. Brooks:

“I liked the market,” he would explain years later. “I felt that being a foreigner I didn’t have a competitive disadvantage there, when I might somewhere else. If you buy GM at forty and it goes to fifty, whether you are an Oriental, a Korean, or a Buddhist doesn’t make any difference.”

Tsai’s reasons for liking the market were similar to Johnson’s reasons: “you were what you were not because you were a friend of somebody, but for yourself.” Brooks continues:

At Fidelity, Tsai was not long in making his mark. Always impeccably groomed, his moon face as impassive as a Buddha, he showed himself to be a shrewd and decisive picker of stocks for shot-term appreciation…

Tsai explained later that Johnson gave you your head–a chance to work on your own rather than as part of a committee–but he simultaneously gave you your rope, saying “God ahead and hang yourself with it.” Tsai also quoted Johnson as saying, “Do it by yourself. Two men can’t play a violin.”

Soon Tsai asked Johnson if he could launch a speculative growth fund. Johnson said yes in the space of an hour, saying, “Go ahead. Here’s your rope.”

Tsai’s rope was called Fidelity Capital Fund, and it was the company’s first frankly speculative public growth fund. Right from the start, he operated it in a way that was at the time considered almost out-and-out gambling. He concentrated Fidelity Capital’s money in a few stock that were then thought to be outrageously speculative and unseasoned for a mutual fund (Polaroid, Xerox, and Litton Industries among them).

Brooks notes:

As once “Jesse Livermore is buying it!” had been the signal for a general stampede into any stock, so now it was “Gerry Tsai is buying it!” Like Livermore’s, his prophecies by force of his reputation came to be to a certain extent self-fulfilling.

Brooks also writes about the invention of the “hedge fund,” so named because, unlike a mutual fund, a hedge fund could operate on margin and make short sales. Brooks describes the man who invented the hedge fund:

He was Alfred Winslow Jones, no sideburned gunslinger but a rather shy, scholarly journalist trained in sociology and devoted to good works. Born in Australia at the turn of the century to American parents posted there by General Electric, he graduated from Harvard in 1923, got a Ph.D. in sociology from Columbia, served in the foreign service in Berlin during the thirties, and became a writer for Time-Life in the forties.

In 1949, Winslow got the idea for a hedge fund. He raised $100,000 in investment capital, $40,000 of it his. The first hedge fund did well, even in the bad market of 1962 because of its capacity to sell short. Its clients were mainly writers, teachers, scholars, social workers. (One client was Sam Stayman, the bridge expert.) Winslow’s fund showed a five-year gain of 325 percent and a ten-year gain of more than twice that amount. The fund took 20 percent of profits as an annual fee.

Brooks adds:

Alfred Jones, in his own middle sixties, had made so much money out of A. W. Jones and Company’s annual 20 percents that he could well afford to indulge his predilections. Spending less and less time at his office on Broad Street, he devoted himself more and more to a personal dream of ending all poverty. Considering material deprivation in the land of affluence to be a national disgrace, he set up a personal foundation devoted to mobilizing available social skills against it… Jones could afford to go the way of the aristocrat, treating money-making as something too simple to be taken very seriously, and putting his most profound efforts into work not in the cause of profit but in that of humanity.

In late 1965, Tsai left Fidelity and formed his own mutual fund, the Manhattan Fund. Tsai set out to raise $25 million, but he ended up raising $247 million. The only problem for Tsai was that the bull market had peaked. Investors expected Tsai to make 50 percent a year, but he could only do so if the bull market continued. Brooks comments:

But if Tsai no longer seemed to know when to cash in the investments he made for others, he knew when to cash in his own. In August, 1968, he sold Tsai Management and Research to C.N.A. Financial Corporation, an insurance holding company, in exchange for a high executive post with C.N.A. and C.N.A. stock worth in the neighborhood of $30 million.

 

THE CONGLOMERATEURS

Brooks defines the term:

Derived from the Latin wordglomus, meaning wax, the word suggests a sort of apotheosis of the old Madison Avenue cliche “a big ball of wax,” and is no doubt apt enough; but right from the start, the heads of conglomerate companies objected to it. Each of them felt thathis company was a mesh of corporate and managerial genius in which diverse lines of endeavor–producing, say, ice cream, cement, and flagpoles–were subtly welded together by some abstruse metaphysical principle so refined as to be invisible to the vulgar eye. Other diversified companies, each such genius acknowledged, were conglomerates; but not his own.

In 1968, 26 of the country’s 500 biggest companies disappeared through conglomerate merger. Some of the largest targets were acquired by companies much smaller than themselves. Moreover, enthusiasts were saying that eventually 200 super-conglomerates would be doing most of the national business. There would only be a handful of non-conglomerates left. Brooks observes:

The movement was new and yet old. In the nineteenth century, few companies diversified their activities very widely by acquiring other companies or by any other means. There is, on the face of it, no basic reason for believing that a man who can successfully run an ice cream business should not be able to successfully run an ice-cream-and-cement business, or even an ice-cream-cement-and-flagpole business. On the other hand, there is no reason for believing that heshould be able to do so. In the Puritan and craft ethic that for the most part ruled nineteenth-century America, one of the cardinal precepts was that the shoemaker should stick to his last.

Brooks notes that it was during the 1950’s that “really uninhibited diversification first appeared. Brooks:

During that decade, National Power and Light, as a result of its purchase of another company, found itself chiefly engaged in peddling soft drinks; Borg-Warner, formerly a maker of automotive parts, got into refrigerators, other consumer products; and companies like Penn-Texas and Merritt Chapman and Scott, under the leadership of corporate wild men like David Carr and Louis E. Wolfson, took to ingesting whatever companies swam within reach. Among the first companies to be called conglomerates were Litton, which in 1958 began to augment its established electronics business with office calculators and computers and later branched out into typewriters, cash registers, packaged foods, conveyor belts, oceangoing ships, solder, teaching aids, and aircraft guidance systems, and Textron, once a placid and single-minded New England textile company, and eventually a purveyor of zippers, pens, snowmobiles, eyeglass frames, silverware, golf carts, metalwork machinery, helicopters, rocket engines, ball bearings, and gas meters.

Brooks lists the factors involved in the conglomerate explosion:

• • corporate affluence
  • a decline of the stick-to-your-last philosophy among businessmen
  • a decline of the stick-to-anything philosophy among almost everyone else
  • a rise in the influence of graduate business schools, led by Harvard, which in the 1960’s were trying to enshrine business as a profession, and often taught that managerial ability was an absolute quality, not limited by the type of business being managed
  • federal antitrust laws, which forbade most mergers between large companies in thesame line of business

One additional factor was that many investors focused on just one metric: the price-to-earnings (P/E) ratio. Brooks clarifies why this isn’t a reliable guide when investing in a conglomerate: when a company with a high P/E buys a company with a low P/E, earnings per share increases. Brooks:

There is an apparent growth in earnings that is entirely an optical illusion. Moreover, under accounting procedures of the late nineteen sixties, a merger could generally be recorded in either of two ways–as a purchase of one company by another, or as a simple pooling of the combined resources. In many cases, the current earnings of the combined company came out quite differently under the two methods, and it was understandable that the company’s accountants were inclined to choose arbitrarily the method that gave the more cheerful result. Indeed, the accountant, through this choice and others as his disposal, was often able to write for the surviving company practically any current earnings figure he chose–a situation that impelled one leading investment-advisory service to issue a derisive bulletin entitled, “Accounting as a Creative Art.”

Brooks continues:

The conglomerate game tended to become a form of pyramiding… The accountant evaluating the results of a conglomerate merger would apply his creative resources by writing an earnings figure that looked good to investors; they, reacting to the artistry, would buy the company’s stock, thereby forcing its market price up to a high multiple again; the company would then make the new merger, write new higher earnings, and so on.

 

THE ENORMOUS BACKROOM

Brooks writes:

Nineteen sixty-eight was to be the year when speculation spread like a prairie fire–when the nation, sick and disgusted with itself, seemed to try to drown its guilt in a frenetic quest for quick and easy money. “The great garbage market,” Richard Jenrette called it–a market in which the “leaders” were neither old blue chips like General Motors and American Telephone nor newer solid starts like Polaroid and Xerox, but stock with names like Four Seasons Nursing Centers, Kentucky Fried Chicken, United Convalescent Homes, and Applied Logic. The fad, as in 1961, was for taking short, profitable rides on hot new issues.

As trading volume increased, back-office troubles erupted. Brooks:

The main barometric measuring-device for the seriousness of back-office trouble was the amount of what Wall Street calls “fails.” A fail, which might more bluntly be called a default, occurs when on the normal settlement date for any stock trade–five days after the transaction–the seller’s broker for some reason does not physically deliver the actual sold stock certificates to the buyer’s broker, or the buyer’s broker for some reason fails to receive it.

The reasons for fails typically are that either the selling broker can’t find the certificates being sold, the buying broker misplaces them, or one side or the other makes a mistake in record-keeping by saying that the stock certificates have not been delivered when in fact they have been.

Lehman Brothers, in particular, was experiencing a high level of fails.

Stock discrepancies at the firm, by the end of May, ran into hundreds of millions of dollars. Lehman reacted by eliminating a few accounts, ceasing the make markets in over-the-counter stocks, and refusing further orders for low-priced securities; it did not augment these comparatively mild measures with drastic ones–the institution of a crash program costing half a million dollars to eliminate stock record errors–until August, when the S.E.C. threatened to suspend Lehman’s registration as a broker-dealer and thus effectively put it out of business. Lehman’s reluctance to act promptly to save its customers’ skins, and ultimately its own, was all too characteristic of Wall Street’s attitude toward its troubles in 1968.

Brooks comments:

Where were the counsels of restraint, not to say common sense, in both Washington and on Wall Street? The answer seems to lie in the conclusion that in America, with its deeply imprinted business ethic, no inherent stabilizer, moral or practical, is sufficiently strong in and of itself to support the turning away of new business when competitors are taking it on. As a people, we would rather face chaos making potsfull of short-term money than maintain long-term order and sanity by profiting less.

 

GO-GO AT HIGH NOON

Brooks on New York City in 1968:

Almost all of the great cultural centers of history have first been financial centers. This generalization, for which New York City provides a classic example, is one to be used for purposes of point-proving only with the greatest caution. To conclude from it that financial centers naturally engender culture would be to fall into the most celebrated of logical fallacies. It is nonetheless a suggestive fact, and particularly so in the light of 1968 Wall Street, standing as it was on the toe of the same rock that supported Broadway, off-Broadway, Lincoln Center, the Metropolitan Museum, the Museum of Modern Art, and Greenwich Village.

Brooks then writes about changes on Wall Street:

Begin with the old social edifices that survived more or less intact. In many instances they were Wall Street’s worst and most dispensable; for example, its long-held prejudices, mitigated only by tokenism, against women and blacks.

A few women–but not many–were reaching important positions. Meanwhile, for blacks, Wall Street “had advanced the miniscule distance from the no-tokenism of 1965 to tokenism at the end of the decade.”

 

CONFRONTATION

Brooks writes:

Spring of 1969–a time that now seems in some ways part of another, and a more romantic, era–was in the business world a time of Davids and Goliaths: of threatened takeovers of venerable Pan American World Airways by upstart Resorts International, for example, and of venerable Goodrich Tire and Rubber by upstart Northwest Industries… Undoubtedly, though, the David-and-Goliath act of early 1969 that most caught the popular imagination was an attempt upon the century-and-a-half-old Chemical Bank New York Trust Company (assets a grand $9 billion) by the eight-year-old Leasco Data Processing Equipment Corporation of Great Neck, Long Island (assets a mere $400 million), a company entirely unknown to almost everyone in the larger business community without a special interest in either computer leasing, Leasco’s principal business until 1968, or in the securities market, in which its stock was a star performer. In that takeover contest, the roles of Goliath and David were played, with exceptional spirit, by William Shryock Renchard of the Chemical and Saul Phillip Steinberg of Leasco.

Renchard was from Trenton, New Jersey, and although he attended Princeton, he probably was not expected to amount to much. That is, he didn’t stand out at all at Princeton and his senior yearbook said, “Renchard is undecided as to his future occupation.”

By 1946, at the age of thirty-eight, Renchard was a vice president of Chemical Bank and Trust Company. In 1955, he became executive vice president; in 1960, he was made president; and in 1966, he was made chairman of the board of what was now called Chemical Bank New York Trust Company. By that time, the bank had $9 billion in assets and was the country’s six largest commercial bank.

Saul Phillip Steinberg was from Brooklyn and was a full generation younger than Renchard. Steinberg was unexceptional, although he did develop an early habit of readingThe Wall Street Journal. He attended the Wharton School of Finance and Commerce at the University of Pennsylvania.

Steinberg researched I.B.M., expecting to find negative things. Instead, he learned that I.B.M. was a brilliantly run company. He also concluded that I.B.M.’s industrial computers would last longer than was assumed. So he started a business whereby he purchased I.B.M. computers and then leased them out for longer terms and for lower rates than I.B.M. itself was offering.

In 1964, two years after launching his business–Ideal Leasing Company–earnings were $255,000 and revenues were $8 million. Steinberg then decided to go public, and the company’s name was changed to Leasco Data Processing Equipment Corporation. Public sale of Leasco stock brought in $750,000. Leasco’s profits skyrocketed: 1967 profits were more than eight times 1966 profits. Brooks:

As might be expected of a young company with ambition, a voracious need for cash, and a high price-to-earnings multiple, Leasco became acquisition-minded… In 1966 and 1967, Leasco increased its corporate muscle by buying several small companies in fields more or less related to computers or to leasing… These acquisitions left the company with $74 million in assets, more than eight hundred employees, larger new headquarters in Great Neck, Long Island, and a vast appetite for further growth through mergers.

Diversified companies learned that if they acquired or merged with a fire-and-casualty company, then the otherwise restricted cash reserves–”redundant capital”–of the fire-and-casualty company could be put to use. Thus, Leasco got the idea of acquiring Reliance Insurance Company, a Philadelphia-based fire-and-casualty underwriter “with more than five thousand employees, almost $350 million in annual revenues, and a fund of more than $100 million in redundant capital.” Brooks writes:

Truly–to change the metaphor–it was a case of the minnow swallowing the whale; Reliance was nearly ten times Leasco’s size, and Leasco, as the surviving company, found itself suddenly more than 80 percent in the insurance business and less than 20 percent in the computer-leasing business.

Brooks adds:

[Leasco] suddenly had assets of $400 million instead of $74 million, net annual income of $27 million instead of $1.4 million, and 8,500 employees doing business in fifty countries instead of 800 doing business in only one.

Brooks notes that Leasco’s stock had, over the previous five years, increased 5,410 percent making Leasco “the undisputed king of all the go-go stocks.” Now comes the story of Leasco and Chemical Bank.

Leasco had gotten interested in acquiring a bank. Banks often sold at low price-to-earnings ratio’s, giving Leasco leverage in a takeover. Also, Steinberg thought “that it would be advantageous to anchor Leasco’s diversified financial services to a New York money-center bank with international connections.” By the fall of 1968, Leasco was zeroing in on Renchard’s $9-billion Chemical Bank.

Leasco had begun buying shares in Chemical and had prepared a hypothetical tender offer involving warrants and convertible debentures when Chemical learned of Leasco’s intended takeover. Brooks:

…Renchard was in no doubt as to Chemical’s response. He and his bank were going to fight Leasco with all their strength. True enough, a merger, as in the Reliance case, would result in immediate financial benefit to the stockholders of both companies. But it seemed to Renchard and his colleagues that more than immediate stockholder profit was involved. The century-and-a-half-old Chemical Bank a mere division of an unseasoned upstart called Leasco?

Renchard organized an eleven-man task force to come up with a strategy for fighting off any takeover attempt. Renchard commented later:

“We were guessing that they would offer stuff with a market value of around $110 for each share of our stock, which was then selling at $72. So we knew well enough it would be tough going persuading our stockholders not to accept.”

First, Renchard leaked the story toThe New York Times. TheTimes published a piece that included the following:

Can a Johnny-come-lately on the business scene move in on the Establishment and knock off one of the biggest prizes in sight?

[…]

Is Chemical in the bag? Hardly. William S. Renchard, chairman of the Chemical Bank, sounded like a Marine Corps colonel in presenting his battle plan…

One strategy Chemical came up with was to attack the value of Leasco stock by selling it or shorting it. This approach was discussed at a February 6 strategy meeting, but no one afterwards was ever willing to admit it. Brooks:

The striking and undeniable fact is, however, that on that very day, Leasco stock, which had been hovering in the stratosphere at around 140, abruptly began to fall in price on large trading volume. By the close the following day Leasco was down almost seven points, and over the following three weeks it would drop inexorably below 100.

Chemical planned a full-scale strategy meeting:

At the Chemical strategy meeting–which was attended, this time, not only by Chemical’s in-house task force, but by invitees from other powerful Wall Street institutions sympathetic to the Chemical cause, including First Boston, Kuhn Loeb, and Hornblower Weeks–a whole array of defensive measures were taken up and thrashed out, among them the organizing of telephone teams to contact Chemical stockholders; the retaining of the leading proxy-soliciting firms solely to deny their services to Leasco; and the possibility of getting state and federal legislation introduced through the bankers’ friends in Albany and Washington in order to make a Leasco takeover of Chemical illegal. Despite the availability of such weapons, the opinion of those present seemed to be that Leasco’s venture had an excellent chance of success.

Finally, Renchard and Steinberg met for lunch. Brooks writes:

One may imagine the first reactions of the antagonists to each other. One was lean, iron-gray, of distinctly military bearing; a North Shore estate owner, very conscious of the entrenched power of the nation standing behind him, very much a man of few and incisive words. The other was round-faced, easy-smiling, a man of many words who looked preposterously younger than his already preposterous twenty-nine years, and given, as he talked, to making windmill gestures with his arms and suddenly jumping galvanically up from his chair; aSouth Shore estate owner…; a young man bubbling with energy and joy in living.

During the meeting, Steinberg said he wanted it to be a friendly takeover. Renchard seemed to be open to that possibility. At the same time, Renchard said that he was “a pretty good gutter fighter,” to which Steinberg replied that his own record as a gutter fighter “was considered to be pretty good, too.”

A second meeting was held. This time, Renchard and Steinberg brought their chief aides. Steinberg put more emphasis on his friendly intentions, and he conceded that he would be willing to not be the chief executive of the merged entity. Renchard said they had lots to consider and would get back in touch shortly.

Then Chemical held another full-scale battle meeting at which they considered several possible options. They thought about changing their company’s charter to make a Leasco takeover legally difficult if not impossible. They floated the idea of buying a fire-and-casualty company to create an antitrust conflict with Leasco’s ownership of Reliance. They even talked about arranging to have a giant insurance company take over Chemical. Brooks notes:

Probably the most effective of Chemical’s various salvos was on the legislative front… Richard Simmons of the Cravath law firm, on retainer from Chemical, began devoting full time to the Leasco affair, concentrating his attention on the drafting of laws specifically designed to prevent or make difficult the takeover of banks similar to Chemical by companies that resembled Leasco, and getting these drafts introduced as bills in the State Legislature in Albany and the Congress in Washington.

Simmons’ anti-bank-takeover bill was introduced in Albany and was passed. Moreover, aWall Street Journal article questioned Leasco’s earnings prospects. As well, the Department of Justice sent a letter to Leasco raising the possibility that the proposed takeover might violate antitrust laws. In truth, the proposed takeover did not violate antitrust laws. How the Justice Department came to send such a letter has never been explained, observes Brooks.

At this point, Steinberg decided to abandon the effort to merge with Chemical. Brooks quotes Steinberg:

“Nobody was objective… bankers and businessmen I’d never met kept calling up out of the blue and attacking us for merely thinking about taking over a big bank. Some of the attacks were pretty funny–responsible investment bankers taking as if we were using Mafia tactics.. Months after we’d abandoned our plans, executives of major corporations were still calling up and ranting, ‘I feel it was so wrong, what you tried to do–’ And yet they could never say why… I still don’t know exactly what it was.”

 

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time. See the historical chart here: https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps. Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals. We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio. The size of each position is determined by its rank. Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost). Positions are held for 3 to 5 years unless a stock approachesintrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods. We also aim to outpace the Russell Microcap Index by at least 2% per year (net). The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: [email protected]

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed. No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

August 7, 2022

If we’re more aware of cognitive biases today than a decade or two ago, that’s thanks in large part to the research of the Israeli psychologists Daniel Kahneman and Amos Tversky. I’ve written about cognitive biases before, including:

• Cognitive Biases:https://boolefund.com/cognitive-biases/
• The Psychology of Misjudgment:https://boolefund.com/the-psychology-of-misjudgment/

I’ve seen few books that do a good job covering the work of Kahneman and Tversky.The Undoing Project: A Friendship That Changed Our Minds, by Michael Lewis, is one such book. (Lewis also writes well about the personal stories of Kahneman and Tversky.)

Why are cognitive biases important? Economists, decision theorists, and others used to assume that people are rational. Sure, people make mistakes. But many scientists believed that mistakes are random: if some people happen to make mistakes in one direction–estimates that are too high–other people will (on average) make mistakes in the other direction–estimates that are too low. Since the mistakes are random, they cancel out, and so the aggregate results in a given market will nevertheless be rational. Markets are efficient.

For some markets, this is still true. Francis Galton, the English Victorian-era polymath, wrote about a contest in which 787 people guessed at the weight of a large ox. Most participants in the contest were not experts by any means, but ordinary people. The ox actually weighed 1,198 pounds. The average guess of the 787 guessers was 1,197 pounds, which was more accurate than the guesses made by the smartest and the most expert guessers. The errors are completely random, and so they cancel out.

This type of experiment can easily be repeated. For example, take a jar filled with pennies, where only you know how many pennies are in the jar. Pass the jar around in a group of people and ask each person–independently (with no discussion)–to write down their guess of how many pennies are in the jar. In a group that is large enough, you will nearly always discover that the average guess is better than any individual guess. (That’s been the result when I’ve performed this experiment in classes I’ve taught.)

However, in other areas, people do not make random errors, but systematic errors. This is what Kahneman and Tversky proved using carefully constructed experiments that have been repeated countless times. In certain situations, many people will tend to make mistakes in the same direction–these mistakes do not cancel out. This means that the aggregate results in a given market can sometimes be much less than fully rational. Markets can be inefficient.

Outline (based on chapters from Lewis’s book):

• Introduction
• Man Boobs
• The Outsider
• The Insider
• Errors
• The Collision
• The Mind’s Rules
• The Rules of Prediction
• Going Viral
• Birth of the Warrior Psychologist
• The Isolation Effect
• This Cloud of Possibility

(Illustration by Alain Lacroix)

 

INTRODUCTION

In his 2003 book,Moneyball, Lewis writes about the Oakland Athletic’s efforts to find betters methods for valuing players and evaluating strategies. By using statistical techniques, the team was able to perform better than many others teams even though the A’s had less money. Lewis says:

A lot of people saw in Oakland’s approach to building a baseball team a more general lesson: If the highly paid, publicly scrutinized employees of a business that had existed since the 1860s could be misunderstood by their market, what couldn’t be? If the market for baseball players was inefficient, what market couldn’t be? If a fresh analytical approach had led to the discovery of new knowledge in baseball, was there any sphere of human activity in which it might not do the same?

After the publication ofMoneyball, people started applying statistical techniques to other areas, such as education, movies, golf, farming, book publishing, presidential campaigns, and government. However, Lewis hadn’t asked the question of what it was about the human mind that led experts to be wrong so often. Why were simple statistical techniques so often better than experts?

The answer had to do with the structure of the human mind. Lewis:

Where do the biases come from? Why do people have them? I’d set out to tell a story about the way markets worked, or failed to work, especially when they were valuing people. But buried somewhere inside it was another story, one that I’d left unexplored and untold, about the way the human mind worked, or failed to work, when it was forming judgments and making decisions. When faced with uncertainty–about investments or people or anything else–how did it arrive at its conclusions? How did it process evidence–from a baseball game, an earnings report, a trial, a medical examination, or a speed date? What were people’s minds doing–even the minds of supposed experts–that led them to the misjudgments that could be exploited for profit by others, who ignored the experts and relied on data?

 

MAN BOOBS

Daryl Morey, the general manager of the Houston Rockets, used statistical methods to make decisions, especially when it came to picking players for the team. Lewis:

His job was to replace one form of decision making, which relied upon the intuition of basketball experts, with another, which relied mainly on the analysis of data. He had no serious basketball-playing experience and no interest in passing himself off as a jock or basketball insider. He’d always been just the way he was, a person who was happier counting than feeling his way through life. As a kid he’d cultivated an interest in using data to make predictions until it became a ruling obsession.

Lewis continues:

If he could predict the future performance of professional athletes, he could build winning sports teams… well, that’s where Daryl Morey’s mind came to rest. All he wanted to do in life was build winning sports teams.

Morey found it difficult to get a job for a professional sports franchise. He concluded that he’d have to get rich so that he could buy a team and run it. Morey got an MBA, and then got a job consulting. One important lesson Morey picked up was that part of a consultant’s job was to pretend to be totally certain about uncertain things.

There were a great many interesting questions in the world to which the only honest answer was, ‘It’s impossible to know for sure.’… That didn’t mean you gave up trying to find an answer; you just couched that answer in probabilistic terms.

Leslie Alexander, the owner of the Houston Rockets, had gotten disillusioned with the gut instincts of the team’s basketball experts. That’s what led him to hire Morey.

Morey built a statistical model for predicting the future performance of basketball players.

A model allowed you to explore the attributes in an amateur basketball player that led to professional success, and determine how much weight should be given to each.

The central idea was that the model would usually give you a “better” answer than relying only on expert intuition. That said, the model had to be monitored closely because sometimes it wouldn’t have important information. For instance, a player might have had a serious injury right before the NBA draft.

(Illustration by fotomek)

Statistical and algorithmic approaches to decision making are more widespread now. But back in 2006 when Morey got started, such an approach was not at all obvious.

In 2008, when the Rocket’s had the 33rd pick, Morey’s model led him to select Joey Dorsey. Dorsey ended up not doing well at all. Meanwhile, Morey’s model had passed over DeAndre Jordan, who ended up being chosen 35th by the Los Angeles Clippers. DeAndre Jordan ended up being the second best player in the entire draft, after Russell Westbrook. What had gone wrong? Lewis comments:

This sort of thing happened every year to some NBA team, and usually to all of them. Every year there were great players the scouts missed, and every year highly regarded players went bust. Morey didn’t think his model was perfect, but he also couldn’t believe that it could be so drastically wrong.

Morey went back to the data and ended up improving his model. For example, the improved model assigned greater weight to games played against strong opponents than against weak ones. Lewis adds:

In the end, he decided that the Rockets needed to reduce to data, and subject to analysis, a lot of stuff that had never before been seriously analyzed: physical traits. They needed to know not just how high a player jumped but how quickly he left the earth–how fast his muscles took him into the air. They needed to measure not just the speed of the player but the quickness of his first two steps.

At the same time, Morey realized he had to listen to his basketball experts. Morey focused on developing a process that relied both on the model and on human experts. It was a matter of learning the strengths and weaknesses of the model, as well as the strengths and weaknesses of human experts.

But it wasn’t easy. By letting human intuition play a role, that opened the door to more human mistakes. In 2007, Morey’s model highly valued the player Marc Gasol. But the scouts had seen a photo of Gasol without a shirt. Gasol was pudgy with jiggly pecs. The Rockets staff nicknamed Gasol “Man Boobs.” Morey allowed this ridicule of Gasol’s body to cause him to ignore his statistical model. The Rockets didn’t select Gasol. The Los Angeles Lakers picked him 48th. Gasol went on to be a two-time NBA All-Star. From that point forward, Morey banned nicknames because they could interfere with good decision making.

Over time, Morey developed a list of biases that could distort human judgment: confirmation bias, the endowment effect, present bias, hindsight bias, et cetera.

 

THE OUTSIDER

Although Danny Kahneman had frequently delivered a semester of lectures from his head, without any notes, he nonetheless always doubted his own memory. This tendency to doubt his own mind may have been central to his scientific discoveries in psychology.

But there was one experience he had while a kid that he clearly remembered. In Paris, about a year after the Germans occupied the city, new laws required Jews to wear the Star of David. Danny didn’t like this, so he wore his sweater inside out. One evening while going home, he saw a German soldier with a black SS uniform. The soldier had noticed Danny and picked him up and hugged him. The soldier spoke in German, with great emotion. Then he put Danny down, showed him a picture of a boy, and gave him some money. Danny remarks:

I went home more certain than ever that my mother was right: people were endlessly complicated and interesting.

Another thing Danny remembers is when his father came home after being in a concentration camp. Danny and his mother had gone shopping, and his father was there when they returned. Despite the fact that he was extremely thin–only ninety-nine pounds–Danny’s father had waited for them to arrive home before eating anything. This impressed Danny. A few years later, his father got sick and died. Danny was angry.

Over time, Danny grew even more fascinated by people–why they thought and behaved as they did.

When Danny was thirteen years old, he moved with his mother and sister to Jerusalem. Although it was dangerous–a bullet went through Danny’s bedroom–it seemed better because they felt they were fighting rather than being hunted.

On May 14, 1948, Israel declared itself a sovereign state. The British soldiers immediately left. The armies from Jordan, Syria, and Egypt–along with soldiers from Iraq and Lebanon–attacked. The war of independence took ten months.

Because he was identified as intellectually gifted, Danny was permitted to go to university at age seventeen to study psychology. Most of his professors were European refugees, people with interesting stories.

Danny wasn’t interested in Freud or in behaviorism. He wanted objectivity.

The school of psychological thought that most charmed him was Gestalt psychology. Led by German Jews–its origins were in the early twentieth century Berlin–it sought to explore, scientifically, the mysteries of the human mind. The Gestalt psychologists had made careers uncovering interesting phenomena and demonstrating them with great flair: a light appeared brighter when it appeared from total darkness; the color gray looked green when it was surrounded by violet and yellow if surrounded by blue; if you said to a person, “Don’t step on the banana eel!,” he’d be sure that you had said not “eel” but “peel.” The Gestalists showed that there was no obvious relationship between any external stimulus and the sensation it created in people, as the mind intervened in many curious ways.

(Two faces or a vase? Illustration by Peter Hermes Furian)

Lewis continues:

The central question posed by Gestalt psychologists was the question behaviorists had elected to ignore: How does the brain create meaning? How does it turn the fragments collected by the senses into a coherent picture of reality? Why does the picture so often seem to be imposed by the mind upon the world around it, rather than by the world upon the mind? How does a person turn the shards of memory into a coherent life story? Why does a person’s understanding of what he sees change with the context in which he sees it?

In his second year at Hebrew Univeristy, Danny heard a fascinating talk by a German neurosurgeon. This led Danny to abandon psychology in order to pursue a medical degree. He wanted to study the brain. But one of his professors convinced him it was only worth getting a medical degree if he wanted to be a doctor.

After getting a degree in psychology, Danny had to serve in the Israeli military. The army assigned him to the psychology unit, since he wasn’t really cut out for combat. The head of the unit at that time was a chemist.Danny was the first psychologist to join.

Danny was put in charge of evaluating conscripts and assigning them to various roles in the army. Those applying to become officers had to perform a task: to move themselves over a wall without touching it using only a log that could not touch the wall or the ground. Danny and his coworkers thought that they could see “each man’s true nature.” However, when Danny checked how the various soldiers later performed, he learned that his unit’s evaluations–with associated predictions–were worthless.

Danny compared his unit’s delusions to theM¼ller-Lyer optical illusion. Are these two lines the same length?

(M¼ller-Lyer optical illusion by Gwestheimer, Wikimedia Commons)

The eye automatically sees one line as longer than the other even though the lines have equal length. Even after you use a ruler to show the lines are equal, the illusion persists. If we’re automatically fooled in such a simple case, what about in more complex cases?

Danny thought up a list of traits that seemed correlated with fitness for combat. However, Danny was concerned about how to get an accurate measure of these traits from an interview. One problem was the halo effect: If people see that a person is strong, they tend to see him as impressive in other ways. Or if people see a person as good in certain areas, then they tend to assume that he must be good in other areas. More on the halo effect:https://boolefund.com/youre-deluding-yourself/

Danny developed special instructions for the interviewers. They had to ask specific questions not about how subjects thought of themselves, but rather about how they actually had behaved in the past. Using this information, before moving to the next question, the interviewers would rate the subject from 1 to 5. Danny’s essential process is still used in Israel today.

 

THE INSIDER

To his fellow Israelis, Amos Tversky somehow was, at once, the most extraordinary person they had ever met and the quintessential Israeli. His parents were among the pioneers who had fled Russian anti-Semitism in the early 1920s to build a Zionist nation. His mother, Genia Tversky, was a social force and political operator who became a member of the first Israeli Parliament, and the next four after that. She sacrificed her private life for public service and didn’t agonize greatly about the choice…

Amos was raised by his father, a veterinarian who hated religion and loved Russian literature, and who was amused by things people say:

…His father had turned away from an early career in medicine, Amos explained to friends, because “he thought animals had more real pain than people and complained a lot less.” Yosef Tversky was a serious man. At the same time, when he talked about his life and work, he brought his son to his knees with laughter about his experiences, and about the mysteries of existence.

Although Amos had a gift for math and science–he may have been more gifted than any other boy–he chose to study the humanities because he was fascinated by a teacher, Baruch Kurzweil. Amos loved Kurzweil’s classes in Hebrew literature and philosophy. Amos told others he was going to be a poet or literary critic.

Amos was small but athletic. During his final year in high school, he volunteered to become an elite soldier, a paratrooper. Amos made over fifty jumps. Soon he was made a platoon commander.

By late 1956, Amos was not merely a platoon commander but a recipient of one of the Israeli army’s highest awards for bravery. During a training exercise in front of the General Staff of the Israeli Defense Forces, one of his soldiers was assigned to clear a barbed wire fence with a bangalore torpedo. From the moment he pulled the string to activate the fuse, the soldier had twenty seconds to run for cover. The soldier pushed the torpedo under the fence, yanked the string, fainted, and collapsed on top of the explosive. Amos’s commanding officer shouted for everyone to stay put–to leave the unconscious soldier to die. Amos ignored him and sprinted from behind the wall that served as cover for his unit, grabbed the soldier, picked him up, hauled him ten yards, tossed him on the ground, and threw himself on top of him. The shrapnel from the explosion remained in Amos for the rest of his life. The Israeli army did not bestow honors for bravery lightly. As he handed Amos his award, Moshe Dayan, who had watched the entire episode, said, “You did a very stupid and brave thing and you won’t get away with it again.”

Amos was a great storyteller and also a true genius. Lewis writes about one time when Tel Aviv University threw a party for a physicist who had just won the Wolf Prize. Most of the leading physicists came to the party. But the prizewinner, by chance, ended up in a corner talking with Amos. (Amos had recently gotten interested in black holes.) The following day, the prizewinner called his hosts to find out the name of the “physicist” with whom he had been talking. They realized he had been talking with Amos, and told him that Amos was a psychologist rather than a physicist. The physicist replied:

“It’s not possible, he was the smartest of all the physicists.”

Most people who knew Amos thought that Amos was the smartest person they’d ever met. Moreover, he kept strange hours and had other unusual habits. When he wanted to go for a run, he’d just sprint out his front door and run until he could run no more. He didn’t pretend to be interested in whatever others expected him to be interested in. Rather, he excelled at doing exactly what he wanted to do and nothing else. He loved people, but didn’t like social norms and he would skip family vacation if he didn’t like the place. Most of his mail he left unopened.

People competed for Amos’s attention. As Lewis explains, many of Amos’s friends would ask themselves: “I know why I like him, but why does he like me?”

While at Hebrew University, Amos was studying both philosophy and psychology. But he decided a couple of years later that he would focus on psychology. He thought that philosophy had too many smart people studying too few problems, and some of the problems couldn’t be solved.

Many wondered how someone as bright, optimistic, logical, and clear-minded as Amos could end up in psychology. In an interview when he was in his mid-forties, Amos commented:

“It’s hard to know how people select a course in life. The big choices we make are practically random. The small choices probably tell us more about who we are. Which field we go into may depend upon which high school teacher we happen to meet. Who we marry may depend on who happens to be around at the right time of life. On the other hand, the small decisions are very systematic. That I became a psychologist is probably not very revealing. What kind of psychologist I am may depend upon deep traits.”

Amos became interested in decision making. While pursuing a PhD at the University of Michigan, Amos ran experiments on people making decisions involving small gambles. Economists had always assumed that people are rational. There were axioms of rationality that people were thought to follow, such as transitivity: if a person prefers A to B and B to C, then he must prefer A to C. However, Amos found that many people preferred A to B when considering A and B, B to C when considering B and C, and C to A when considering A and C. Many people violated transitivity. Amos didn’t generalize his findings at that point, however.

(Transitivity illustration by Thuluviel, Wikimedia Commons)

Next Amos studied how people compare things. He had read papers by the Berkeley psychologist Eleanor Rosch, who explored how people classified objects.

People said some strange things. For instance, they said that magenta was similar to red, but that red wasn’t similar to magenta. Amos spotted the contradiction and set out to generalize it. He asked people if they thought North Korea was like Red China. They said yes. He asked them if Red China was like North Korea–and they said no. People thought Tel Aviv was like New York but that New York was not like Tel Aviv. People thought that the number 103 was sort of like the number 100, but that 100 wasn’t like 103. People thought a toy train was a lot like a real train but that a real train was not like a toy train.

Amos came up with a theory, “features of similarity.” When people compare two things, they make a list of noticeable features. The more features two things have in common, the more similar they are. However, not all objects have the same number of noticeable features. New York has more than Tel Aviv.

This line of thinking led to some interesting insights:

When people picked coffee over tea, and tea over hot chocolate, and then turned around and picked hot chocolate over coffee–they weren’t comparing two drinks in some holistic manner. Hot drinks didn’t exist as points on some mental map at fixed distances from some ideal. They were collections of features. Those features might become more or less noticeable; their prominence in the mind depended on the context in which they were perceived. And the choice created its own context: Different features might assume greater prominence in the mind when the coffee was being compared to tea (caffeine) than when it was being compared to hot chocolate (sugar). And what was true of drinks might also be true of people, and ideas, and emotions.

 

ERRORS

Amos returned to Israel after marrying Barbara Gans, who was a fellow graduate student in psychology at the University of Michigan. Amos was now an assistant professor at Hebrew University.

Israel felt like a dangerous place because there was a sense that if the Arabs ever united instead of fighting each other, they could overrun Israel. Israel was unusual in how it treated its professors: as relevant. Amos gave talks about the latest theories in decision-making to Israeli generals.

Furthermore, everyone who was in Israel was in the army, including professors. On May 22, 1967, the Egyptian president Gamal Abdel Nasser announced that he was closing the Straits of Tiran to Israeli ships. Since most Israeli ships passed through the straits, Israel viewed the announcement as an act of war. Amos was given an infantry unit to command.

By June 7, Israel was in a war on three fronts against Egypt, Jordan, and Syria. In the span of a week, Israel had won the war and the country was now twice as big. 679 had died. But because Israel was a small country, virtually everyone knew someone who had died.

Meanwhile, Danny was helping the Israeli Air Force to train fighter pilots. He noticed that the instructors viewed criticism as more useful than praise. After a good performance, the instructors would praise the pilot and then the pilot would usually perform worse on the next run. After a poor performance, the instructors would criticize the pilot and the pilot would usually perform better on the next run.

Danny explained that pilot performance regressed to the mean. An above average performance would usually be followed by worse performance–closer to the average. A below average performance would usually be followed by better performance–again closer to the average. Praise and criticism had little to do with it.

Danny was brilliant, though insecure and moody. He became interested in several different areas in psychology. Lewis adds:

That was another thing colleagues and students noticed about Danny: how quickly he moved on from his enthusiasms, how easily he accepted failure. It was as if he expected it. But he wasn’t afraid of it. He’d try anything. He thought of himself as someone who enjoyed, more than most, changing his mind.

Danny read about research by Eckhart Hess focused on measuring the dilation and contraction of the pupil in response to various stimuli. People’s pupils expanded when they saw pictures of good-looking people of the opposite sex. Their pupils contracted if shown a picture of a shark. If given a sweet drink, their pupils expanded. An unpleasant drink caused their pupils to contract. If you gave people five slightly differently flavored drinks, their pupils would faithfully record the relative degree of pleasure.

People reacted incredibly quickly, before they were entirely conscious of which one they liked best. “The essential sensitivity of the pupil response,” wrote Hess, “suggests that it can reveal preferences in some cases in which the actual taste differences are so slight that the subject cannot even articulate them.”

Danny tested how the pupil responded to a series of tasks requiring mental effort. Does intense mental activity hinder perception? Danny found that mental effort also caused the pupil to dilate.

 

THE COLLISION

Danny invited Amos to come to his seminar, Applications in Psychology, and talk about whatever he wanted.

Amos was now what people referred to, a bit confusingly, as a “mathematical psychologist.” Nonmathematical psychologists, like Danny, quietly viewed much of mathematical psychology as a series of pointless exercises conducted by people who were using their ability to do math as camouflage for how little of psychological interest they had to say. Mathematical psychologists, for their part, tended to view nonmathematical psychologists as simply too stupid to understand the importance of what they were saying. Amos was then at work with a team of mathematically gifted American academics on what would become a three-volume, molasses-dense, axiom-filled textbook calledFoundations of Measurement–more than a thousand pages of arguments and proofs of how to measure stuff.

Instead of talking about his own research, Amos talked about a specific study of decision making and how people respond to new information. In the experiment, the psychologists presented people with two bags full of poker chips. Each bag contained both red poker chips and white poker chips. In one bag, 75 percent of the poker chips were white and 25 percent red. In the other bag, 75 percent red and 25 percent white. The subject would pick a bag randomly and, without looking in the bag, begin pulling poker chips out one at a time. After each draw, the subject had to give her best guess about whether the chosen bag contained mostly red or mostly white chips.

There was a correct answer to the question, and it was provided by Bayes’s theorem:

Bayes’s rule allowed you to calculate the true odds, after each new chip was pulled from it, that the book bag in question was the one with majority white, or majority red, chips. Before any chips had been withdrawn, those odds were 50:50–the bag in your hands was equally likely to be either majority red or majority white. But how did the odds shift after each new chip was revealed?

That depended, in a big way, on the so-called base rate: the percentage of red versus white chips in the bag… If you know that one bag contains 99 percent red chips and the other, 99 percent white chips, the color of the first chip drawn from the bag tells you a lot more than if you know that each bag contains only 51 percent red or white… In the case of the two bags known to be 75 percent-25 percent majority red or white, the odds that you are holding the bag containing mostly red chips rise by three times every time you draw a red chip, and are divided by three every time you draw a white chip. If the first chip you draw is red, there is a 3:1 (or 75 percent) chance that the bag you are holding is majority red. If the second chip you draw is also red, the odds rise to 9:1, or 90 percent. If the third chip you draw is white, they fall back to 3:1. And so on.

Were human beings good intuitive statisticians?

(Image by Honina, Wikimedia Commons)

Lewis notes that these experiments were radical and exciting at the time. Psychologists thought that they could gain insight into a number of real-world problems: investors reacting to an earnings report, political strategists responding to polls, doctors making a diagnosis, patients reacting to a diagnosis, coaches responding to a score, et cetera. A common example is when a woman is diagnosed with breast cancer from a single test. If the woman is in her twenties, it’s far more likely to be a misdiagnosis than if the woman is in her forties. That’s because the base rates are different: there’s a higher percentage of women in their forties than women in their twenties who have breast cancer.

Amos concluded that people do move in the right direction, however they usually don’t move nearly far enough. Danny didn’t think people were good intuitive statisticians at all. Although Danny was the best teacher of statistics at Hebrew University, he knew that he himself was not a good intuitive statistician because he frequently made simple mistakes like not accounting for the base rate.

Danny let Amos know that people are not good intuitive statisticians. Uncharacteristically, Amos didn’t argue much, except he wasn’t inclined to jettison the assumption of rationality:

Until you could replace a theory with a better theory–a theory that better predicted what actually happened–you didn’t chuck a theory out. Theories ordered knowledge, and allowed for better prediction. The best working theory in social science just then was that people were rational–or, at the very least, decent intuitive statisticians. They were good at interpreting new information, and at judging probabilities. They of course made mistakes, but their mistakes were a product of emotions, and the emotions were random, and so could be safely ignored.

Note: To say that the mistakes are random means that mistakes in one direction will be cancelled out by mistakes in the other direction. This implies that the aggregate market can still be rational and efficient.

Amos left Danny’s class feeling doubtful about the assumption of rationality. By the fall of 1969, Amos and Danny were together nearly all the time. Many others wondered at how two extremely different personalities could wind up so close. Lewis:

Danny was a Holocaust kid; Amos was a swaggering Sabra–the slang term for a native Israeli. Danny was always sure he was wrong. Amos was always sure he was right. Amos was the life of every party; Danny didn’t go to parties. Amos was loose and informal; even when he made a stab at informality, Danny felt as if he had descended from some formal place. With Amos you always just picked up where you left off, no matter how long it had been since you last saw him. With Danny there was always a sense you were starting over, even if you had been with him just yesterday. Amos was tone-deaf but would nevertheless sing Hebrew folk songs with great gusto. Danny was the sort of person who might be in possession of a lovely singing voice that he would never discover. Amos was a one-man wrecking ball for illogical arguments; when Danny heard an illogical argument, he asked,What might that be true of? Danny was a pessimist. Amos was not merely an optimist; Amoswilled himself to be optimistic, because he had decided pessimism was stupid.

Lewis later writes:

But there was another story to be told, about how much Danny and Amos had in common. Both were grandsons of Eastern European rabbis, for a start. Both were explicitly interested in how people functioned when there were in a normal “unemotional” state. Both wanted to do science. Both wanted to search for simple, powerful truths. As complicated as Danny might have been, he still longed to do “the psychology of single questions,” and as complicated as Amos’s work might have seemed, his instinct was to cut through endless bullshit to the simple nub of any matter. Both men were blessed with shockingly fertile minds.

After testing scientists with statistical questions, Amos and Danny found that even most scientists are not good intuitive statisticians. Amos and Danny wrote a paper about their findings, “A Belief in the Law of Small Numbers.” Essentially, scientists–including statisticians–tended to assume that any given sample of a large population was more representative of that population than it actually was.

Amos and Danny had suspected that many scientists would make the mistake of relying too much on a small sample. Why did they suspect this? Because Danny himself had made the mistake many times. Soon Amos and Danny realized that everyone was prone to the same mistakes that Danny would make. In this way, Amos and Danny developed a series of hypotheses to test.

 

THE MIND’S RULES

The Oregon Research Institute is dedicated to studying human behavior. It was started in 1960 by psychologist Paul Hoffman. Lewis observes that many of the psychologists who joined the institute shared an interest in Paul Meehl’s book,Clinical vs. Statistical Prediction. The book showed how algorithms usually perform better than psychologists when trying to diagnose patients or predict their behavior.

In 1986, thirty two years after publishing his book, Meehl argued that algorithms outperform human experts in a wide variety of areas. That’s what the vast majority of studies had demonstrated by then. Here’s a more recent meta-analysis:https://boolefund.com/simple-quant-models-beat-experts-in-a-wide-variety-of-areas/

In the 1960s, researchers at the institute wanted to build a model of how experts make decisions. One study they did was to ask radiologists how they determined if a stomach ulcer was benign or malignant. Lewis explains:

The Oregon researchers began by creating, as a starting point, a very simple algorithm, in which the likelihood that an ulcer was malignant depended on the seven factors the doctors had mentioned, equally weighted. The researchers then asked the doctors to judge the probability of cancer in ninety-six different individual stomach ulcers, on a seven-point scale from “definitely malignant” to “definitely benign.” Without telling the doctors what they were up to, they showed them each ulcer twice, mixing up the duplicates randomly in the pile so the doctors wouldn’t notice they were being asked to diagnose the exact same ulcer they had already diagnosed.

Initially the researchers planned to start with a simple model and then gradually build a more complex model. But then they got the results of the first round of questions. It turned out that the simple statistical model often seemed as good or better than experts at diagnosing cancer. Moreover, the experts didn’t agree with each other and frequently even contradicted themselves when viewing the same image a second time.

Next, the Oregon experimenters explicitly tested a simple algorithm against human experts: Was a simple algorithm better than human experts? Yes.

If you wanted to know whether you had cancer or not, you were better off using the algorithm that the researchers had created than you were asking the radiologist to study the X-ray. The simple algorithm had outperformed not merely the group of doctors; it had outperformed even the single best doctor.

(Algorithm illustration by Blankstock)

The strange thing was that the simple model was built on the factors that the doctors themselves had suggested as important. While the algorithm was absolutely consistent, it appeared that human experts were rather inconsistent, most likely due to things like boredom, fatigue, illness, or other distractions.

Amos and Danny continued asking people questions where the odds were hard or impossible to know. Lewis:

…Danny made the mistakes, noticed that he had made the mistakes, and theorized about why he had made the mistakes, and Amos became so engrossed by both Danny’s mistakes and his perceptions of those mistakes that he at least pretended to have been tempted to make the same ones.

Once again, Amos and Danny spent hour after hour after hour together talking, laughing, and developing hypotheses to test. Occasionally Danny would say that he was out of ideas. Amos would always laugh at this–he remarked later, “Danny has more ideas in one minute than a hundred people have in a hundred years.” When they wrote, Amos and Danny would sit right next to each other at the typewriter. Danny explained:

“We were sharing a mind.”

The second paper Amos and Danny did–as a follow-up on their first paper, “Belief in the Law of Small Numbers”–focused on how people actually make decisions. The mind typically doesn’t calculate probabilities. What does it do? It uses rules of thumb, or heuristics, said Amos and Danny. In other words, people develop mental models, and then compare whatever they are judging to their mental models. Amos and Danny wrote:

“Our thesis is that, in many situations, an event A is judged to be more probable than an event B whenever A appears more representative than B.”

What’s a bit tricky is that often the mind’s rules of thumb lead to correct decisions and judgments. If that weren’t the case, the mind would not have evolved this ability. For the same reason, however, when the mind makes mistakes because it relies on rules of thumb, those mistakes are not random, but systematic.

When does the mind’s heuristics lead to serious mistakes? When the mind is trying to judge something that has a random component. That was one answer. What’s interesting is that the mind can be taught the correct rule about how sample size impacts sampling variance; however, the mind rarely follows the correct statistical rule, even when it knows it.

For their third paper, Amos and Danny focused on theavailability heuristic. (The second paper had been about therepresentativeness heuristic.) In one question, Amos and Danny asked their subjects to judge whether the letter “k” is more frequently the first letter of a word or the third letter of a word. Most people thought “k” was more frequently the first letter because they could more easily recall examples where “k” was the first letter.

The more easily people can call some scenario to mind–the more available it is to them–the more probable they find it to be. An fact or incident that was especially vivid, or recent, or common–or anything that happened to preoccupy a person–was likely to be recalled with special ease and so be disproportionately weighted in any judgment. Danny and Amos had noticed how oddly, and often unreliably, their own minds recalculated the odds, in light of some recent or memorable experience. For instance, after they drove past a gruesome car crash on the highway, they slowed down: Their sense of the odds of being in a crash had changed. After seeing a movie that dramatizes nuclear war, they worried more about nuclear war; indeed, they felt that it was more likely to happen.

Amos and Danny ran similar experiments and found similar results. The mind’s rules of thumb, although often useful, consistently made the same mistakes in certain situations. It was similar to how the eye consistently falls for certain optical illusions.

Another rule of thumb Amos and Danny identified was theanchoring and adjustment heuristic. One famous experiment they did was to ask people to spin a wheel of fortune, which would stop on a number between 0 and 100, and then guess the percentage of African nations in the United Nations. The people who spun higher numbers tended to guess a higher percentage than those who spun lower numbers, even though the number spun was purely random and was irrelevant to the question.

 

THE RULES OF PREDICTION

For Amos and Danny, a prediction is a judgment under uncertainty. They observed:

“In making predictions and judgments under uncertainty, people do not appear to follow the calculus of chance or the statistical theory of prediction. Instead, they rely on a limited number of heuristics which sometimes yield reasonable judgments and sometimes lead to severe and systematic error.”

In 1972, Amos gave talks on the heuristics he and Danny had uncovered. In the fifth and final talk, Amos spoke about historical judgment, saying:

“In the course of our personal and professional lives, we often run into situations that appear puzzling at first blush. We cannot see for the life of us why Mr. X acted in a particular way, we cannot understand how the experimental results came out the way they did, etc. Typically, however, within a very short time we come up with an explanation, a hypothesis, or an interpretation of the facts that renders them understandable, coherent, or natural. The same phenomenon is observed in perception. People are very good at detecting patterns and trends even in random data. In contrast to our skill in inventing scenarios, explanations, and interpretations, our ability to assess their likelihood, or to evaluate them critically, is grossly inadequate. Once we have adopted a particular hypothesis or interpretation, we grossly exaggerate the likelihood of that hypothesis, and find it very difficult to see things in any other way.”

In one experiment, Amos and Danny asked students to predict various future events that would result from Nixon’s upcoming visit to China and Russia. What was intriguing was what happened later: If a predicted event had occurred, people overestimated the likelihood they had previously assigned to that event. Similarly, if a predicted event had not occurred, people tended to claim that they always thought it was unlikely. This came to be calledhindsight bias.

• A possible event that had occurred was seen in hindsight to be more predictable than it actually was.
• A possible event that had not occurred was seen in hindsight to be less likely that it actually was.

As Amos said:

All too often, we find ourselves unable to predict what will happen; yet after the fact we explain what did happen with a great deal of confidence. This “ability” to explain that which we cannot predict, even in the absence of any additional information, represents an important, though subtle, flaw in our reasoning. It leads us to believe that there is a less uncertain world than there actually is…

Experts from many walks of life–from political pundits to historians–tend to impose an imagined order on random events from the past. They change their stories to “explain”–and by implication, “predict” (in hindsight)–whatever random set of events occurred. This ishindsight bias, or “creeping determinism.”

Hindsight bias can create serious problems: If you believe that random events in the past are more predictable than they actually were, you will tend to see the future as more predictable than it actually is. You will be surprised much more often than you should be.

 

GOING VIRAL

Part of Don Redelmeier’s job at Sunnybrook Hospital (located in a Toronto suburb) was to check the thinking of specialists for mental mistakes. In North America, more people died every year as a result of preventable accidents in hospitals than died in car accidents. Redelmeier focused especially on clinical misjudgment. Lewis:

Doctors tended to pay attention mainly to what they were asked to pay attention to, and to miss some bigger picture. They sometimes failed to notice what they were not directly assigned to notice.

[…]

Doctors tended to see only what they were trained to see… A patient received treatment for something that was obviously wrong with him, from a specialist oblivious to the possibility that some less obvious thing might also be wrong with him. The less obvious thing, on occasion, could kill a person.

When he was only seventeen years old, Redelmeier had read an article by Kahneman and Tversky, “Judgment Under Uncertainty: Heuristics and Biases.” Lewis writes:

What struck Redelmeier wasn’t the idea that people make mistakes. Of course people made mistakes! What was so compelling is that the mistakes were predictable and systematic. They seemed ingrained in human nature.

One major problem in medicine is that the culture does not like uncertainty.

To acknowledge uncertainty was to admit the possibility of error. The entire profession had arranged itself as if to confirm the wisdom of its decisions. Whenever a patient recovered, for instance, the doctor typically attributed the recovery to the treatment he had prescribed, without any solid evidence the treatment was responsible… [As Redelmeier said:] “So many diseases are self-limiting. They will cure themselves. People who are in distress seek care. When they seek care, physicians feel the need to do something. You put leeches on; the condition improves. And that can propel a lifetime of leeches. A lifetime of overprescribing antibiotics. A lifetime of giving tonsillectomies to people with ear infections. You try it and they get better the next day and it is so compelling…”

One day, Redelmeier was going to have lunch with Amos Tversky. Hal Sox, Redelmeier’s superior, told him just to sit quietly and listen, because Tversky was like Einstein, “one for the ages.” Sox had coauthored a paper Amos had done about medicine. They explored how doctors and patients thought about gains and losses based upon how the choices were framed.

An example was lung cancer. You could treat it with surgery or radiation. Surgery was more likely to extend your life, but there was a 10 percent chance of dying. If you told people that surgery had a 90 percent chance of success, 82 percent of patients elected to have surgery. But if you told people that surgery had a 10 percent chance of killing them, only 54 percent chose surgery. In a life-and-death decision, people made different choices based not on the odds, but on how the odds were framed.

Amos and Redelmeier ended up doing a paper:

[Their paper] showed that, in treating individual patients, the doctors behaved differently than they did when they designed ideal treatments for groups of patients with the same symptoms. They were likely to order additional tests to avoid raising troubling issues, and less likely to ask if patients wished to donate their organs if they died. In treating individual patients, doctors often did things they would disapprove of if they were creating a public policy to treat groups of patients with the exact same illness…

The point was not that the doctor was incorrectly or inadequately treating individual patients. The point was that he could not treat his patient one way, and groups of patients suffering from precisely the same problem in another way, and be doing his best in both cases. Both could not be right.

Redelmeier pointed out that the facade of rationality and science and logic is “a partial lie.”

In late 1988 or early 1989, Amos introduced Redelmeier to Danny. One of the recent things Danny had been studying was people’s experience of happiness versus their memories of happiness. Danny also looked at how people experienced pain versus how they remembered it.

One experiment involved sticking the subject’s arms into a bucket of ice water.

[People’s] memory of pain was different from their experience of it. They remembered moments of maximum pain, and they remembered, especially, how they felt the moment the pain ended. But they didn’t particularly remember the length of the painful experience. If you stuck people’s arms in ice buckets for three minutes but warmed the water just a bit for another minute or so before allowing them to flee the lab, they remembered the experience more fondly than if you stuck their arms in the bucket for three minutes and removed them at a moment of maximum misery. If you asked them to choose one experience to repeat, they’d take the first session. That is, people preferred to endure more total pain so long as the experience ended on a more pleasant note.

Redelmeier tested this hypothesis on seven hundred people who underwent a colonoscopy. The results supported Danny’s finding.

 

BIRTH OF THE WARRIOR PSYCHOLOGIST

In 1973, the armies of Egypt and Syria surprised Israel on Yom Kippur. Amos and Danny left California for Israeli. Egyptian President Anwar Sadat had promised to shoot down any commercial airliners entering Israel. That was because, as usual, Israelis in other parts of the world would return to Israel during a war. Amos and Danny managed to land in Tel Aviv on an El Al flight. The plane had descended in total darkness. Amos and Danny were to join the psychology field unit.

Amos and Danny set out in a jeep and went to the battlefield in order to study how to improve the morale of the troops. Their fellow psychologists thought they were crazy. It wasn’t just enemy tanks and planes. Land mines were everywhere. And it was easy to get lost. People were more concerned about Danny than Amos because Amos was more of a fighter. But Danny proved to be more useful because he had a gift for finding solutions to problems where others hadn’t even noticed the problem.

Soon after the war, Amos and Danny studied public decision making.

Both Amos and Danny thought that voters and shareholders and all the other people who lived with the consequences of high-level decisions might come to develop a better understanding of the nature of decision making. They would learn to evaluate a decision not by its outcomes–whether it turned out to be right or wrong–but by the process that led to it. The job of the decision maker wasn’t to be right but to figure out the odds in any decision and play them well.

It turned out that Israeli leaders often agreed about probabilities, but didn’t pay much attention to them when making decisions on whether to negotiate for peace or fight instead. The director-general of the Israeli Foreign Ministry wasn’t even interested in the best estimates of probabilities. Instead, he made it clear that he preferred to trust his gut. Lewis quotes Danny:

“That was the moment I gave up on decision analysis. No one ever made a decision because of a number. They need a story.”

Some time later, Amos introduced Danny to the field of decision making under uncertainty. Many students of the field studied subjects in labs making hypothetical gambles.

The central theory in decision making under uncertainty had been published in the 1730s by the Swiss mathematician Daniel Bernoulli. Bernoulli argued that people make probabilistic decisions so as to maximize their expected utility. Bernoulli also argued that people are “risk averse”: each new dollar has less utility than the one before. This theory seemed to describe some human behavior.

(Utility as a function of outcomes, Global Water Forum, Wikimedia Commons)

The utility function above illustratesrisk aversion: Each additional dollar–between $10 and $50–has less utility than the one before.

In 1944, John von Neumann and Oskar Morgenstern published the axioms of rational decision making. One axiom was “transitivity”: if you preferred A to B, and B to C, then you preferred A to C. Another axiom was “independence”: if you preferred A to B, your preference between A and B wouldn’t change if some other alternative (say D) was introduced.

Many people, including nearly all economists, accepted von Neumann and Morgenstern’s axioms of rationality as a fair description for how people actually made choices. Danny recalls that Amos regarded the axioms as a “sacred thing.”

By the summer of 1973, Amos was searching for ways to undo the reigning theory of decision making, just as he and Danny had undone the idea that human judgment followed the precepts of statistical theory.

Lewis records that by the end of 1973, Amos and Danny were spending six hours a day together. One insight Danny had about utility was that it wasn’t levels of wealth that represented utility (or happiness); it was changes in wealth–gains and losses–that mattered.

 

THE ISOLATION EFFECT

Many of the ideas Amos and Danny had could not be attributed to either one of them individually, but seemed to come from their interaction. That’s why they always shared credit equally–they switched the order of their names for each new paper, and the order for their very first paper had been determined by a coin toss.

In this case, though, it was clear that Danny had the insight that gains and losses are more important than levels of utility. However, Amos then asked a question with profound implications: “What if we flipped the signs?” Instead of asking whether someone preferred a 50-50 gamble for $1,000 or $500 for sure, they asked this instead:

Which of the following do you prefer?

• Gift A: A lottery ticket that offers a 50 percent chance of losing $1,000
• Gift B: A certain loss of $500

When the question was put in terms of possible gains, people preferred the sure thing. But when the question was put in terms of possible losses, people preferred to gamble. Lewis elaborates:

The desire to avoid loss ran deep, and expressed itself most clearly when the gamble came with the possibility of both loss and gain. That is, when it was like most gambles in life. To get most people to flip a coin for a hundred bucks, you had to offer them far better than even odds. If they were going to loss $100 if the coin landed on heads, they would need to win $200 if it landed on tails. To get them to flip a coin for ten thousand bucks, you had to offer them even better odds than you offered them for flipping it for a hundred.

It was easy to see thatloss aversion had evolutionary advantages. People who weren’t sensitive to pain or loss probably wouldn’t survive very long.

A loss is when you end up worse than your status quo. Yet determining the status quo can be tricky because often it’s a state of mind. Amos and Danny gave this example:

Problem A. In addition to whatever you own, you have been given $1,000. You are now required to choose between the following options:

• Option 1. A 50 percent chance to win $1,000
• Option 2. A gift of $500

Problem B. In addition to whatever you own, you have been given $2,000. You are now required to choose between the following options:

• Option 3. A 50 percent chance to lose $1,000
• Option 4. A sure loss of $500

In Problem A, most people picked Option 2, the sure thing. In Problem B, most people chose Option 3, the gamble. However, the two problems are logically identical: Overall, you’re choosing between $1,500 for sure versus a 50-50 chance of either $2,000 or $1,000.

What Amos and Danny had discovered wasframing. The way a choice is framed can impact the way people choose, even if two different frames both refer to exactly the same choice, logically speaking. Consider the Asian Disease Problem, invented by Amos and Danny. People were randomly divided into two groups. The first group was given this question:

Problem 1. Imagine that the U.S. is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative problems to combat the disease have been proposed. Assume that the exact scientific estimate of the consequence of the programs is as follows:

• If Program A is adopted, 200 people will be saved.
• If Program B is adopted, there is a 1/3 probability that 600 people will be saved, and a 2/3 probability that no one will be saved.

Which of the two programs would you favor?

People overwhelming chose Program A, saving 200 people for sure.

The second group was given the same problem, but was offered these two choices:

• If Program C is adopted, 400 people will die.
• If Program D is adopted, there is a 1/3 probability that nobody will die and a 2/3 probability that 600 people will die.

People overwhelmingly chose Program D. Once again, the underlying choice in each problem is logically identical. If you save 200 for sure, then 400 will die for sure. Because of framing, however, people make inconsistent choices.

 

THIS CLOUD OF POSSIBILITY

In 1984, Amos learned he had been given a MacArthur “genius” grant. He was upset, as Lewis explains:

Amos disliked prizes. He thought that they exaggerated the differences between people, did more harm than good, and created more misery than joy, as for every winner there were many others who deserved to win, or felt they did.

Amos was angry because he thought that being given the award, and Danny not being given the award, was “a death blow” for the collaboration between him and Danny. Nonetheless, Amos kept on receiving prizes and honors, and Danny kept on not receiving them. Furthermore, ever more books and articles came forth praising Amos for the work he had done with Danny, as if he had done it alone.

Amos continued to be invited to lectures, seminars, and conferences. Also, many groups asked him for his advice:

United States congressmen called him for advice on bills their were drafting. The National Basketball Association called to hear his argument about statistical fallacies in basketball. The United States Secret Service flew him to Washington so that he could advise them on how to predict and deter threats to the political leaders under their protection. The North Atlantic Treaty Organization flew him to the French Alps to teach them about how people made decisions in conditions of uncertainty. Amos seemed able to walk into any problem, and make the people dealing with it feel as if he grasped its essence better than they did.

Despite the work of Amos and Danny, many economists and decision theorists continued to believe in rationality. These scientists argued that Amos and Danny had overstated human fallibility. So Amos looked for new ways to convince others. For instance, Amos asked people: Which is more likely to happen in the next year, that a thousand Americans will die in a flood, or that an earthquake in California will trigger a massive flood that will drown a thousand Americans? Most people thought the second scenario was more likely; however, the second scenario is a special case of the first scenario, and therefore the first scenario is automatically more likely.

Amos and Danny came up with an even more stark example. They presented people with the following:

Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.

Which of the two alternatives is more probable?

• Linda is a bank teller.
• Linda is a bank teller and is active in the feminist movement.

Eighty-five percent of the subjects thought that the second scenario is more likely than the first scenario. However, just like the previous problem, the second scenario is a special case of the first scenario, and so the first scenario is automatically more likely than the second scenario.

Say there are 50 people who fit the description, are named Linda, and are bank tellers. Of those 50, how many are also active in the feminist movement? Perhaps quite a few, but certainly not all 50.

Amos and Danny constructed a similar problem for doctors. But the majority of doctors made the same error.

Lewis:

The paper Amos and Danny set out to write about what they were now calling “the conjunction fallacy” must have felt to Amos like an argument ender–that is, if the argument was about whether the human mind reasoned probabilistically, instead of the ways Danny and Amos had suggested. They walked the reader through how and why people violated “perhaps the simplest and the most basic qualitative law of probability.” They explained that people chose the more detailed description, even though it was less probable, because it was more “representative.” They pointed out some places in the real world where this kink in the mind might have serious consequences. Any prediction, for instance, could be made to seem more believable, even as it became less likely, if it was filled with internally consistent details. And any lawyer could at once make a case seem more persuasive, even as he made the truth of it less likely, by adding “representative” details to his description of people and events.

Around the time Amos and Danny published work with these examples, their collaboration had come to be nothing like it was before. Lewis writes:

It had taken Danny the longest time to understand his own value. Now he could see that the work Amos had done alone was not as good as the work they had done together. The joint work always attracted more interest and higher praise than anything Amos had done alone.

Danny pointed out to Amos that Amos that been a member of the National Academy of Sciences for a decade, but Danny still wasn’t a member. Danny asked Amos why he hadn’t put Danny’s name forward.

A bit later, Danny told Amos they were no longer friends. Three days after that, Amos called Danny. Amos learned that his body was riddled with cancer and that he had at most six months to live.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time. See the historical chart here: https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps. Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals. We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio. The size of each position is determined by its rank. Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost). Positions are held for 3 to 5 years unless a stock approachesintrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods. We also aim to outpace the Russell Microcap Index by at least 2% per year (net). The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: [email protected]

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed.No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

July 31, 2022

In today’s blog post, I review some lessons from Michael Mauboussin’s excellent book Think Twice: Harnessing the Power of Counterintuition. Each chapter is based on a common mistake in decision-making:

• RQ vs. IQ
• The Outside View
• Open to Options
• The Expert Squeeze
• Situational Awareness
• More Is Different
• Evidence of Circumstance
• Phase Transitions–”Grand Ah-Whooms”
• Sorting Luck From Skill
• Time to Think Twice

 

RQ vs IQ

Given a proper investment framework or system, obviously IQ can help a great deal over time. Warren Buffett and Charlie Munger are seriously smart. But they wouldn’t have become great investors without a lifelong process of learning and improvement, including learning how to be rational. The ability to be rational may be partly innate, but it can be improved–sometimes significantly–with work.

An investor dedicated to lifelong improvements in knowledge and rationality can do well in value investing even without being brilliant. A part of rationality is focusing on the knowable and remembering the obvious.

“We try more to profit from always remembering the obvious than from grasping the esoteric. It is remarkable how much long-term advantage people like us have gotten by trying to be consistently not stupid, instead of trying to be very intelligent.” – Charlie Munger

Quite often, the best approach for a value investor is to invest in an index fund or in a quantitative value fund. Lifelong improvements are still helpful in these cases. Many value investors, including the father of value investing Ben Graham, have advocated and used a quantitative approach.

 

THE OUTSIDE VIEW

Mauboussin discusses why Big Brown was a bad bet to win the Triple Crown in 2008. Big Brown had won the Kentucky Derby by four-and-three-quarters lengths, and he won the Preakness by five-and-one-quarter lengths. The horse’s trainer, Rick Dutrow, said, “He looks as good as he can possibly look. I can’t find any flaws whatsoever in Big Brown. I see the prettiest picture. I’m so confident, it’s unbelievable.” UPS (after whom Big Brown was named) signed a marketing deal. And enthusiasm for Big Brown’s chances in the Belmont Stakes grew.

(Photo of Big Brown by Naoki Nakashima, via Wikimedia Commons)

What happened? Big Brown trailed the field during the race, so his jockey eased him out of the race. This was a shocking result. But the result of not winning could have been much more widely anticipated if people had used the outside view.

The outside view means identifying similar situations and finding the statistics on how things worked out. Renowned handicapper Steven Crist developed an outside view, as Mauboussin summarizes:

Of the twenty-nine horses with a chance to capture the Triple Crown after winning the Kentucky Derby and the Preakness Stakes, only eleven triumphed, a success rate less than 40 percent. But a closer examination of those statistics yielded a stark difference before and after 1950. Before 1950, eight of the nine horses attempting to win the Triple Crown succeeded. After 1950, only three of twenty horses won. It’s hard to know why the achievement rate dropped from nearly 90 percent to just 15 percent, but logical factors include better breeding (leading to more quality foals) and bigger starting fields.

Most people naturally use the inside view. This essentially means looking at more subjective factors that are close at hand, like how tall and strong the horse looks and the fact that Big Brown had handily won the Kentucky Derby and the Preakness.

Why do people naturally adopt the inside view? Mauboussin gives three reasons:

• the illusion of superiority
• the illusion of optimism
• the illusion of control

First is the illusion of superiority. Most people say they are above average in many areas, such as looks, driving, judging humor, investing. Most people have an unrealistically positive view of themselves. In many areas of life, this does not cause problems. In fact, unrealistic positivity may often be an advantage that helps people to persevere. But in zero-sum games–like investing–where winning requires clearly being above average, the illusion of superiority is harmful.

Munger calls it the Excessive Self-Regard Tendency. Munger also notes that humans tend to way overvalue the things they possess–the endowment effect. This often causes someone already overconfident about a bet he is considering to become even more overconfident after making the bet.

The illusion of optimism, which is similar to the illusion of superiority, causes most people to see their future as brighter than that of others.

The illusion of control causes people to behave as if chance events are somehow subject to their control. People throwing dice throw softly when they want low numbers and hard for high numbers. A similar phenomenon is seen when people choose which lottery card to take, as opposed to getting one by chance.

Mauboussin observes that a vast range of professionals tends to use the inside view to make important decisions, with predictably poor results.

Encouraged by the three illusions, most believe they are making the right decision and have faith that the outcomes will be satisfactory.

In the world of investing, many investors believe that they will outperform the market over time. However, after several decades, there are very few investors who have done better than the market.

Another area where people fall prey to the three illusions is mergers and acquisitions. Two-thirds of acquisitions fail to create value, but most executives, relying on the inside view, believe that they can beat the odds.

The planning fallacy is yet another example of how most people rely on the inside view instead of the outside view. Mauboussin gives one common example of students estimating when they’d finish an assignment:

…when the deadline arrived for which the students had given themselves a 50 percent chance of finishing, only 13 percent actually turned in their work. At the point when the students thought there was 75 percent chance they’d be done, just 19 percent had completed the project. All the students were virtually sure they’d be done by the final date. But only 45 percent turned out to be right.

Daniel Kahneman gives his own example of the planning fallacy. He was part of a group assembled to write a curriculum to teach judgment and decision-making to high school students. Kahneman asked everyone in the group to write down their opinion of when they thought the group would complete the task. Kahneman found that the average was around two years, and everyone, including the dean, estimated between eighteen and thirty months.

Kahneman then realized that the dean had participated in similar projects in the past. Kahneman asked the dean how long it took them to finish.

The dean blushed and then answered that 40 percent of the groups that had started similar programs had never finished, and that none of the groups completed it in less than seven years. Kahneman then asked how good this group was compared to past groups. The dean thought and then replied: ‘Below average, but not by much.’

 

OPEN TO OPTIONS

In making decisions, people often fail to consider a wide enough range of alternatives. People tend to have “tunnel vision.”

Anchoringis an important example of this mistake. Mauboussin:

Kahneman and Amos Tversky asked people what percentage of the UN countries is made up of African nations. A wheel of fortune with the numbers 1 to 100 was spun in front of the participants before they answered. The wheel was rigged so it gave either 10 or 65 as the result of a spin. The subjects were then asked–before giving their specific prediction–if the answer was higher or lower than the number on the wheel. The median response from the group that saw the wheel stop at 10 was 25%, and the median response from the group that saw 65 was 45%.

(Illustration by Olga Vainshtein)

Behavioral finance expert James Montier has run his own experiment onanchoring. People are asked to write down the last four digits of their phone number. Then they are asked whether the number of doctors in their capital city is higher or lower than the last four digits of their phone number. Results: Those whose last four digits were greater than 7000 on average report 6762 doctors, while those with telephone numbers below 2000 arrived at an average 2270 doctors.

Stock prices often have a large component of randomness, but investors tend to anchor on various past stock prices. The rational way to avoid such anchoring is to carefully develop different possible scenarios for the intrinsic value of a stock. For instance, you could ask:

• What is the business worth if things go better than expected?
• What is the business worth if things go as expected? Or: What is the business worth under normal conditions?
• What is the business worth if things go worse than expected?

Ideally, you would not want to know about past stock prices–or even the current stock price–before developing the intrinsic value scenarios.

The Representativeness Heuristic

The representativeness heuristic is another bias that leads many people not to consider a wide range of possibilities. Daniel Kahneman and Amos Tversky defined representativeness as “the degree to which [an event] (i) is similar in essential characteristics to its parent population, and (ii) reflects the salient features of the process by which it is generated.”

People naturally tend to believe that something that is more representative is more likely. But frequently that’s not the case. Here is an example Kahneman and Tversky have used:

“Steve is very shy and withdrawn, invariably helpful but with very little interest in people or in the world of reality. A meek and tidy soul, he has a need for order and structure, and a passion for detail. Question: Is Steve more likely to be a librarian or a farmer?”

Most people say “a librarian.” But the fact that the description seems more representative of librarians than of farmers does not mean that it is more likely that Steve is a librarian. Instead, one must look at the base rate: there are twenty times as many farmers as librarians, so it is far more likely that Steve is a farmer.

Another example Kahneman gives:

“Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations. Question: Which is more probable?

1. Linda is a bank teller.
2. Linda is a bank teller and is active in the feminist movement.”

Most people say the second option is more likely. But just using simple logic, we know that the second option is a subset of the first option, so the first option is more likely. Most people get this wrong because they use the representativeness heuristic.

Availability Bias, Vividness Bias, Recency Bias

If a fact is easily available–which often happens if a fact is vivid or recent–people generally far overestimate its probability.

A good example is a recent and vivid plane crash. The odds of dying in a plane crash are one in 11 million–astronomically low. The odds of dying in a car crash are one in five thousand. But many people, after seeing recent and vivid photos of a plane crash, decide that taking a car is much safer than taking a plane.

Extrapolating the Recent Past

Most people automatically extrapolate the recent past into the future without considering various alternative scenarios. To understand why, consider Kahneman’s definitions of two systems in the mind, System 1 and System 2:

System 1: Operates automatically and quickly; makes instinctual decisions based on heuristics.

System 2: Allocates attention (which has a limited budget) to the effortful mental activities that demand it, including logic, statistics, and complex computations.

In Thinking, Fast and Slow,Kahneman writes that System 1 and System 2 work quite well on the whole:

The division of labor between System 1 and System 2 is highly efficient: it minimizes effort and optimizes performance. The arrangement works well most of the time because System 1 is generally very good at what it does: its models of familiar situations are accurate, its short-term predictions are usually accurate as well, and its initial reactions to challenges are swift and generally appropriate. System 1 has biases, however, systematic errors that it is prone to make in specified circumstances. As we shall see, it sometimes answers easier questions than the one it was asked, and it has little understanding of logic and statistics.

System 1 is automatic and quick, and it works remarkably well much of the time. Throughout most of our evolutionary history, System 1 has been instrumental in keeping us alive. However, when we were hunter-gatherers, the recent past was usually the best guide to the future.

• If there was a rustling in the grass or any other sign of a predator, the brain automatically went on high alert, which was useful because otherwise you weren’t likely to survive long. A statistical calculation wasn’t needed.
• There were certain signs indicating the potential presence of animals to hunt or wild plants to collect. You learned to recognize those signs. You foraged or you died. You didn’t need to know any statistics.
• Absent any potential threats, and assuming enough to eat, then things were fine and you could relax for a spell.

In today’s world–unlike when we were hunter-gatherers–the recent past is often a terrible guide to the future. For instance, when it comes to investing, extrapolating the recent past is one of the biggest mistakes that investors make. In a highly random environment, you should expect reversion to the mean, rather than a continuation of the recent past. Investors must learn to think counterintuitively. That includes thinking probabilistically–in terms of possible scenarios and reversion to the mean.

Doubt Avoidance

Charlie Munger–see Poor Charlie’s Almanack, Expanded Third Edition–explains what he calls Doubt Avoidance Tendency as follows:

“The brain of man is programmed with a tendency to quickly remove doubt by reaching some decision.”

System 1 is designed (by evolution) to jump to conclusions. In the past, when things were simpler and less probabilistic, the ability to make a quick decision was beneficial. In today’s complex world, you must train yourself to slow down when facing an importantdecision under uncertainty–a decision that depends on possible scenarios and their associated probabilities.

The trouble is that our mind–due to System 1–wants to jump immediately to a conclusion, even more so if we feel pressured, puzzled, or stressed. Munger explains:

What triggers Doubt-Avoidance Tendency? Well, an unthreatened man, thinking of nothing in particular, is not being prompted to remove doubt through rushing to some decision. As we shall see later when we get to Social-Proof Tendency and Stress-Influence Tendency, what usually triggers Doubt-Avoidance Tendency is some combination of (1) puzzlement and (2) stress…

The fact that social pressure and stress trigger the Doubt-Avoidance Tendency supports the notion that System 1 excelled at keeping us alive when we lived in a much more primitive world. In that type of environment where things usually were what they seemed to be, the speed of System 1 in making decisions was vital. If the group was running in one direction, the immediate, automatic decision to follow was what kept you alive over time.

Inconsistency Avoidance and Confirmation Bias

Munger on the Inconsistency-Avoidance Tendency:

The brain of man conserves programming space by being reluctant to change, which is a form of inconsistency avoidance. We see this in all human habits, constructive and destructive. Few people can list a lot of bad habits that they have eliminated, and some people cannot identify even one of these. Instead, practically everyone has a great many bad habits he has long maintained despite their being known as bad…. chains of habit that were too light to be felt before they became too heavy to be broken.

The rare life that is wisely lived has in it many good habits maintained and many bad habits avoided or cured.

Munger continues:

It is easy to see that a quickly reached conclusion, triggered by Doubt-Avoidance Tendency, when combined with a tendency to resist any change in that conclusion, will naturally cause a lot of errors in cognition for modern man. And so it observably works out…

And so, people tend to accumulate large mental holdings of fixed conclusions and attitudes that are not often reexamined or changed, even though there is plenty of good evidence that they are wrong.

Our brain will jump quickly to a conclusion and then resist any change in that conclusion. How do we combat this tendency? One great way to overcome first conclusion bias is to train our brains to emulate Charles Darwin:

One of the most successful users of an antidote to first conclusion bias was Charles Darwin. He trained himself, early, to intensively consider any evidence tending to disconfirm any hypothesis of his, more so if he thought his hypothesis was a particularly good one. The opposite of what Darwin did is now called confirmation bias, a term of opprobrium. Darwin’s practice came from his acute recognition of man’s natural cognitive faults arising from Inconsistency-Avoidance Tendency.He provides a great example of psychological insight correctly used to advance some of the finest mental work ever done.(my emphasis)

Selective Attention and Inattentional Blindness

We tend to be very selective about what we hear and see, and this is partly a function of what we already believe. We often see and hear only what we want, and tune out everything else.

On a purely visual level, there is something called inattentional blindness. When we focus on certain aspects of our environment, this causes many of us to miss other aspects that are plainly visible. There is a well-known experiment related to inattentional blindness. People watch a thirty-second video that shows two teams, one wearing white and the wearing black. Each team is passing a basketball back and forth. In the middle of the video, a woman wearing a gorilla suit walks into the middle of the scene, thumps her chest, and walks off. Roughly half of the people watching the video have no recollection of the gorilla.

Struggles and Stresses

Stress or fatigue causes many of us to make poorer decisions than we otherwise would. Thus, we must take care. With the right attitude, however, stress can slowly be turned into an advantage over a long period of time.

As Ray Dalio and Charlie Munger have pointed out, mental strength is one of life’s greatest gifts. With a high degree of focus and discipline, a human being can become surprisingly strong and resilient. But this typically only happens gradually, over the course of years or decades, as the result of an endless series of struggles, stresses, and problems.

A part of strength that can be learned over time is inner peace or total calm in the face of seemingly overwhelming difficulties. The practice of transcendental meditation is an excellent way to achieve inner peace and total calm in the face of any adversity. But there are other ways, too.

Wise men such as Munger or Lincoln are of the view that total calm in the face of any challenge is simply an aspect of mental strength that can be developed over time. Consider Rudyard Kipling’s poem “If”:

If you can keep your head when all about you

Are losing theirs and blaming it on you,

If you can trust yourself when all men doubt you,

But make allowance for their doubting too;

If you can wait and not be tired by waiting,

Or being lied about, don’t deal in lies,

Or being hated, don’t give way to hating,

And yet don’t look too good, nor talk too wise…

…So, maybe in that sense I think a tougher hand has been good for us. My answer to that question reminds me of my old Harvard law professor who used to say, ‘Charlie, let me know what your problem is and I’ll try to make it harder for you.’ I’m afraid that’s what I’ve done to you.

As for how do I understand a new industry: the answer is barely. I just barely have enough cognitive ability to do what I do. And that’s because the world promoted me to the place where I’m stressed. And you’re lucky if it happens to you, because that’s what you want to end up: stressed. You want to have your full powers called for. Believe you me, I’ve had that happen all my life. I’ve just barely been able to think through to the right answer, time after time. And sometimes I’ve failed…

Link to 2016 Daily Journal Meeting Notes (recorded courtesy of Whitney Tilson): https://www.scribd.com/doc/308879985/MungerDJ-2-16

Incentives

Mauboussin writes about the credit crisis of 2007-2008. People without credit could buy nice homes. Lenders earned fees and usually did not hold on to the mortgages. Investment banks bought mortgages and bundled them for resale, earning a fee. Rating agencies were paid to rate the mortgage-backed securities, and they rated many of them AAA (based partly on the fact that home prices had never declined nationwide). Investors worldwide in AAA-rated mortgage-backed securities earned higher returns than they did on other AAA issues. Some of these investors were paid based on portfolio performance and thus earned higher fees this way.

Incentives are extremely important:

“Never, ever think about something else when you should be thinking about incentives.” – Charlie Munger

Under a certain set of incentives, many people who normally are good people will behave badly. Often this bad behavior is not only due to the incentives at play, but also involves other psychological pressures like social proof, stress, and doubt-avoidance. A bad actor could manipulate basically good people to do bad things using social proof and propaganda. If that fails, he could use bribery or blackmail.

Finally, Mauboussin offers advice about how to deal with “tunnel vision,” or the insufficient consideration of alternatives:

• Explicitly consider alternatives.
• Seek dissent. (This is very difficult, but highly effective. Think of Lincoln’s team of rivals.)
• Keep track of previous decisions. (A decision journal does not cost much, but it can help one over time to make better decisions.)
• Avoid making decisions while at emotional extremes. (One benefit to meditation–in addition to total calm and rationality–is that it can give you much greater self-awareness. You can learn to accurately assess your emotional state, and you can learn to postpone important decisions if you’re too emotional or tired.)
• Understand incentives.

 

THE EXPERT SQUEEZE

In business today, there are many areas where you can get better insights or predictions than what traditional experts can offer.

Mauboussin gives the example of Best Buy forecasting holiday sales. In the past, Best Buy depended on specialists to make these forecasts. James Surowiecki, author of The Wisdom of Crowds, went to Best Buy’s headquarters and told them that a crowd could predict better than their specialists could.

Jeff Severts, a Best Buy executive, decided to test Surowiecki’s suggestion. Late in 2005, Severts set up a location for employees to submit and update their estimates of sales from Thanksgiving to year-end. In early 2006, Severts revealed that the internal experts had been 93 percent accurate, while the “amateur crowd” was off only one-tenth of one percent. Best Buy then allocated more resources to its prediction market, and benefited.

Another example of traditional experts being supplanted: Orley Ashenfelter, wine lover and economist, figured out a simple regression equation that predicts the quality of red wines from France’s Bordeaux region better than most wine experts. Mauboussin:

With the equation in hand, the computer can deliver appraisals that are quicker, cheaper, more reliable, and without a whiff of snobbishness.

Mauboussin mentions four categories over which we can judge experts versus computers:

Rule based; limited range of outcomes–experts are generally worse than computers. Examples include credit scoring and simple medical diagnosis.

Rule based; wide range of outcomes–experts are generally better than computers. But this may be changing. For example, humans used to be better at chess and Go, but now computers are far better than humans.

Probabilistic; limited range of outcomes–experts are equal or worse than collectives. Examples include admissions officers and poker.

Probabilistic; wide range of outcomes–experts are worse than collectives. Examples include forecasting any of the following: stock prices, the stock market, interest rates, or the economy.

Regarding areas that are probabilistic, with a wide range of outcomes (the fourth category), Mauboussin comments on economic and political forecasts:

The evidence shows that collectives outperform experts in solving these problems. For instance, economists are extremely poor forecasters of interest rates, often failing to accurately guess the direction of rate moves, much less their correct level. Note, too, that not only are experts poor at predicting actual outcomes, they rarely agree with one another. Two equally credentialed experts may make opposite predictions and, hence, decisions from one another.

Mauboussin notes that experts do relatively well with rule-based problems with a wide range of outcomes because they can be better than computers at eliminating bad choices and making creative connections between bits of information. A fascinating example: Eric Bonabeau, a physicist, has developed programs that generate alternative designs for packaging using the principles of evolution (recombination and mutation). But the experts select the best designs at the end of the process, since the computers have no taste.

Yet computers will continue to make big improvements in this category (rule-based problems with a wide range of outcomes). For instance, many chess programs today can beat any human, whereas there was only one program (IBM’s Deep Blue) that could do this in the late 1990’s. Also, in October 2015, Google DeepMind’s program AlphaGo beat Fan Hui, the European Go champion.

Note: We still need experts to make the systems that replace them. Severts had to set up the prediction market. Ashenfelter had to find the regression equation. And experts need to stay on top of the systems, making improvements when needed.

Also, experts are still needed for many areas in strategy, including innovation and creativity. And people are needed to deal with people. (Although many jobs will soon be done by robots.)

I’ve written before about how simple quant models outperform experts in a wide variety of areas: https://boolefund.com/simple-quant-models-beat-experts-in-a-wide-variety-of-areas/

 

SITUATIONAL AWARENESS

Mauboussin writes about the famous experiment by Solomon Asch. The subject is shown lines of obviously different lengths. But in the same room with the subject are shills, who unbeknownst to the subject have already been instructed to say that two lines of obviously different lengths actually have the same length. So the subject of the experiment has to decide between the obvious evidence of his eyes–the two lines are clearly different lengths–and the opinion of the crowd. A significant number (36.8 percent) ignored their own eyes and went with the crowd, saying that the two lines had equal length, despite the obvious fact that they didn’t.

(Photo by D-janous, via Wikimedia Commons)

Mauboussin notes that the interesting question about the Solomon Asch experiment is: what’s going on in the heads of people who conform? Asch himself suggested three possibilities:

Distortion of judgment. The subjects conclude that their perceptions are wrong and that the group is right.

Distortion of action. These individuals suppress their own knowledge in order to go with the majority.

Distortion of perception. This group is not aware that the majority opinion distorts their estimates.

Unfortunately, Asch didn’t have the tools to try to test these possibilities. Gregory Berns, a neuroscientist, five decades after Asch, used functional magnetic resonance imaging (fMRI) in the lab at Emory University.

For the conforming subjects, the scientists found activity in the areas of the brain that were related to perception of the object. Also, the scientists did not find a meaningful change in activity in the frontal lobe–an area associated with activities like judgment. Thus, for conforming subjects, it is a distortion of perception: what the majority claims to see, the subject actually does see. Remarkable.

What about the people who remained independent when faced with the group’s wrong responses? Those subjects showed increased activity in the amygdala, a region that signals to prepare for immediate action (fight or flight). Mauboussin comments: “…while standing alone is commendable, it is unpleasant.”

Priming

Mauboussin:

How do you feel when you read the word ‘treasure’? … If you are like most people, just ruminating on ‘treasure’ gives you a little lift. Our minds naturally make connections and associate ideas. So if someone introduces a cue to you–a word, a smell, a symbol–your mind often starts down an associative path. And you can be sure the initial cue will color a decision that waits at the path’s end. All this happens outside your perception.

(Subconscious as brain under water, Illustration by Agawa288)

Scientists did the following experiment:

In this test, the researchers placed the French and German wines next to each other, along with small national flags. Over two weeks, the scientists alternated playing French accordion music and German Bierkeller pieces and watched the results. When French music played, French wines represented 77 percent of the sales. When German music played, consumers selected German wines 73 percent of the time… The music made a huge difference in shaping purchases. But that’s not what the shoppers thought…

While the customers acknowledged that the music made them think of either France or Germany, 86 percent denied that the tunes had any influence on their choice. This experiment is an example of priming, which psychologists formally define as ‘the incidental activation of knowledge structures by the current situational context.’ In other words, what comes in through our senses influences how we make decisions, even when it seems completely irrelevant in a logical sense. Priming is by no means limited to music. Researchers have manipulated behavior through exposure to words, smells, and visual backgrounds.

Mauboussin gives some examples of priming:

• Immediately after being exposed to words associated with the elderly, primed subjects walked 13 percent slower than subjects seeing neutral words.
• Exposure to the scent of an all-purpose cleaner prompted study participants to keep their environment tidier while eating a crumbly biscuit.
• Subjects reviewing Web pages describing two sofa models preferred the more comfortable model when they saw a background with puffy clouds, and favored the cheaper sofa when they saw a background with coins.

The Fault of the Default

While virtually 100 percent of Austrians have consented to be an organ donor, only 12 percent of Germans have. The difference is due entirely to how the choice is presented. In Austria, you must opt-out of being an organ donor–being an organ donor is the default choice. In Germany, you must opt-in to being an organ donor–not being a donor is the default choice. But this directly translates into many more saved lives in Austria than in Germany.

Mauboussin makes an important larger point. We tend to assume that people decide what is best for them independent of how the choice is framed, but in reality, “many people simply go with the default options.” This includes consequential areas (in addition to organ donation) like savings, educational choice, medical alternatives, etc.

The Power of Inertia

To overcome inertia, Peter Drucker suggested asking: “If we did not do this already, would we, knowing what we now know, go into it?”

Dr. Atul Gawande, author of The Checklist Manifesto, tells the story of Dr. Peter Pronovost, an anesthesiologist and critical-care specialist at the Johns Hopkins Hospital. Pronovost’s father died due to a medical error, which led Pronovost to dedicate his career to ensuring the safety of patients. Mauboussin explains:

In the United States, medical professionals put roughly 5 million lines into patients each year, and about 4 percent of those patients become infected within a week and a half. The added cost of treating those patients is roughly $3 billion per year, and the complications result in twenty to thirty thousand annual preventable deaths.

Pronovost came up with a simple checklist because he observed that physicians in a hurry would often overlook some simple routine that is normally done as a part of safety. It saved numerous lives and millions of dollars in the first few years at Johns Hopkins Hospital, so Pronovost got the Michigan Health & Hospital Association to try the checklist. After just three months, the rate of infection dropped by two-thirds. After eighteen months, the checklist saved 1,500 lives and nearly $200 million.

 

MORE IS DIFFERENT

Mauboussin covers complex adaptive systems such as the stock market or the economy. His advice, when dealing with a complex adaptive system, is:

• Consider the system at the correct level. An individual agent in the system can be very different from one outside the system.
• Watch for tightly coupled systems. A system is tightly coupled when there is no slack between items, allowing a process to go from one stage to the next without any opportunity to intervene. (Examples include space missions and nuclear power plants.) Most complex adaptive systems are loosely coupled, where removing or incapacitating one or a few agents has little impact on the system’s performance.
• Use simulations to create virtual worlds. Simulation is a tool that can help our learning process. Simulations are low cost, provide feedback, and have proved their value in other domains like military planning and pilot training.

Mauboussin notes that complex adaptive systems often perform well at the system level, despite dumb agents (consider ants or bees). Moreover, there are often unintended consequences that can lead to failure when well-meaning humans try to manage a complex system towards a particular goal.

 

EVIDENCE OF CIRCUMSTANCE

Decisions that work well in one context can often fail miserably in a different context. The right answer to many questions that professionals face is: “It depends.”

Mauboussin writes about how most people make decisions based on a theory, even though often they are not aware of it. Two business professors, Paul Carlile and Clayton Christensen, describe three stages of theory building:

• The first stage is observation, which includes carefully measuring a phenomenon and documenting the results. The goal is to set common standards so that subsequent researchers can agree on the subject and the terms to describe it.
• The second stage is classification, where researchers simplify and organize the world into categories to clarify the differences among phenomena. Early in theory development, these categories are based predominantly on attributes.
• The final stage is definition, or describing the relationship between the categories and the outcomes. Often, these relationships start as simple correlations.

What’s especially important, writes Mauboussin:

Theories improve when researchers test predictions against real-world data, identify anomalies, and subsequently reshape the theory. Two crucial improvements occur during this refining process. In the classification stage, researchers evolve the categories to reflect circumstances, not just attributes. In other words, the categories go beyond what works to when it works. In the definition stage, the theory advances beyond simple correlations and sharpens to define causes–why it works. This pair of improvements allows people to go beyond crude estimates and to tailor their choices to the situation they face.

Here is what is often done: Some successes are observed, some common attributes are identified, and it is proclaimed that these attributes can lead others to success. This doesn’t work.

By the same logic, a company should not adopt a strategy without understanding the conditions under which it succeeds or fails. Mauboussin gives the example of Boeing outsourcing both the design and the building of sections of the Dreamliner to its suppliers. This was a disaster. Boeing had to pull the design work back in-house.

The Colonel Blotto Game

Each player gets a hundred soldiers (resources) to distribute across three battlefields (dimensions). The players make their allocations in secret. Then the players’ choices are simultaneously revealed, and the winner of each battle is whichever army has more soldiers in that battlefield. The overall winner is whichever player wins the most battles. What’s interesting is how the game changes as you adjust one of the two parameters (resources, dimensions).

Mauboussin observes that it’s not intuitive how much advantage additional points give to one side in a three-battlefield game:

In a three-battlefield game, a player with 25 percent more resources has a 60 percent expected payoff (the proportion of battles the player wins), and a player with twice the resources has a 78 percent expected payoff. So some randomness exists, even in contests with fairly asymmetric resources, but the resource-rich side has a decisive advantage. Further, with low dimensions, the game is largely transitive: if A can beat B and B can beat C, then A can beat C. Colonel Blotto helps us to understand games with few dimensions, such as tennis.

Things can change even more unexpectedly when the number of dimensions is increased:

But to get the whole picture of the payoffs, we must introduce the second parameter, the number of dimensions or battlefields. The more dimensions the game has, the less certain the outcome (unless the players have identical resources). For example, a weak player’s expected payoff is nearly three times higher in a game with fifteen dimensions than in a nine-dimension game. For this reason, the outcome is harder to predict in a high-dimension game than in a low-dimension game, and as a result there are more upsets. Baseball is a good example of a high-dimension game…

What may be most surprising is that the Colonel Blotto game is highly nontransitive (except for largely asymmetric, low-dimension situations). This means that tournaments often fail to reveal the best team. Mauboussin gives an example where A beats B, B beats C, C beats A, and all of them beat D. Because there is no best player, the winner of a tournament is simply “the player who got to play D first.” Mauboussin:

Because of nontransitivity and randomness, the attribute of resources does not always prevail over the circumstance of dimensionality.

Bottom Line on Attributes vs. Circumstances

Mauboussin sums up the main lesson on attributes versus circumstances:

Most of us look forward to leveraging our favorable experiences by applying the same approach to the next situation. We also have a thirst for success formulas–key steps to enrich ourselves. Sometimes our experience and nostrums work, but more often they fail us. The reason usually boils down to the simple reality that the theories guiding our decisions are based on attributes, not circumstances. Attribute-based theories come very naturally to us and often appear compelling… However, once you realize the answer to most questions is, ‘It depends,’ you are ready to embark on the quest to figure out what it depends on.

 

PHASE TRANSITIONS–”GRAND AH-WHOOMS”

Just a small incremental change in temperature leads to a change from solid to liquid or from liquid to gas. Philip Ball, a physicist and author of Critical Mass: How One Thing Leads to Another, calls it a grand ah-whoom.

(Illustration by Designua)

Critical Points, Extremes, and Surprise

In part due to the writings of Nassim Taleb, people are more aware of black swans, or extreme outcomes within a power law distribution. According to Mauboussin, however, what most people do not yet appreciate is how black swans are caused:

Here’s where critical points and phase transitions come in. Positive feedback leads to outcomes that are outliers. And critical points help explain our perpetual surprise at black swan events because we have a hard time understanding how such small incremental perturbations can lead to such large outcomes.

Mauboussin explains critical points in social systems. Consider the wisdom of crowds: Crowds tend to make accurate predictions when three conditions prevail–diversity, aggregation, and incentives.

Diversity is about people having different ideas and different views of things. Aggregation means you can bring the group’s information together. Incentives are rewards for being right and penalties for being wrong that are often, but not necessarily, monetary.

Mauboussin continues:

For a host of psychological and sociological reasons, diversity is the most likely condition to fail when humans are involved. But what’s essential is that the crowd doesn’t go from smart to dumb gradually. As you slowly remove diversity, nothing happens initially. Additional reductions may also have no effect. But at a certain critical point, a small incremental reduction causes the system to change qualitatively.

Blake LeBaron, an economist at Brandeis University, has done an experiment. LaBaron created a thousand investors within the computer and gave them money, guidelines on allocating their portfolios, and diverse trading rules. Then he let the system play out. As Mauboussin describes:

His model was able to replicate many of the empirical features we see in the real world, including cycles of booms and crashes. But perhaps his most important finding is that a stock price can continue to rise even while the diversity of decision rules falls. Invisible vulnerability grows. But then, ah-whoom, the stock price tumbles as diversity rises again. Writes LaBaron, ‘During the run-up to a crash, population diversity falls. Agents begin using very similar trading strategies as their common good performance is reinforced. This makes the population very brittle, in that a small reduction in the demand for shares could have a strong destabilizing impact on the market.’

The Problem of Induction, Reductive Bias, and Bad Predictions

Extrapolating from what we see or have seen, to what will happen next, is a common decision-making mistake. Nassim Taleb retells Bertrand Russell’s story of a turkey (Taleb said turkey instead of chicken to suit his American audience). The turkey is fed a thousand days in a row. The turkey feels increasingly good until the day before Thanksgiving, when an unexpected event occurs. None of the previous one thousand days has given the turkey any clue about what’s next. Mauboussin explains:

The equivalent of the turkey’s plight–sharp losses following a period of prosperity–has occurred repeatedly in business. For example, Merrill Lynch (which was acquired by Bank of America) suffered losses over a two-year period from 2007 to 2008 that were in excess of one-third of the profits it had earned cumulatively in its thirty-six years as a public company….

The term black swan reflects the criticism of induction by the philosopher Karl Popper. Popper argued that seeing lots of white swans doesn’t prove the theory that all swans are white, but seeing one black swan does disprove it. So Popper’s point is that to understand a phenomenon, we’re better off focusing on falsification than on verification. But we’re not naturally inclined to falsify something.

Not only does System 1 naturally look for confirming evidence. But even System 2 uses a positive test strategy, looking for confirming evidence for any hypothesis, rather than looking for disconfirming evidence.

People have a propensity to stick to whatever they currently believe. Most people rarely examine or test their beliefs (hypotheses). As Bertrand Russell pointed out:

Most people would rather die than think; many do.

People are generally overconfident. Reductive bias means that people tend to believe that reality is much simpler and more predictable than it actually is. This causes people to oversimplify complex phenomena. Instead of properly addressing the real questions–however complex and difficult–System 1 naturally substitutes an easier question. The shortcuts used by System 1 work quite well in simple environments. But these same shortcuts lead to predictable errors in complex and random environments.

System 2–which can be trained to do logic, statistics, and complex computations–is naturally lazy. It requires conscious effort to activate System 2 . If System 1 recognizes a serious threat, then System 2 can be activated if needed.

The problem is that System 1 does not recognize the dangers associated with complex and random environments. Absent an obvious threat, System 1 will nearly always oversimplify complex phenomena. This creates overconfidence along with comforting illusions–”everything makes sense” and “everything is fine.” But complex systems frequently undergo phase transitions, and some of these new phases have sharply negative consequences, especially when people are completely unprepared.

Even very smart people routinely oversimplify and are inclined to trust overly simple mathematical models–for instance, models that assume a normal distribution even when the distribution is far from normal. Mauboussin argues that Long-Term Capital Management, which blew up in the late 1990’s, had oversimplified reality by relying too heavily on its financial models. According to their models, the odds of LTCM blowing up–as it did–were astronomically low (1 out of billions). Clearly their models were very wrong.

Mauboussin spoke with Benoit Mandelbrot, the French mathematician and father of fractal geometry. Mauboussin asked about the reductive bias. Mandelbrot replied that the wild randomness of stock markets was clearly visible for all to see, but economists continued to assume mild randomness, largely because it simplified reality and made the math more tractable. If you assume a normal distribution, the math is much easier than if you tried to capture the wildness and complexity of reality:

Mandelbrot emphasized that while he didn’t know what extreme event was going to happen in the future, he was sure that the simple models of the economists would not anticipate it.

Mauboussin gives the example of David Li’s formula, which measures the correlation of default between assets. (The formula is known as a Gaussian copula function.) Li’s equation could measure the likelihood that two or more assets within a portfolio would default at the same time. This “opened the floodgates” for financial engineers to create new products, including collateralized debt obligations (bundles of corporate bonds), and summarize the default correlation using Li’s equation “rather than worry about the details of how each corporate bond within the pool would behave.”

Unfortunately, Li’s equation oversimplified a complex world: Li’s equation did not make any adjustments for the fact that many correlations can change significantly.

The failure of Long-Term Capital Management illustrates how changing correlations can wreak havoc. LTCM observed that the correlation between its diverse investments was less than 10 percent over the prior five years. To stress test its portfolio, LTCM assumed that correlations could rise to 30 percent, well in excess of anything the historical data showed. But when the financial crisis hit in 1998, the correlations soared to 70 percent. Diversification went out the window, and the fund suffered mortal losses. ‘Anything that relies on correlation is charlatanism,’ scoffed Taleb. Or, as I’ve heard traders say, ‘The only thing that goes up in a bear market is correlation.’

Music Lab

Duncan Watts, a sociologist, led a trio of researchers at Columbia University in doing a social experiment. Subjects went to a web site–Music Lab–and were invited to participate in a survey. Upon entering the site, 20 percent of the subjects were assigned to an independent world and 10 percent each to eight worlds where people could see what other people were doing.

In the independent world, subjects were free to listen to songs, rated them, and download them, but they had no information about what other subjects were doing. In each of the other eight worlds, the subjects could see how many times other people had downloaded each song.

The subjects in the independent world collectively gave a reasonable indication of the quality of each of the songs. Thus, you could see for the other eight worlds whether social influence made a difference or not.

Song quality did play a role in the ranking, writes Mauboussin. A top-five song in the independent world had about a 50 percent chance of finishing in the top five in a social influence world. And the worst songs rarely topped the charts. But how would you guess the average song did in the social worlds?

The scientists found that social influence played a huge part in success and failure. One song, ‘Lockdown’ by the band 52metro, ranked twenty-sixth in the independent world, effectively average. Yet it was the number one song in one of the social influence worlds, and number forty in another. Social influence catapulted an average song to hit status in one world–ah-whoom–and relegated it to the cellar in another. Call it Lockdown’s lesson.

In the eight social worlds, the songs the subjects downloaded early in the experiment had a huge influence on the songs subjects downloaded later. Since the patterns of download were different in each social world, so were the outcomes.

(Illustration by Mindscanner)

Mauboussin summarizes the lessons:

• Study the distribution of outcomes for the system you are dealing with. Taleb defines gray swans as “modelable extreme events,” which are events you can at least prepare for, as opposed to black swans, which are by definition exceedingly difficult to prepare for.
• Look for ah-whoom moments. In social systems, you must be mindful of the level of diversity.
• Beware of forecasters. Especially for phase transitions, forecasts are generally dismal.
• Mitigate the downside, capture the upside. One of the Kelly criterion’s central lessons is that betting too much in a system with extreme outcomes leads to ruin.

 

SORTING LUCK FROM SKILL

In areas such as business, investing, and sports, people make predictable and natural mistakes when it comes to distinguishing skill from luck. Consider reversion to the mean:

The idea is that for many types of systems, an outcome that is not average will be followed by an outcome that has an expected value closer to the average. While most people recognize the idea of reversion to the mean, they often ignore or misunderstand the concept, leading to a slew of mistakes in their analysis.

Reversion to the mean was discovered by the Victorian polymath Francis Galton, a cousin of Charles Darwin. For instance, Dalton found that tall parents tend to have children that are tall, but not as tall–their heights are closer to the mean. Similarly, short parents tend to have children that are short, but not as short–their heights are closer to the mean.

Yet it’s equally true that tall people have parents that are tall, but not as tall–the parents’ heights are closer to the mean. Similarly, short people have parents that are short, but not as short–their heights are closer to the mean. Thus, Dalton’s crucial insight was that the overall distribution of heights remains stable over time: the proportions of the population in every height category was stable as one looks forward or backward in time.

Skill, Luck, and Outcomes

Mauboussin writes that Daniel Kahneman was asked to offer a formula for the twenty-first century. Kahneman gave two formulas:

Success = Some talent + luck

Great success = Some talent + a lot of luck

Consider an excellent golfer who scores well below her handicap during the first round. What do you predict will happen in the second round? We expect the golfer to have a score closer to her handicap for the second roundbecause we expect there to be less luck compared to the first round.

When you think about great streaks in sports like baseball, the record streak always belongs to a very talented player. So a record streak is a lot of talent plus a lot of luck.

 

TIME TO THINK TWICE

You don’t need to think twice before every decision. The stakes for most decisions are low. And even when the stakes are high, the best decision is often obvious enough.

The value of Think Twice is in situations with high stakes where your natural decision-making process will typically lead to a suboptimal choice. Some final thoughts:

Raise Your Awareness

As Kahneman has written, it is much easier to notice decision-making mistakes in others than in ourselves. So pay careful attention not only to others, but also to yourself.

It is difficult to think clearly about many problems. Furthermore, after outcomes have occurred, hindsight bias causes many of us to erroneously recall that we assigned the outcome a much higher probability than we actually did ex ante.

Put Yourself in the Shoes of Others

Embracing the outside view is typically essential when making an important probabilistic decision. Although the situation may be new for us, there are many others who have gone through similar things.

When it comes to understanding the behavior of individuals, often the situation–or specific, powerful incentives–can overwhelm otherwise good people.

Also, be careful when trying to understand or to manage a complex adaptive system, whether an ecosystem or the economy.

Finally, leaders must develop empathy for people.

Recognize the Role of Skill and Luck

When luck plays a significant role, anticipate reversion to the mean: extreme outcomes are followed by more average outcomes.

Short-term investment results reflect a great deal of randomness.

Get Feedback

Timely, accurate, and clear feedback is central to deliberate practice, which is the path to gaining expertise. The challenge is that in some fields, like long-term investing, most of the feedback comes with a fairly large time lag.

For investors, it is quite helpful to keep a journal detailing the reasons for every investment decision. (If you have the time, you can also write down how you feel physically and mentally at the time of each decision.)

 

(Photo by Vinay_Mathew)

A well-kept journal allows you to clearly audit your investment decisions. Otherwise, most of us will lose any ability to recall accurately why we made the decisions we did. This predictable memory loss–in the absence of careful written records–is often associated with hindsight bias.

It’s essential to identify–regardless of the outcome–when you have made a good decision and when you have made a bad decision. A good decision means that you faithfully followed a solid, proven process.

Another benefit of a well-kept investment journal is that you will start to notice other factors or patterns associated with bad investment decisions. For instance, too much stress or too much fatigue is often associated with poorer decisions. On the other hand, a good mood is often associated with overconfident decisions.

Mauboussin mentions a story told by Josh Waitzkin about Tigran Petrosian, a former World Chess Champion:

“When playing matches lasting days or weeks, Petrosian would wake up and sit quietly in his room, carefully assessing his own mood. He then built his game plan for the day based on that mood, with great success. A journal can provide a structured tool for similar introspection.”

Create a Checklist

Mauboussin:

When you face a tough decision, you want to be able to think clearly about what you might inadvertently overlook. That’s where a decision checklist can be beneficial.

Mauboussin again:

A good checklist balances two opposing objectives. It should be general enough to allow for varying conditions, yet specific enough to guide action. Finding this balance means a checklist should not be too long; ideally, you should be able to fit it on one or two pages.

If you have yet to create a checklist, try it and see which issues surface. Concentrate on steps or procedures, and ask where decisions have gone off track before. And recognize that errors are often the result of neglecting a step, not from executing the other steps poorly.

Perform a Premortem

Mauboussin explains:

You assume you are in the future and the decision you made has failed. You then provide plausible reasons for that failure. In effect, you try to identify why your decision might lead to a poor outcome before you make the decision. Klein’s research shows that premortems help people identify a greater number of potential problems than other techniques and encourage more open exchange, because no one individual or group has invested in a decision yet.

…You can track your individual or group premortems in your decision journal. Watching for the possible sources of failure may also reveal early signs of trouble.

Know What You Can’t Know

• In decisions that involve a system with many interacting parts, causal links are frequently unclear…. Remember what Warren Buffet said: ‘Virtually all surprises are unpleasant.’ So considering the worst-case scenarios is vital and generally overlooked in prosperous times.
• Also, resist the temptation to treat a complex system as if it’s simpler than it is…. We can trace most of the large financial disasters to a model that failed to capture the richness of outcomes inherent in a complex system like the stock market.

Mauboussin notes a paradox with decision making: Nearly everyone realizes its importance, but hardly anyone practices (or keeps a journal). Mauboussin concludes:

There are common and identifiable mistakes that you can understand, see in your daily affairs, and manage effectively. In those cases, the correct approach to deciding well often conflicts with what your mind naturally does. But now that you know when to think twice, better decisions will follow. So prepare your mind, recognize the context, apply the right technique–and practice.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time. See the historical chart here: https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps. Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals. We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio. The size of each position is determined by its rank. Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost). Positions are held for 3 to 5 years unless a stock approachesintrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods. We also aim to outpace the Russell Microcap Index by at least 2% per year (net). The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: [email protected]

 

 

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed.No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.

July 24, 2022

Shoe Dog is the autobiography of Phil Knight, the creator of Nike. Bill Gates mentioned this book as one of his favorites in 2016, saying it was “a refreshingly honest reminder of what the path to business success really looks like: messy, precarious, and riddled with mistakes.”

After the introduction, Knight has a chapter for each year, starting in 1962 and going through 1980.

 

DAWN

Knight introduces his story:

On paper, I thought, I’m an adult. Graduated from a good college – University of Oregon. Earned a master’s from a top business school – Stanford. Survived a yearlong hitch in the U.S. Army – Fort Lewis and Fort Eustis. My resume said I was a learned, accomplished soldier, a twenty-four-year-old man in full… So why, I wondered, why do I still feel like a kid?

Worse, like the same shy, pale, rail-thin kid I’d always been.

Maybe because I still hadn’t experienced anything of life. Least of all its many temptations and excitements. I hadn’t smoked a cigarette, hadn’t tried a drug. I hadn’t broken a rule, let alone a law. The 1960s were just underway, the age of rebellion, and I was the only person in America who hadn’t yet rebelled. I couldn’t think of one time I’d cut loose, done the unexpected.

I’d never even been with a girl.

If I tended to dwell on all the things I wasn’t, the reason was simple. Those were the things I knew best. I’d have found it difficult to see who or what exactly I was, or might become. Like all my friends I wanted to be successful. Unlike my friends I didn’t know what that meant. Money? Maybe. Wife? Kids? House? Sure, if I was lucky. These were the goals I was taught to aspire to, and part of me did aspire to them, instinctively. But deep down I was searching for something else, something more. I had an aching sense that our time is short, shorter than we ever know, short as a morning run, and I wanted mine to be meaningful. And purposeful. And creative. And important. Above all… different.

I wanted to leave a mark on the world…

And then it happened. As my young heart began to thump, as my pink lungs expanded like the wings of a bird, as the trees turned to greenish blurs, I saw it all before me, exactly what I wanted my life to be. Play.

Yes, I thought. That’s it. That’s the word. The secret of happiness, I’d always suspected, the essence of beauty or truth, or all we ever need to know of either, lay somewhere in that moment when the ball is in midair, when both boxers sense that approach of the bell, when the runners near the finish line and the crowd rises as one. There’s a kind of exuberant clarity in that pulsing half second before winning and losing are decided. I wanted that, whatever that was, to be my life, my daily life.

(Sweet Sixteen Syracuse vs. Gonzaga, March 25, 2016, Photo by Ryan Dickey, Wikimedia Commons)

Knight continues:

At different times, I’d fantasized about becoming a great novelist, a great journalist, a great statesman. But the ultimate dream was always to be a great athlete. Sadly, fate had made me good, not great. At twenty-four, I was finally resigned to that fact. I’d run track at Oregon, and I’d distinguished myself, lettering three of four years. But that was that, the end. Now, as I began to clip off one brisk six-minute mile after another, as the rising sun set fire to the lowest needles of the pines, I asked myself: What if there were a way, without being an athlete, to feel what athletes feel? To play all the time, instead of working? Or else to enjoy work so much that it becomes essentially the same thing.

I was suddenly smiling. Almost laughing. Drenched in sweat, moving as gracefully and effortlessly as I ever did, I saw my Crazy Idea shining up ahead, and it didn’t look all that crazy. It didn’t even look like an idea. It looked like a place. It looked like a person, or some life force that existed long before I did, separate from me, but also part of me. Waiting for me, but also hiding from me. That might sound a little high-flown, a little crazy. But that’s how I felt back then.

…At twenty-four, I did have a crazy idea, and somehow, despite being dizzy with existential angst, and fears about the future, and doubts about myself, as all young men and women in their midtwenties are, I did decide that the world is made up of crazy ideas. History is one long processional of crazy ideas. The things I loved most – books, sports, democracy, free enterprise – started as crazy ideas.

For that matter, few ideas are as crazy as my favorite thing, running. It’s hard. It’s painful. It’s risky. The rewards are few and far from guaranteed… Whatever pleasures or gains you drive from the act of running, you must find them within. It’s all in how you frame it, how you sell it to yourself.

(Runner silhouette, Illustration by Msanca)

Knight:

So that morning in 1962 I told myself: Let everyone else call your idea crazy… just keep going. Don’t stop. Don’t even think about stopping until you get there, and don’t give much thought to where ‘there’ is. Whatever comes, just don’t stop.

That’s the precocious, prescient, urgent advice I managed to give myself, out of the blue, and somehow managed to take. Half a century later, I believe it’s the best advice – maybe the only advice – any of us should ever give.

 

1962

Knight explains that his crazy idea started as a research paper for a seminar on entrepreneurship at Stanford. He became obsessed with the project. As a runner, he knew about shoes. He also knew that some Japanese products, such as cameras, had recently gained much market share. Perhaps Japanese running shoes might do the same thing.

When Knight presented his idea to his classmates, everyone was bored. No one asked any questions. But Knight held on to his idea. He imagined pitching it to a Japanese shoe company. Knight also conceived of the idea of seeing the world on his way to Japan. He wanted to see “the world’s most beautiful and wondrous places.”

And its most sacred. Of course I wanted to taste other foods, hear other languages, dive into other cultures, but what I really craved was connection with a capital C. I wanted to experience what the Chinese call Tao, the Greeks call Logos, the Hindus call Jnana, the Buddhists call Dharma. What the Christians call Spirit. Before setting out on my own personal life voyage, I thought, let me first understand the greater voyage of humankind. Let me explore the grandest temples and churches and shrines, the holiest rivers and mountaintops. Let me feel the presence of… God?

Yes, I told myself, yes. For lack of a better word, God.

But Knight needed his father’s blessing and cash in order to make the trip around the world.

At the time, most people had never been on an airplane. Also, Knight’s father’s father had died in an air crash. As for the shoe company idea, Knight was keenly aware that twenty-six out of twenty-seven new companies failed. Knight then notes that his father, besides being a conventional Episcopalian, also liked respectability. Traveling around the world just wasn’t done except by beatniks and hipsters.

Knight then adds:

Possibly, the main reason for my father’s respectability fixation was a fear of his inner chaos. I felt this, viscerally, because every now and then that chaos would burst forth.

Knight tells about having to pick his father up from his club. On these evenings, Knight’s father had had too much to drink. But father and son would pretend nothing was wrong. They would talk sports.

Knight’s mom’s mom, “Mom Hatfield” – from Roseburg, Oregon – warned “Buck” (Knight’s nickname) that the Japanese would take him prisoner and gouge out his eyeballs. Knight’s sisters, four years younger (twins), Jeanne and Joanne, had no reaction. His mom didn’t say anything, as usual, but seemed proud of his decision.

Knight asked a Stanford classmate, Carter, a college hoops star, to come with him. Carter loved to read good books. And he liked Buck’s idea.

The first stop was Honolulu. After seeing Hawaiian girls, then diving into the warm ocean, Buck told Carter they should stay. What about the plan? Plans change. Carter liked the new idea and grinned.

They got jobs selling Encyclopedias door-to-door. But their main mission was learning how to surf. “Life was heaven.” Except that Buck couldn’t sell encyclopedias. He thought he was getting shier as he got older.

So he tried a job selling securities. Specifically, Dreyfus funds for Investors Overseas Services, Bernard Cornfeld’s firm. Knight had better luck with this.

Eventually, the time came for Buck and Carter to continue on their trip around the world. However, Carter wasn’t sure.

He’d met a girl. A beautiful Hawaiian teenager with long brown legs and jet-black eyes, the kind of girl who’d greeted our airplane, the kind I dreamed of having and never would. He wanted to stick around, and how could I argue?

Buck hesitated, not sure he wanted to continue on alone. But he decided not to stop his journey. He bought a plane ticket that was good for one year on any airline going anywhere.

When Knight got to Tokyo, much of the city was black because it still hadn’t been rebuilt after the bombing.

American B-29s. Superfortresses. Over a span of several nights in the summer of 1944, waves of them dropped 750,000 pounds of bombs, most filled with gasoline and flammable jelly. One of the world’s oldest cities, Tokyo was made largely of wood, so the bombs set off a hurricane of fire. Some three hundred thousand people were burned alive, instantly, four times the number who died in Hiroshima. More than a million were gruesomely injured. And nearly 80 percent of the buildings were vaporized. For long, solemn stretches the cab driver and I said nothing. There was nothing to say.

Fortunately, Buck’s father knew some people in Tokyo at United Press International. They advised Buck to talk to two ex-GI’s who ran a monthly magazine, theImporter.

First, Knight spent long periods of time in walled gardens reading about Buddhism and Shinto. He liked the concept of kensho, or sartori – a flash of enlightenment.

But according to Zen, reality is nonlinear. No past, no present. All is now. That required Knight to change his thinking. There is no self. Even in competition, all is one.

Knight decided to mix it up and visited the Tokyo Stock Exchange – Tosho. All was madness and yelling. Is this what it’s all about?

Knight sought peace and enlightenment again. He visited the garden of the nineteenth century emperor Meiji and his empress. This particular place was thought to possess great spiritual power. Buck sat beneath the ginkgo trees, beside the gorgeous torii gate, which was thought of as a portal to the sacred.

Next it was Tsukiji, the world’s largest fish market. Tosho all over again.

Then to the lakes region in the Northern Hakone mountains. An area that inspired many of the great Zen poets.

Knight went to see the two ex-GI’s. They told him how they’d fallen in love with Japan during the Occupation. So they stayed. They had managed to keep the import magazine going for seventeen years thus far.

Knight told them he liked the Tiger shoes produced by Onitsuka Co. in Kobe, Japan. The ex-GI’s gave him tips on negotiating with the Japanese:

‘No one ever turns you down, flat. No one ever says, straight out, no. But they don’t say yes, either. They speak in circles, sentences with no clear subject or object. Don’t be discouraged, but don’t be cocky. You might leave a man’s office thinking you’ve blown it, when in fact he’s ready to do a deal. You might leave thinking you’ve closed a deal, when in fact you’ve just been rejected. You never know.’

Knight decided to visit Onitsuka right away, with the advice fresh in his mind. He managed to get an appointment, but got lost and arrived late.

When he did arrive, several executives met him. Ken Miyazaki showed him the factory. Then they went to a conference room.

Knight had rehearsed this scene his head, just like he used to visualize his races. But one thing he hadn’t prepared for was the recent history of World War II hanging over everything. The Japanese had heroically rebuilt, putting the war behind them. And these Japanese executives were young. Still, Knight thought, their fathers and uncles had tried to kill his. In brief, Knight hesitated and coughed, then finally said, “Gentlemen.”

Mr. Miyazaki interrupted, “Mr. Knight. What company are you with?”

Knight replied, “Ah, yes. Good question.” Knight experienced fight or flight for a moment. A random jumble of thoughts flickered in his mind until he visualized his wall of blue ribbons from track. “Blue Ribbon… Gentleman, I represent Blue Ribbon Sports of Portland, Oregon.”

Knight presented his basic argument, which was that the American shoe market was huge and largely untapped. If Onitsuka could produce good shoes and price them below Adidas, it could be highly profitable. Knight had spent so much time on his research paper at Stanford that he could simply quote it and come across as eloquent.

The Japanese executives started talking excitedly together, then suddenly stood up and left the room. Knight didn’t know if he had been rejected. Perhaps he should leave. He waited.

Then they came back into the room with sketches of different Tiger shoes. They told him they had been thinking about the American market for some time. They asked Knight how big he thought the market could be. Knight tossed out, “$1 billion.” He doesn’t know where the number came from.

They asked him if Blue Ribbon would be interested in selling Tigers in the United States. Yes, please send samples to this address, Knight said, and I’ll send a money order for fifty dollars.

Knight considered returning home to get a jump on the new business. But then he decided to finish his trek around the world.

Hong Kong, then the Phillipines.

I was fascinated by all the great generals, from Alexander the Great to George Patton. I hated war, but I loved the warrior spirit. I hated the sword, but loved the samurai. And of all the great fighting men in history I found MacArthur the most compelling. Those Ray-Bans, that corncob pipe – the man didn’t lack for confidence. Brilliant tactician, master motivator, he also went on to head the U.S. Olympic Committee. How could I not love him?

Of course, he was deeply flawed. But he knew that…

Bangkok. He made his way to Wat Phra Kaew, a huge 600-year-old Buddha carved from one hunk of jade. One of the most sacred statues in Asia.

(Emerald Buddha at Wat Phra Kaew, Image by J. P. Swimmer, Wikimedia Commons)

Vietnam, where U.S. soldiers filled the streets. Everyone knew a very ugly and different war was coming.

Calcutta. Knight got sick immediately. He thinks food poisoning. He was sure, for one whole day, that he was going to die. He rallied. He ended up at the Ganges. There was a funeral. Others were bathing. Others were drinking the same water.

“The Upanishads say,Lead me from the unreal to the real.” So Knight went to Kathmandu and hiked up the Himalayas.

Back to India. Bombay.

Kenya. Giant ostriches tried to outrun the bus, records Knight. When Masai warriors boarded the bus, a baboon or two would also try to board.

Cairo. The Giza plateau. Standing besides desert nomads with their silk-draped camels. At the foot of the Great Sphinx.

…The sun hammered down on my head, the same sun that hammered down on the thousands of men who built these pyramids, and the millions of visitors who came after. Not one of them was remembered, I thought. All is vanity, says the Bible. All is now, says Zen. All is dust, says the desert.

(Great Sphinx of Giza,Photo by Johnny 201, Wikimedia Commons)

Then Jerusalem.

…the first century rabbi Eleazar ben Azariah said our work is the holiest part of us. All are proud of their craft. God speaks of his work; how much more should man.

Istanbul. Turkish coffee. Lost on the confusing streets of the Bosphorus. Glowing minarets. Then the golden labyrinths of Topkapi Palace.

Rome. Tons of pasta. And the most beautiful women and shoes he’d ever seen, says Knight. The Coliseum. The Vatican. The Sistine Chapel.

Florence. Reading Dante. Milan. Da Vinci: One of his obsessions was the human foot, which he called a masterpiece of engineering.

Venice. Marco Polo. The palazzo of Robert Browning: “If you get simply beauty and naught else, you get about the best thing God invents.”

Paris. The Pantheon. Rousseau. Voltaire: “Love truth, but pardon error.” Praying at Notre Dame. Lost in the Louvre.

(The Louvre,Photo by Pipiten, Wikimedia Commons)

Then to where Joyce slept, and F. Scott Fitzgerald. Walking down the Seine, and stopping where Hemingway and Dos Passos read the New Testament aloud to each other.

Next, up the Champs-Elysees, along the liberators’ path, thinking of Patton: “Don’t tell people how to do things, tell them what to do and let them surprise you with their results.”

Munich. Berlin. East Berlin:

…I looked around, all directions. Nothing. No trees, no stores, no life. I thought of all the poverty I’d seen in every corner of Asia. This was a different kind of poverty, more willful, somehow, more preventable. I saw three children playing in the street. I walked over, took their picture. Two boys and a girl, eight years old. The girl – red wool hat, pink coat – smiled directly at me. Will I ever forget her? Or her shoes? They were made of cardboard.

Vienna. Stalin, Trotsky, Tito, Hitler, Jung, Freud. All at the same location in the same time period. A “coffee-scented crossroads.” Where Mozart walked. Crossing the Danube. The spires of St. Stephen’s Church, where Beethoven realized he was deaf.

London. Buckingham Palace, Speakers’ Corner, Harrods.

Knight asked himself what the highlight of his trip was.

Greece, I thought. No question. Greece.

…I meditated on that moment, looking up at those astonishing columns, experiencing that bracing shock, the kind you receive from all great beauty, but mixed with a powerful sense of – recognition?

Was it only my imagination? After all, I was standing at the birthplace of Western civilization. Maybe I merely wanted it to be familiar. But I don’t think so. I had the clearest thought: I’ve been here before.

Then, walking up those bleached steps, another thought: This is where it all begins.

On my left was the Parthenon, which Plato had watched the teams of architects and workmen build. On my right was the Temple of Athena Nike. Twenty-five centuries ago, per my guidebook, it had housed a beautiful frieze of the goddess Athena, thought to be the bringer of “nike,” or victory.

It was one of many blessings Athena bestowed. She also rewarded the dealmakers. In the Oresteia she says: ‘I admire… the eyes of persuasion.’ She was, in a sense, the patron saint of negotiators.

(Temple of Athena Nike, Photo by Steve Swayne, Wikimedia Commons)

 

1963

When Buck got home, his hair was to his shoulders and his beard three inches long. It had been four months since meeting with Onitsuka. But they hadn’t sent the sample shoes. Knight wrote to them to ask why. They wrote back, “Shoes coming… In a little more days.”

Knight got a haircut and shaved. He was back. His father suggested he speak with his old friend, Don Frisbee, CEO of Pacific Power & Light. Frisbee had an MBA from Harvard. Frisbee told Buck to get his CPA while he was young, a relatively conservative way to put a floor under his earnings. Knight liked that idea. He had to take three more courses in accounting, first, which he promptly did at Portland State.

Then Knight worked at Lybrand, Ross Bros. & Montgomery. It was a Big Eight national firm, but its Portland office was small. $500 a month and some solid experience. But pretty boring.

 

1964

Finally, twelve pairs of shoes arrived from Onitsuka. They were beautiful, writes Knight. He sent two pairs immediately to his old track coach at Oregon, Bill Bowerman.

Bowerman was a genius coach, a master motivator, a natural leader of young men, and there was one piece of gear he deemed crucial to their development. Shoes.

Bowerman was obsessed with shoes. He constantly took his runners’ shoes and experimented on them. He especially wanted to make the shoes lighter. One ounce over a mile is fifty pounds.

Bowerman would try anything. Kangaroo. Cod. Knight says four or five runners on the team were Bowerman’s guinea pigs. But Knight was his “pet project.”

It’s possible that everything I did in those days was motivated by some deep yearning to impress, to please, Bowerman. Besides my father there was no man whose approval I craved more, and besides my father there was no man who gave it less often. Frugality carried over to every part of the coach’s makeup. He weighed and hoarded words of praise, like uncut diamonds.

After you’d won a race, if you were lucky, Bowerman might say: ‘Nice race.’ (In fact, that’s precisely what he said to one of his milers after the young man became one of the very first to crack the mythical four-minute mark in the United States.) More likely Bowerman would say nothing. He’d stand before you in his tweed blazer and ratty sweater vest, his string tie blowing in the wind, his battered ball cap pulled low, and nod once. Maybe stare. Those ice-blue eyes, which missed nothing, gave nothing. Everyone talked about Bowerman’s dashing good looks, his retro crew cut, his ramrod posture and planed jawline, but what always got me was that gaze of pure violet blue.

(Statue of Bill Bowerman, Photo by Diane Lee Jackson, Wikimedia Commons)

For his service in World War II, Bowerman received the Silver Star and four Bronze Stars. Bowerman eventually became the most famous track coach in America. But he hated being called “coach,” writes Knight. He called himself, “Professor of Competitive Responses” because he viewed himself as preparing his athletes for the many struggles and competitions that lay ahead in life.

Knight did his best to please Bowerman. Even so, Bowerman would often lose patience with Knight. On one occasion, Knight told Bowerman he was coming down with the flu and wouldn’t be able to practice. Bowerman told him to get his ass out there. The team had a time trial that day. Knight was close to tears. But he kept his composure and ran one of his best times of the year. Bowerman gave him a nod afterward.

Bowerman suggested meeting for lunch shortly after seeing the Tiger shoes from Onitsuka. At lunch, Bowerman told Knight the shoes were pretty good and suggested they become business partners. Knight was shocked.

Had God himself spoken from the whirlwind and asked to be my partner, I wouldn’t have been more surprised.

Knight and Bowerman signed an agreement soon thereafter. Knight found himself thinking again about his coach’s eccentricities.

…He always went against the grain. Always. For example, he was the first college coach in America to emphasize rest, to place as much value on recovery as on work. But when he worked you, brother, he worked you. Bowerman’s strategy for running the mile was simple. Set a fast pace for the first two laps, run the third as hard as you can, then triple your speed on the fourth. There was a Zen-like quality to this strategy because it was impossible. And yet it worked. Bowerman coached more sub-four-minute milers than anybody, ever.

Knight wrote Onitsuka and ordered three hundred pairs of shoes, which would cost $1,ooo. Buck had to ask his dad for another loan, who asked him, “Buck, how long do you think you’re going to keep jackassing around with these shoes?” His father told him he didn’t send him to Oregon and Stanford to be a door-to-door shoe salesman.

At this point, Knight’s mother told him she wanted to purchase a pair of Tigers. This helped convince Knight’s father to give him another loan.

In April 1964, Knight got the shipment of Tigers. Also, Mr. Miyazaki told him he could be the distributor for Onitsuka in the West. Knight quit his accounting job to focus on selling shoes that spring. His dad was horrified, his mom happy, remarks Knight.

After being rejected by a couple of sporting goods stores, Knight decided to travel around to various track meets in the Pacific Northwest. Between races, he’d talk with the coaches, the runners, the fans. He couldn’t write the orders fast enough. Knight wondered how this was possible, given his inability to sell encyclopedias.

…So why was selling shoes so different? Because, I realized, it wasn’t selling. I believed in running. I believed that if people got out and ran a few miles every day, the world would be a better place, and I believed these shoes were better to run in. People, sensing my belief, wanted some of that belief for themselves.

Belief, I decided. Belief is irresistable.

(Illustration by Lkeskinen0)

Knight started the mail order business because he started getting letters from folks wanting Tigers. To help the process along, he mailed some handouts with big type:

‘Best news in flats! Japan challenges European track shoe domination! Low Japanese labor costs make it possible for an exciting new firm to offer these shoes at the low, low price of $6.95.’ [Note: This is close to $54 in 2018 dollars, due to inflation.]

Knight had sold out his first shipment by July 4, 1964. So he ordered 900 more. This would cost $3,000. His dad grudgingly gave him a letter of guarantee, which Buck took to the First National Bank of Oregon. They approved the loan.

Knight wondered how to sell in California. He couldn’t afford airfare. So every other weekend, he’d stuff a duffel bag with Tigers. He’d don his army uniform and head to the local air base. The MPs would wave him on to the next military transport to San Francisco or Los Angeles.

When in Los Angeles, he’d save more money by staying with a friend from Stanford, Chuck Cale. At a meet at Occidental College, a handsome guy approached Knight, introducing himself as Jeff Johnson. He was a fellow runner whom Knight had run with and against while at Stanford. At this point, Johnson was studying anthropology and planning on becoming a social worker. But he was selling shoes – Adidas then – on weekends. Knight tried to recruit him to sell Tigers instead. No, because he was getting married and needed stability, responded Johnson.

Then Knight got a letter from a high school wrestling coach in Manhasset, New York, claiming that Onitsuka had named him the exclusive distributor for Tigers in the United States. He ordered Knight to stop selling Tigers.

Knight contacted his cousin, Doug Houser, who’d recently graduated from Stanford Law School. Houser found out Mr. Manhasset was a bit of a celebrity, a model who was one of the original Marlboro Men. Knight: “Just what I need. A pissing match with some mythic American cowboy.”

Knight went into a funk for awhile. Then he decided to go visit Onitsuka in Japan. Knight bought a new suit and also a book,How to Do Business with the Japanese.

Knight realized he had to remain cool. Emotion could be fatal.

The art of competition, I’d learned from track, was the art of forgetting, and now I reminded myself of that fact. You must forget your limits. You must forget your doubts, your pain, your past. You must forget that internal voice screaming, begging, ‘Not one more step!’ And when it’s not possible to forget it, you must negotiate with it. I thought over all the races in which my mind wanted one thing, and my body wanted another, those laps in which I’d had to tell my body, ‘Yes, you raise some excellent points, but let’s keep going anyway…’

After finding a place to stay in Kobe, Knight called Onitsuka and requested a meeting. He got a call back saying Mr. Miyazaki no longer worked there. Mr. Morimoto had replaced him, and didn’t want Knight to visit headquarters. Mr. Morimoto would meet him for tea. None of this was good.

At the meeting, Knight layed out the arguments. They had had an agreement. He also pointed out the very robust sales Blue Ribbon had had thus far. He dropped the name of his business partner. Mr. Morimoto, who was about Knight’s age, said he’d get back to him.

Knight thought it was over. But then he got a call from Morimoto saying, “Mr. Onitsuka…himself… wishes to see you.”

At this meeting, Knight first presented his arguments again to those who were initially present. Then Mr. Onitsuka arrived.

Dressed in a dark blue Italian suit, with a head of black hair as thick as shag carpet, he filled every man in the conference room with fear. He seemed oblivious, however. For all his power, for all his wealth, his movements were deferential… Morimoto tried to summarize my reasons for being there. Mr. Onitsuka raised a hand, cut him off.

Without preamble, he launched into a long, passionate monologue. Some time ago, he’d said, he’d had a vision. A wondrous glimpse of the future. ‘Everyone in the world wear athletic shoes all the time,’ he said. ‘I know this day will come.’ He paused, looking around the table at each person, to see if they also knew. His gaze rested on me. He smiled. I smiled. He blinked twice. ‘You remind me of myself when I am young,’ he said softly. His stared into my eyes. One second. Two. Now he turned his gaze to Morimoto. ‘This about those thirteen western states?’ he said. ‘Yes,’ Morimoto said. ‘Hm,’ Onitsuka said. ‘Hmmmm.’ He narrowed his eyes, looked down. He seemed to be meditating. Again he looked up at me. ‘Yes,’ he said. ‘Alright. You have western states.’

Knight ordered $3,400 worth of shoes [about $26,000 in 2018 dollars].

(Mount Fuji, Photo by Wipark Kulnirandorn)

To celebrate, Knight decided to climb to the top of Mount Fuji. Buck met a girl on the wap up, Sarah, who was studying philosophy at Connecticut College for Women. It went well for a time. Many letters back and forth. A couple of visits. But she decided Knight wasn’t “sophisticated” enough. Jeanne, one of Buck’s younger sisters, found the letters, read them, and told Buck, “You’re better off without her.” Buck then asked his sister – given her interest in mail – if she’d like to help with the mail order business for $1.50 an hour. Sure. Blue Ribbon Employee Number One.

 

1965

Buck got a letter from Johnson. He’d bought some Tigers and loved them. Could he become a commissioned salesman for Blue Ribbon? Sure. $1.75 for each pair of running shoes, $2 for spikes, were the commissions.

Then the letters from Johnson kept coming:

I liked his energy, of course. And it was hard to fault his enthusiasm. But I began to worry he might have too much of each. With the twentieth letter, or the twenty-fifth, I began to worry that the man might be unhinged. I wondered why everything was so breathless. I wondered if he was ever going to run out of things he urgently needed to tell me, or ask me…

…He wrote to say that he wanted to expand his sales territory beyond California, to include Arizona, and possibly New Mexico. He wrote to suggest that we open a retail store in Los Angeles. He wrote to tell me that he was considering placing ads in running magazines and what did I think? He wrote to inform me that he’d placed those ads in running magazines and the response was good. He wrote to ask why I hadn’t answered any of his previous letters. He wrote to plead for encouragement. He wrote to complain that I hadn’t responded to his previous plea for encouragement.

I’d always considered myself a conscientious correspondent… And I always meant to answer Johnson’s letters. But before I got around to it, there was always another one, waiting. Something about the sheer volume of his correspondence stopped me…

Eventually Johnson realized he loved shoes and running more than anthropology or social work.

(Monk meditating, Photo by Ittipon)

In his heart of hearts Johnson believed that runners are God’s chosen, that running, done right, in the correct spirit and with the proper form, is a mystical exercise, no less than meditation or prayer, and thus he felt called to help runners reach their nirvana. I’d been around runners much of my life, but this kind of dewy romanticism was something I’d never encountered. Not even the Yahweh of running, Bowerman, was as pious about the sport as Blue Ribbon’s Part-Time Employee Number Two.

In fact, in 1965, running wasn’t even a sport. It wasn’t popular, it wan’t unpopular, it just was. To go out for a three-mile run was something weirdos did, presumably to burn off manic energy. Running for exercise, running for pleasure, running for endorphins, running to live better and longer – these things were unheard of.

People often went out of their way to mock runners. Drivers would slow down and honk their horns. ‘Get a horse!,’ they’d yell, throwing a beer or soda at the runner’s head. Johnson had been drenched by many a Pepsi. He wanted to change all this…

Above all, he wanted to make a living doing it, which was next to impossible in 1965. In me, in Blue Ribbon, he thought he saw a way.

I did everything I could to discourage Johnson from thinking like this. At every turn, I tried to dampen his enthusiasm for me and my company. Besides not writing back, I never phoned, never visited, never invited him to Oregon. I also never missed an opportunity to tell him the unvarnished truth. I put it flatly: ‘Though our growth has been good, I owe First National Bank of Oregon $11,000… Cash flow is negative.’

He wrote back immediately, asking if he could work for me full-time…

Knight just shook his head. Finally in last summer of 1965, Knight accepted Johnson’s offer. Johnson had been making $460 as a social worker, so he proposed $400 a month [over $3,000 a month in 2018 dollars]. Knight very reluctantly agreed. It seemed like a huge sum. Knight writes:

As ever, the accountant in me saw the risk, the entrepreneur the possibility. So I split the difference and kept moving forward.

Knight then forgot about Johnson because he had bigger issues. Blue Ribbon had doubled its sales in one year. But Knight’s banker said they were growing too fast for their equity. Knight asked how doubling sales – profitably – can be a bad thing.

In those days, however, commercial banks were quite different from investment banks. Commercial banks never wanted you to outgrow your cash balance. Knight tried to explain that growing sales as much as possible – profitably – was essential to convince Onitsuka to stick with Blue Ribbon. And then there’s the monster, Adidas. But his banker kept repeating:

‘Mr. Knight, you need to slow down. You don’t have enough equity for this kind of growth.’

Knight kept hearing the word “equity” in his head over and over. “Cash,” that’s what it meant. But he was deliberately reinvesting every dollar – on a profitable basis. What was the problem?

Every meeting with his banker, Knight managed to hold his tongue and say nothing, basically agreeing. Then he’d keep doubling his orders from Onitsuka.

Knight’s banker, Harry White, had essentially inherited the account. Previously, Ken Curry was Knight’s banker, but Curry bailed when Knight’s father wouldn’t guarantee the account in the case of business failure.

Furthermore, the fixation on equity didn’t come from White, but from White’s boss, Bob Wallace. Wallace wanted to be the next president of the bank. Credit risks were the main roadblock to that goal.

Oregon was smaller back then. First National and U.S. Bank were the only banks, and the second one had already turned Blue Ribbon down. So Knight didn’t have a choice. Also, there as no such thing as venture capital in 1965.

(First National Bank of Oregon, Photo by Steve Morgan, Wikimedia Commons)

To make matters worse, Onitsuka was always late in its shipments, no matter how much Knight pleaded with them.

By this point, Knight had passed the four parts of the CPA exam. So he decided to get a job as an accountant. He invested a good chunk of his paycheck into Blue Ribbon.

In analyzing companies as an accountant, Knight learned how they sold things or didn’t, how they survived or didn’t. He learned how companies got into trouble and how they got out.

It was while working for the Portland branch of Price Waterhouse that he met Delbert J. Hayes, who was the best accountant in the office. Knight describes Hayes as a man with “great talent, great wit, great passions – and great appetites.” Hayes was six-foot-two and three hundred pounds. He loved food and alcohol. And he smoked two packs a day.

Hayes looked at numbers the way a poet looks at clouds or a geologist looks at rocks, says Knight. He could see the beauty of numbers. Numbers were a secret code.

Every evening, Hayes would insist on taking junior accountants out for a drink. Hayes would talk nonstop, like he drank. But while other accountants dismissed Hayes’ stories, Knight always paid careful attention. In every tale told by Hayes was some piece of wisdom about business. So Knight would match Hayes, shot for shot, in order to learn as much as he could.

The following morning, Knight was always sick. But he willed himself to do the work. Being in the Army Reserves at the same time wasn’t easy. Meanwhile, the conflict in Vietnam was heating up. Knight:

I had grown to hate that war. Not simply because I felt it was wrong. I also felt it was stupid, wasteful. I hated stupidity. I hated waste. Above all, that war, more than other wars, seemed to be run along the same principles of my bank. Fight not to win, but to avoid losing. A surefire losing strategy.

Hayes came to appreciate Knight. Hayes thought it was a tough time to launch a new company with zero cash balance. But he did acknowledge that having Bowerman as a partner was a valuable, intangible asset.

Recently, Bowerman and Mrs. Bowerman had visited Onitsuka and charmed everyone. Mr. Onitsuka told Bowerman about founding his shoe company in the rubble after World War II.

He’d built his first lasts, for a line of basketball shoes, by pouring hot wax from Buddhist candles over his own feet. Though the basketball shoes didn’t sell, Mr. Onitsuka didn’t give up. He simply switched to running shoes, and the rest is shoe history. Every Japanese runner in the 1964 Games, Bowerman told me, was wearing Tigers.

Mr. Onitsuka also told Bowerman that the inspiration for the unique soles on Tigers had come to him while eating sushi. Looking down at his wooden platter, at the underside of an octopus’s leg, he thought a similar suction cup might work on the sole of a runner’s flat. Bowerman filed that away. Inspiration, he learned, can come from quotidian things. Things you might eat. Or find lying around the house.

Bowerman started corresponding not only with Mr. Onitsuka, but with the entire production team at the Onitsuka factory. Bowerman realized that Americans tend to be longer and heavier than the Japanese. He thought the Tigers could be modified to fit Americans better. Most of Bowerman’s letters went unanswered, but like Johnson Bowerman just kept writing more.

Eventually he broke through. Onitsuka made prototypes that conformed to Bowerman’s vision of a more American shoe. Soft inner sole, more arch support, heel wedge to reduce stress on the Achilles tendon – they sent the prototype to Bowerman and he went wild for it. He asked for more.

Bowerman also experimented with drinks to help his runners recover. He invented an early version of Gatorade. As well, he conducted experiments to make the track softer. He invented an early version of polyurethane.

 

1966

Johnson kept inundating Knight with long letters, including a boatload of parenthetical comments and a list of PS’s. Knight felt he didn’t have time to send the requested words of encouragement. Also, it wasn’t his style.

I look back now and wonder if I was truly being myself, or if I was emulating Bowerman, or my father, or both. Was I adopting their man-of-few-words demeanor? Was I maybe modeling all the men I admired? At the time I was reading everything I could get my hands on about generals, samurai, shoguns, along with biographies of my three main heroes – Churchill, Kennedy, and Tolstoi. I had no love of violence, but I was fascinated by leadership, or lack thereof, under extreme conditions…

I wasn’t that unique. Throughout history men have looked to the warrior for a model of Hemingway’s cardinal virtue, pressurized grace… One lesson I took from all my home-schooling about heroes was that they didn’t say much. None was a blabbermouth. None micromanaged. “Don’t tell people how to do things, tell them what to do and let them surprise you with their results.”

(Winston Churchill in 1944, Wikimedia Commons)

Johnson never let Knight’s lack of communication discourage him. Johnson was full of energy, passion, and creativity. He was going all-out, seven days a week, to sell Blue Ribbon shoes. Johnson had an index card for each customer including their shoe sizes and preferences. He sent all of them birthday cards and Christmas cards. Johnson developed extensive correspondence with hundreds of customers.

Johnson began aggregating customer feedback on the shoes.

…One man, for instance, complained that Tiger flats didn’t have enough cushion. He wanted to run the Boston Marathon but didn’t think the Tigers would last the twenty-six miles. So Johnson hired a local cobbler to graft rubber soles from a pair of shower shoes into a pair of Tiger flats. Voila. Johnsn’s Frankenstein flat had space-age, full-length, midsole cushioning. (Today it’s standard in all training shoes for runners.) The jerry-rigged Johnson sole was so dynamic, so soft, so new, Johnson’s customer posted a personal best in the Boston. Johnson forwarded me the results and urged me to pass them along to Tiger. Bowerman had just asked me to do the same with his batch of notes a few weeks earlier. Good grief, I thought, one mad genius at a time.

Johnson had customers in thirty-seven states. Knight meant to warn him about encroaching on Malboro Man’s territory. But he never got around to it.

Knight did write to tell Johnson that if he could sell 3,250 shoes by the end of June 1966, then he could open the retail outlet he’d been asking about. Knight calculated that 3,250 was impossible, so he wasn’t too worried.

Somehow Johnson hit 3,250. So Blue Ribbon opened its first retail store in Santa Monica.

He then set about turning the store into a mecca, a holy of holies for runners. He bought the most comfortable chairs he could find, and afford (yard sales), and he created a beautiful space for runners to hang out and talk. He built shelves and filled them with books that every runner should read, many of them first editions from his own library. He covered the walls with photos of Tiger-shod runners, and laid in a supply of silk-screened T-shirts withTiger across the front, which he handed out to his best customers. He also stuck Tigers to a black lacquered wall and illuminated them with a strip of can lights – very hip. Very mod. In all the world, there had never been a sanctuary for runners, a place that didn’t just sell them shoes but celebrated them and their shoes. Johnson, the aspiring cult leader of runners, finally had his church. Services were Monday through Saturday, nine to six.

When he first wrote me about the store, I thought of the temples and shrines I’d seen in Asia, and I was anxious to see how Johnson’s compared. But there just wasn’t time…

Knight got a heads up that the Marlboro man had just launched an advertising campaign which involved poaching customers of Blue Ribbon. So Knight flew down to see Johnson.

(Jeff Johnson, Employee Number One)

Johnson’s apartment was one giant running shoe. There were running shoes seemingly everywhere. And there were many books – mostly thick volumes on philosophy, religion, sociology, anthropology, and classics in Western literature. Knight had thought he liked to read. This was a new level, says Knight.

Johnson told Knight he had to go visit Onitsuka again. Johnson started typing notes, ideas, lists, which would become a manifesto for Knight to take to Onitsuka. Knight wired Onitsuka. They got back to him, but it wasn’t Morimoto. It was a new guy, Kitami.

Knight told Kitami and other executives about the performance of Blue Ribbon thus far, virtually doubling sales each year and projecting more of the same. Kitami said they wanted someone more established, with offices on the East Coast. Knight replied that Blue Ribbon had offices on the East Coast and could handle national distribution. “Well,” said Kitami, “this changes things.”

The next morning, Kitami awarded Blue Ribbon exclusive distribution rights for the United States. A three-year contract. Knight promptly placed an order for 5,000 more shoes, which would cost $20,000 – more than $150,000 in 2018 dollars – that he didn’t have. Kitami said he would ship them to Blue Ribbon’s East Coast office.

There was only one person crazy enough to move to the East Coast on a moment’s notice….

 

1967

Knight delayed telling Johnson. Then he hired John Bork, a high school track coach and a friend of a friend, to run the Santa Monica store. Bork showed up at the store and told Johnson that he, Bork, was the new boss so that Johnson could go back east.

Johnson called Knight. Knight told him he’d had to tell Onitsuka that Blue Ribbon had an east coast office. A huge shipment was due to arrive at this office. Johnson was the only one who could manage the east coast store. The fate of the company was on his shoulders. Johnson was shocked, then mad, then freaked out. Knight flew down to visit him.

Johnson talked himself into going to the east coast.

The forgiveness Johnson showed me, the overall good nature he demonstrated, filled me with gratitude, and a new fondness for the man. And perhaps a deeper loyalty. I regretted my treatment of him. All those unanswered letters. There are team players, I thought, and then there are team players, and then there’s Johnson.

Soon thereafter, Bowerman called asking Knight to add a new employee – Geoff Hollister. A former track guy. Full-time Employee Number Three.

Then Bowerman called again with yet another employee – Bob Woodell.

I knew the name, of course. Everyone in Oregon knew the name. Woodell had been a standout on Bowerman’s 1965 team. Not quite a star, but a gritty and inspiring competitor. With Oregon defending its second national championship in three years, Woodell had come out of nowhere and won the long jump against vaunted UCLA. I’d been there, I’d watched him do it, and I’d come away mighty impressed.

The very next day, during a celebration, there had been an accident. The float twenty guys were carrying collapsed after someone lost their footing. It landed on Woodell and crushed one of his vertebra, paralyzing his legs.

Knight called Woodell. Knight realized it was best to keep it strictly business. So he told Woodell that Bowerman had recommended him. Would he like to grab lunch to discuss the possibility of working for Blue Ribbon? Sure thing, he said.

Woodell had already mastered a special car, a Mercury Cougar with hand controls. At lunch, they hit it off and Woodell impressed Knight.

I wasn’t certain what Blue Ribbon was, or if it would ever become a thing at all, but whatever it was or might become, I hoped it would have something of this man’s spirit.

Knight offered Woodell a job opening a second retail store, in Eugene, for a monthly salary of $400. Woodell immediately agreed. They shook hands. “He still hand the strong grip of an athlete.”

(Bob Woodell 1967)

Bowerman’s latest experiment was with the Spring Up. He noticed the outer sole melted, whereas the midsole remained solid. He convinced Onitsuka to fuse the outer sole to the midsole. The result looked like the ultimate distance training shoe. Onitsuka also accepted Bowerman’s suggestion of a name for the shoe, the “Aztec,” in homage to the upcoming 1968 Olympics in Mexico City. Unfortunately, Adidas had a similar name for one of its shoes and threatened to sue. So Bowerman changed the name to “Cortez.”

The situation with Adidas reminded Knight of when he had been a runner in high school. The fastest runner in the state was Jim Grelle (pronounced “Grella”) and Knight had been second-fastest. So Knight spent many races staring at Grelle’s back. Then they both went to Oregon, so Knight spent more years staring at Grelle’s back.

Adidas made Knight think of Grelle. Knight felt super motivated.

Once again, in my quixotic effort to overtake a superior opponent, I had Bowerman as my coach. Once again he was doing everything he could to put me in position to win. I often drew on the memory of his old prerace pep talks, especially when we were up against our blood rivals, Oregon State. I would replay Bowerman’s epic speeches… Nearly sixty years later it gives me chills to recall his words, his tone. No one could get your blood going like Bowerman, though he never raised his voice.

Thanks to the Cortez, Blue Ribbon finished the year strong. They had nearly doubled their sales again, to $84,000. Knight rented an office for $50 a month. And he transferring Woodell to the “home office.” Woodell had shown himself to be highly skilled and energetic, and in particular, he was excellent at organizing.

The office was cold and the floor was warped. But it was cheap. Knight built a corkboard wall, pinning up different Tiger models and borrowing some of Johnson’s ideas from the Santa Monica store.

Knight thought perhaps he could save even more money by living at his office. Then he reflected that living at your office was what a crazy person does. Then he got a letter from Johnson saying he was living at his office. Johnson had set up shop in Wellesley, a suburb of Boston.

Johnson told Knight how he had chosen the location. He’d seen people running along country roads, many of them women. Ali MacGraw look-alikes. Sold.

 

1968

Knight:

I wanted to dedicate every minute of every day to Blue Ribbon… I wanted to be present, always. I wanted to focus constantly on the one task that really mattered. If my life was to be all work and no play, I wanted my work to be play. I wanted to quit Price Waterhouse. Not that I hated it; it just wasn’t me.

I wanted what everyone wants. To be me, full-time.

Even though Blue Ribbon was on track to double sales again, there was never enough cash, certainly not to pay Knight. Knight found another job he thought might fit better with his desire to focus as much as possible on Blue Ribbon. Assistant Professor of Accounting at Portland State University.

Knight, a CPA who had worked for two accounting firms, knew accounting pretty well at this point. But he was restless and twitchy, with several nervous tics – including wrapping rubber banks around his wrist and snapping them. One of his students was named Penelope Parks. Knight was captivated by her.

Knight decided to use the Socratic method to teach accounting. Miss Parks turned out to be the best student in the class. Soon thereafter, Miss Parks asked if Knight would be her advisor. Knight then asked her if she’d like a job for Blue Ribbon to help with bookkeeping. “Okay.”

On Miss Parks’ first day at Blue Ribbon, Woodell gave her a list of things – typing, bookkeeping, scheduling, stocking, filing invoices – and told her to pick two. Hours later, she’d done every thing on the list. Within a week, Woodell and Knight couldn’t remember how they’d gotten by without her, recalls Knight.

Furthermore, Miss Parks was “all-in” with respect to the mission of Blue Ribbon. She was good with people, too. She had a healing effect on Woodell, who was still struggling to adjust to his legs being paralyzed.

Knight often volunteered to go get lunch for the three of them. But his head was usually so full of business matters that he would invariably get the orders mixed up. “Can’t wait to see what I’m eating for lunch today,” Woodell might say quietly. Miss Parks would hide a smile.

Later on, Knight found out that Miss Parks and Woodell weren’t cashing any of their paychecks. They truly believed in Blue Ribbon. It was more than just a job for them.

Knight and Penny started dating. They were good at communicating nonverbally since they were both shy people. They were a good match and eventually decided to get married. Knight felt like she was a partner in life.

Knight made another trip to Onitsuka. Kitami was very friendly this time, inviting him to the company’s annual picnic.Knight met a man named Fujimoto at the picnic. It turned out to be another life-altering partnership.

…I was doing business with a country I’d come to love. Gone was the initial fear. I connected with the shyness of the Japanese people, with the simplicity of their culture and products and arts. I liked that they tried to add beauty to every part of life, from the tea ceremony to the commode. I liked that the radio announced each day which cherry trees, on which corner, were blossoming, and how much.

(Cherry trees in Japan, Photo by Nathapon Triratanachat)

 

1969

Knight was able to hire more ex-runners on commission. Sales in 1968 had been $150,000 and now they were on track for $300,000 for 1969.

Knight was finally able to pay himself a salary. But before leaving Portland State, he happened to see a starving artist in the hallway and asked if she’d do advertising art part-time. Her name was Carolyn Davidson, and she said OK.

Bowerman and Knight were losing trust in Kitami. Bowerman thought he didn’t know much about shoes and was full of himself. Knight hired Fujimoto to be a spy. Knight pondered again that when it came to business in Japan, you never knew what a competitor or a partner would do.

Knight was absentminded. He couldn’t go to the store and return with the one thing Penny asked for. He misplaced wallets and keys frequently. And he was constantly bumping into trees, poles, and fenders while driving.

Knight got in the habit of calling his father in the evening. His father would be in his recliner, while Buck would be in his. They’d hash things over.

Woodell and Knight began looking for a new office. They started enjoying hanging out together. Before parting, Knight would time Woodell on how fast he could fold up his wheelchair and get it and himself into his car.

Woodell was super positive and super energetic, a constant reminder of the importance of good spirits and a great attitude.

Buck and Penny would have Woodell over for dinner. Those were fun times. They would take turns describing what the company was and might be, and what it must never be. Woodell was always dressed carefully and always had on a pair of Tigers.

Knight asked Woodell to become operations manager. He’d demonstrated already that he was exceptionally good at managing day-to-day tasks. Woodell was delighted.

 

1970

Knight visited Onitsuka again. He discovered that Kitami was being promoted to operations manager. Onitsuka and Blue Ribbon signed another 3-year agreement. Knight looked into Kitami’s eyes and noticed something very cold. Knight never forgot that cold look.

Knight pondered the fact that the shipments from Onitsuka were always late, and sometimes had the wrong sizes or even the wrong models. Woodell and Knight discovered that Onitsuka always filled its orders from Japanese companies first, and then sent its foreign exports.

Meanwhile, Wallace at the bank kept making things difficult. Knight concluded that a small public offerings could create the extra cash Blue Ribbon needed. At the time, in 1970, a few venture capital firms had been launched. But they were in California and mostly invested in high-tech. So Knight formed Sports-Tek, Inc., as a holding company for Blue Ribbon. They tried a small public offering. It didn’t work.

Friends and family chipped in. Woodell’s parents were particularly generous.

On June 15, 1970, Knight was shocked to see a Man of Oregon on the cover ofSports Illustrated. His name was Steve Prefontaine. He’d already set a national record in high school at the two-mile (8:41). In 1970, he’d run three miles in 13:12.8, the fastest time on the planet.

Knight learned from aFortune magazine about Japan’s hyper-aggressive sosa shoga, “trading companies.” It was hard to see what these trading companies were exactly. Sometimes they were importers. Sometimes they were exporters. Sometimes they were banks. Sometimes they were an arm of the government.

After being harangued by Wallace at First National about cash balances again, Knight walked out and saw a sign for the Bank of Tokyo. He was escorted to a back room, where a man appeared after a couple of minutes. Knight showed the man his financials and said he needed credit. The man said that Japan’s sixth-largest trading company had an office at the top floor of this same building. Nissho Iwai was a $100-billion dollar company.

Knight met a man named Cam Murakami, who offered Knight a deal on the spot. Knight said he had to check with Onitsuka first. Knight wired Kitami, but heard nothing back at all for weeks.

Then Knight got a call from a guy on the east coast who told him that Onitsuka had approached him about becoming its new U.S. distributor. Knight checked with Fujimoto, his spy. Yes, it was true. Onitsuka was considering a clean break with Blue Ribbon.

Knight invited Kitami to visit Blue Ribbon.

 

1971

March 1971. Kitami was on his way. Blue Ribbon vowed to give him the time of his life.

Kitami arrived with a personal assistant, Hiraku Iwano, who was just a kid. At one point, Kitami told Knight that Blue Ribbon’s sales were disappointing. Knight said sales were doubling every year. “Should be triple some people say,” Kitami replied. “What people?”, asked Knight. “Never mind,” answered Kitami.

Kitami took a folder from his briefcase and repeated the charge. Knight tried to defend Blue Ribbon. Back and forth. Kitami had to use the restroom. When he left the meeting room, Knight looked into Kitami’s briefcase and tried to snag the folder that he thought Kitami had been referring to.

Kitami went back to his hotel. Knight still had the folder. He and Woodell opened it up. They found a list of eighteen U.S. athletic shoe distributors. These were the “some people” who told Kitami that Blue Ribbon wasn’t performing well enough.

I was outraged, of course. But mostly hurt. For seven years we’d devoted ourselves to Tiger shoes. We’d introduced them to America, we’d reinvented the line. Bowerman and Johnson had shown Onitsuka how to make a better shoe, and their designs were now foundational, setting sales records, changing the face of the industry – and this was how we were repaid?

At the end of Kitami’s visit, as planned, there was dinner with Bowerman, Mrs. Bowerman, and his friend (and lawyer), Jaqua. Mrs. Bowerman usually didn’t allow alcohol, but she was making an exception. Knight and Kitami both liked mai tais, which were being served.

Unfortunately, Bowerman had a few too many mai tais. It appeared things might get out of hand. Knight looked at Jaqua, remembering that he’d been a fighter pilot in World World II, and that his wingman, one of his closest friends, had been shot out of the sky by a Japanese Zero. Knight thought he sensed something starting to erupt in Jaqua.

Kitami, however, was having a great time. Then he found a guiter. He started playing it and singing a country Western. Suddenly, he sang “O Sole Mio.”

A Japanese businessman, strumming a Western guitar, singing an Italian ballad, in the voice of an Irish tenor. It was surreal, then a few miles past surreal, and it didn’t stop. I’d never know there were so many verses to “O Sole Mio.” I’d never known a roomful of active, restless Oregonians could sit still and quite for so long. When he set down the guitar, we all tried not to make eye contact with each other as we gave him a big hand. I clapped and clapped and it all made sense. For Kitami, this trip to the United States – the visit to the bank, the meetings with me, the dinner with the Bowermans – wasn’t about Blue Ribbon. Nor was it about Onitsuka. Like everything else, it was all about Kitami.

At a meeting soon thereafter, however, Kitami told Knight that Onitsuka wanted to buy Blue Ribbon. If Blue Ribbon did not accept, Onitsuka would have to work with other distributors. Knight knew he still needed Onitsuka, at least for awhile. So he thought of a stall. He told Kitami he’d have to talk with Bowerman. Kitami said OK and left.

Knight sent the budget and forecast to First National. White informed Knight at a meeting that First National would no longer be Blue Ribbon’s bank. White was sick about it, the bank officers were divided, but it had been Wallace’s call. Knight strove straight to U.S. Bank. Sorry. No.

Blue Ribbon was finishing 1971 with $1.3 million in sales, but it was in danger of failing. Fortunately, Bank of Cal gave Blue Ribbon a small line of credit.

Knight went back to Nissho and met Tom Sumeragi. Sumeragi told Knight that Nissho was willing to take a second position to their banks. Also, Nissho had sent a delegation to Onitsuka to try to work out a deal on financing. Onitsuka had tossed them out. Nissho was embarrased that a $25 million company had thrown out a $100 billion company. Sumeragi told Knight that Nissho could introduce him to other shoe manufacturers in Japan.

Knight knew he had to find a new shoe factory somewhere. He found one in Gaudalajara, Mexico. Knight placed an order for three thousand soccer shoes. It’s at this point that Knight asked his part-time artist, Carolyn Davidson, to try to design a logo. “Something that evokes a sense of motion.” She came back two weeks later and her sketches had a theme. But Knight was wondering what the theme was, “…fat lightning bolts? Chubby check marks? Morbidly obese squiggles?…”

Davidson returned later. Same theme, but better. Woodell, Johnson, and a few others liked it, saying it looked like a wing or a whoosh of air. Knight wasn’t thrilled about it, but went along because they were out of time and had to send it to the factory in Mexico.

(Nike logo, Timidonfire, Wikimedia Commons)

They also needed a name. Falcon. Dimension Six. These were possibilities they’d come up with. Knight liked Dimension Six mostly because he’d come up with it. Everyone told him it was awful. It didn’t mean anything. Bengal. Condor. They debated possibilities.

It was time to decide. Knight still didn’t know. Then Woodell told him that Johnson had had a dream and then woke up with the name clearly in mind: “Nike.”

Knight reminisced… “The Greek goddess of victory. The Acropolis. The Parthenon. The Temple…”

Knight had to decide. He hated having to decide under time pressure. He’s not sure if it was luck or spirit or something else, but he chose “Nike.” Woodell said, “Hm.” Knight replied, “Yeah, I know. Maybe it’ll grow on us.”

(Nike logo, Wikimedia Commons)

Meanwhile, Nissho was infusing Blue Ribbon with cash. But Knight wanted a more permanent solution. He conceived of a public offering of convertible debentures. People bought them, including Knight’s friend Cale.

The factory in Mexico didn’t produce good shoes. Knight talked with Sumeragi, who knew a great deal about shoe factories around the world. Sumeragi also offered to introduce Knight to Jonas Senter, “a shoe dog.”

Shoe dogs were people who devoted themselves wholly to the making, selling, buying, or designing of shoes. Lifers used the phrase cheerfully to describe other lifers, men and women who had toiled so long and hard in the shoe trade, they thought and talked about nothing else. It was an all-consuming mania… But I understood. The average person takes seventy-five hundred steps a day, 274 million steps over the course of a long life, the equivalent of six times around the globe – shoe dogs, it seemed to me, simply wanted to be part of that journey. Shoes were their way of connecting with humanity…

Senter was the “knockoff king.” He’d been behind a recent flood of knockoff Adidas. Senter’s protege was a guy named Sole.

Knight wasn’t sure partnering with Nissho was the best move. Jaqua suggested Knight meet with his brother-in-law, Chuck Robinson, CEO of Marcona Mining, which had many joint ventures. Each of the big eight Japanese trading firms was a partner in at least one of Marcona’s mines, records Knight. Chuck to Buck: “If the Japanese trading company understands the rules from the first day, they will be the best partners you’ll ever have.”

Knight went to Sumeragi and said: “No equity in my company. Ever.” Sumeragi consulted a few folks, came back and said: “No problem. But here’s our deal. We take four percent off the top, as a markup on product. And market interest rates on top of that.” Done.

Knight met Sole, who mentioned five factories in Japan.

A bit later, Bowerman was eating breakfast when he noticed the waffle iron’s gridded pattern. This gave him an idea and he started experimenting.

…he took a sheet of stainless steel and punched it with holes, creating a waffle-like surface, and brought this back to the rubber company. The mold they made from that steel sheet was pliable, workable, and Bowerman now had two foot-sized squares of hard rubber nubs, which he brought home and sewed to the sole of a pair of running shoes. He gave these to one of his runners. The runner laced them on and ran like a rabbit.

Bowerman phoned me, excited, and told me about his experiment. He wanted me to send a sample of his waffle-soled shoes to one of my new factories. Of course, I said. I’d send it right away – to Nippon Rubber.

I look back over decades and see him toiling in his workshop, Mrs. Bowerman carefully helping, and I get goosebumps. He was Edison in Menlo Park, Da Vinci in Florence, Tesla in Wardenclyffe. Divinely inspired. I wonder if he knew, if he had any clue, that he was the Daedalus of sneakers, that he was making history, remaking an industry, transforming the way athletes would run and stop and jump for generations. I wonder if he could conceive in that moment all he’d done. All that would follow.

 

1972

The National Sporting Goods Association Show in Chicago in 1972 was extremely important for Blue Ribbon because they were going to introduce the world to Nike shoes. If sales reps liked Nike shoes, Blue Ribbon had a chance to flourish. If not, Blue Ribbon wouldn’t be back in 1973.

Right before the show, Onitsuka announced its “acquisition” of Blue Ribbon. Knight had to reassure Sumeragi that there was no acquisition. At the same time, Knight couldn’t break from Onitsuka just yet.

As Woodell and Johnson prepared the booth – with stacks of Tigers and also with stacks of Nikes – they realized the Nikes from Nippon Rubber weren’t as high-quality as the Tigers. The swooshes were crooked, too.

Darn it, this was no time to be introducing flawed shoes. Worse, we had to push these flawed shoes on to people who weren’t our kind of people. They were salesmen. They talked like salesmen, walked like salesmen, and they dressed like salesmen – tight polyester shirts, Sansabelt slacks. They were extroverts, we were introverts. They didn’t get us, we didn’t get them, and yet our future depended on them. And now we’d have to persuade them, somehow, that this Nike thing was worth their time and trust – and money.

I was on the verge of losing it, right on the verge. Then I saw Johnson and Woodell were already losing it, and I realized that I couldn’t afford to… ‘Look fellas, this is the worst the shoes will ever be. They’ll get better. So if we can just sell these… we’ll be on our way.’

The salesmen were skeptical and full of questions about the Nikes. But by the end of the day, Blue Ribbon had exceeded its highest expectations. Nikes had been the smash hit of the show.

Johnson was so perplexed that he demanded an answer from the representative of one his biggest accounts. The rep explained:

‘We’ve been doing business with you Blue Ribbon guys for years and we know that you guys tell the truth. Everyone else bullshits, you guys always shoot straight. So if you say this new shoe, this Nike, is worth a shot, we believe.’

Johnson came back to the booth and said, “Telling the truth. Who knew?” Woodell laughed. Johnson laughed. Knight laughed.

Two weeks later, Kitami showed up without warning in Knight’s office, asking about “this… NEE-kay.” Knight had been rehearsing for this situation. He replied simply that it was a side project just in case Onitsuka drops Blue Ribbon. Kitami seemed placated.

Kitami asked if the Nikes were in stores. No, said Knight. Kitami asked when Blue Ribbon was going to sell to Onitsuka. Knight answered that he still needed to talk with Bowerman. Kitami then said he had business in California, but would be back.

Knight called Bork in Los Angeles and told him to hide the Nikes. Bork hid them in the back of the store. But Kitami, when visiting the store, told Bork he had to use the bathroom. While in the back, Kitami found stacks of Nikes.

Bork called Knight and told him, “Jig’s up… It’s over.” Bork ended up quitting. Knight discovered later that Bork had a new job… working for Kitami.

Kitami demanded a meeting. Bowerman, Jaqua, and Knight were in attendance. Jaqua told Knight to say nothing no matter what. Jaqua told Kitami that he hoped something could still be worked out, since a lawsuit would be damaging to both companies.

Knight called a company-wide meeting to explain that Onitsuka had cut them off. Many people felt resigned, says Knight, in part because there was a recession in the United States. Gas lines, political gridlock, rising unemployment, Vietnam. Knight saw the discouragement in the faces of Blue Ribbon employees, so he told them:

‘…This is the moment we’ve been waiting for. One moment. No more selling someone else’s brand. No more working for someone else. Onitsuka has been holding us down for years. Their late deliveries, their mixed-up orders, their refusal to hear and implement our design ideas – who among us isn’t sick of dealing with all that? It’s time we faced facts: If we’re going to succeed, or fail, we should do so on our own terms, with our own ideas – our own brand. We posted two million in sales last year… none of which had anything to do with Onitsuka. That number was a testament to our ingenuity and hard work. Let’s not look at this as a crisis. Let’s look at this as our liberation. Our Independence Day.’

Johnson told Knight, “Your finest hour.” Knight replied he was just telling the truth.

The Olympic track-and-field trials in 1972 were going to be in Eugene. Blue Ribbon gave Nikes to anyone who would take them. And they handed out Nike T-shirts left and right.

The main event was on the final day, a race between Steve Prefontaine – known as “Pre” – and the great Olympian George Young. Pre was the biggest thing to hit American track and field since Jesse Owens. Knight tried to figure out why. It was hard to say, exactly. Knight:

Sometimes I thought the secret to Pre’s appeal was his passion. He didn’t care if he died crossing the finish line, so long as he crossed first. No matter what Bowerman told him, no matter what his body told him, Pre refused to slow down, ease off. He pushed himself to the brink and beyond. This was often a counterproductive strategy, and sometimes it was plainly stupid, and occasionally it was suicidal. But it was always uplifting for the crowd. No matter the sport – no matter the human endeavor, really – total effort will win people’s hearts.

(Steve Prefontaine)

Gerry Lindgren was also in this race with Pre and Young. Lindgren may have been the best distance runner in the world at that time. Lindgren had beaten Pre when Lindgren was a senior and Pre a freshman.

Pre took the lead right away. Young tucked in behind him. In no time they pulled way ahead of the field and it became a two-man affair… Each man’s strategy was clear. Young meant to stay with Pre until the final lap, then use his superior sprint to go by and win. Pre, meanwhile, intended to set such a fast pace at the outset that by the time they got to that final lap, Young’s legs would be gone.

For eleven laps they ran a half stride apart. With the crowd now roaring, frothing, shrieking, the two men entered the final lap. It felt like a boxing match. It felt like a joust… Pre reached down, found another level – we saw him do it. He opened up a yard lead, then two, then five. We saw Young grimacing and we knew that he would not, could not, catch Pre. I told myself, Don’t forget this. Do not forget. I told myself there was much to be learned from such a display of passion, whether you were running a mile or a company.

Both men had broken the American record. Pre had broken it by a little bit more.

…What followed was one of the greatest ovations I’ve ever heard, and I’ve spent my life in stadiums.

I’d never witnessed anything quite like that race. And yet I didn’t just witness it. I took part in it. Days later I felt sore in the hams and quads. This, I decided, this is what sports are, what they can do. Like books, sports give people a sense of having lived other lives, of taking part in other people’s victories. And defeats. When sports are at their best, the spirit of the fan merges with the spirit of the athlete, and in that convergence, in that transference, is the oneness that mystics talk about.

 

1973

Bowerman had retired from coaching, partly because of the sadness of the terrorist attacks at the 1972 Olympics in Munich. Bowerman had been able to help hide one Israeli athlete. Bowerman had immediately called the U.S. consul and shouted, “Send the marines!” Eleven Israeli athletes had been captured and later killed. An unspeakable tragedy. Knight thought of the deaths of the two Kennedys, and Dr. King, and the students at Kent State.

Ours was a difficult, death-drenched age, and at least once every day you were forced to ask yourself: What’s the point?

Although Bowerman had retired from coaching, he was still coaching Pre. Pre had finished a disappointing fourth at the Olympics. He could have gotten silver if he’d allowed another runner to be the front runner and if he’d coasted in his wake. But, of course, Pre couldn’t do that.

It took Pre six months to re-emerge. He won the NCAA three-mile for a fourth straight year, with a time of 13:05.3. He also won in the 5,000 by a good margin with a time of 13:22.4, a new American record. And Bowerman had finally convinced Pre to wear Nikes.

At that time, Olympic athletes couldn’t receive endorsement money. So Pre sometimes tended bar and occasionally ran in Europe in exchange for illicit cash from promoters.

Knight decided to hire Pre, partly to keep him from injuring himself by racing too much. Pre’s title was National Director of Public Affairs. People often asked Knight what that meant. Knight would say, “It means he can run fast.” Pre wore Nikes everywhere and he preached Nike as gospel, says Knight.

Around this time, Knight realized that Johnson was becoming an excellent designer. The East Coast was running smoothly, but needed reorganization. So Knight asked Johnson to switch places with Woodell. Woodell excelled at operations and thus would be a great fit for the East Coast situation.

Although Johnson and Woodell irritated one another, they both denied it. When Knight asked them to switch places, the two exchanged house keys without the slightest complaint.

In the spring of 1973, Knight held his second meeting with the debenture holders. He had to tell them that despite $3.2 million in sales, the company had lost $57,000. The reaction was negative. Knight tried to explain that sales continue to explode higher. But the investors were not happy.

Knight left the meeting thinking he would never, ever take the company public. He didn’t want to deal with that much negativity and rejection ever again.

Onitsuka filed suit against Blue Ribbon in Japan. So Blue Ribbon had to file against them in the United States.

Knight asked his Cousin Houser to be in charge of the case. Houser was a fine lawyer who carried himself with confidence.

Better yet, he was a tenacious competitor. When we were kids Cousin Houser and I used to play vicious, marathon games of badminton in his backyard. One summer we played exactly 116 games. Why 116? Because Cousin Houser beat me 115 straight times. I refused to quit until I’d won. And he had no trouble understanding my position.

More importantly, Cousin Houser was able to talk his firm into taking the Blue Ribbon case on contingency.

Knight continued his evening conversations with this father, who believed strongly in Blue Ribbon’s cause. Knight’s father, who had been trained as a lawyer, spent time studying law books. He reassured Buck, “we” are going to win. This support from his father boosted Buck’s spirits at a challenging time.

(Law library, Photo by Spiroview Inc.)

Cousin Houser told Knight one day that he was bringing on a new member of the team, a young lawyer from UC Berkeley School of Law, Rob Strasser. Not only was Strasser brilliant. He also believed in the rightness of Blue Ribbon’s case, viewing it as a “holy crusade.”

Strasser was a fellow Oregonian who felt looked down on by folks north and south. Moreover, he felt like an outcast. Knight could relate. Strasser often downplayed his intelligence for fear of alienating people. Knight could relate to that, too.

Intelligence like Strasser’s, however, couldn’t be hidden for long. He was one of the greatest thinkers I ever met. Debator, negotiator, talker, seeker – his mind was always whirring, trying to understand.

When he wasn’t preparing for the trial, Knight was exclusively focused on sales. It was essential that they sell out every pair of shoes in each order. The company was still growing fast and cash was always short.

Whenever there was a delay, Woodell always knew what the problem was and could quickly let Knight know. Knight on Woodell:

He had a superb talent for underplaying the bad, and underplaying the good, for simply being in the moment… throughout the day a steady rain of pigeon poop would fall on Woodell’s hair, shoulders, desktop. But Woodell would simply dust himself off, casually clear his desk with the side of his hand, and continue with his work.

…I tried often to emulate Woodell’s Zen monk demeanor. Most days, however, it was beyond me…

Blue Ribbon couldn’t meet demand. This frustrated Knight. Supplies were arriving on time. But in 1973, it seemed that the whole world, all at once, wanted running shoes. And there were never enough. This made things precarious, to say the least, for Blue Ribbon:

…We were leveraged to the hilt, and like most people who live from paycheck to paycheck, we were walking the edge of a precipice. When a shipment of shoes was late, our pair count plummeted. When our pair count plummeted, we weren’t able to generate enough revenue to repay Nissho and the Bank of California on time. When we couldn’t repay Nissho and the Bank of California on time, we couldn’t borrow more. When we couldn’t borrow more we were late placing our next order.

Sales for 1973 hit $4.8 million, up 50 percent from the previous year. But Blue Ribbon was still on fragile ground, it seemed. Knight then thought of asking their retailers to sign up for large and unrefundable orders, six months in advance, in exchange for hefty discounts, up to 7 percent. Such long-term commitments from well-established retailers like Nordstrom, Kinney, Athlete’s Foot, United Sporting Goods, and others, could then be used to get more credit from Nissho and the Bank of California.

Much later, after much protesting, the retailers signed on to the long-term commitments.

 

1974

The trial. Federal courthouse in downtown Portland. Wayne Hilliard was the lead lawyer for the opposition. He was fiery and eloquent. Cousin Houser was the lead for Blue Ribbon. He’d convinced his firm to take the Blue Ribbon case on contingency. But instead of a few months, it was now two years later. Houser hadn’t seen a dime and costs were huge. Moreover, Houser told Knight that his fellow law partners sometimes put a great deal of pressure on Houser to drop the Blue Ribbon case.

(Federal courthouse in Portland, Oregon, Wikimedia Commons)

Houser stuck with the case. He wasn’t fiery. But he was prepared and dedicated. Knight was initially disappointed, but later came to admire him. “Fire or no, Cousin Houser was a true hero.”

After being questioned by both sides, Knight felt he hadn’t done well at all, a D minus. Houser and Strasser didn’t disagree.

The judge in the case was the Honorable James Burns. He called himself James the Just. Johnson made the mistake of discussing the trial with a store manager after James the Just had expressly forbidden all discussion of the case outside the courtroom. James the Just was upset. Knight:

Johnson redeemed himself with his testimony. Articulate, dazzlingly anal about the tiniest details, he described the Boston and the Cortez better than anyone else in the world could, including me. Hilliard tried and tried to break him, and couldn’t.

Later on, the testimony of Iwano, the young assistant who’d been with Kitami, was heard. Iwano testified that Kitami had a fixed plan already in place to break the contract with Blue Ribbon. Kitami had openly discussed this plan on many occasions, said Iwano.

Bowerman’s testimony was so-so because, out of disdain, he hadn’t prepared. Woodell, for his part, was nervous.

Mr. Onitsuka said he hadn’t known anything about the conflict between Kitami and Knight. Kitami, in his testimony, lied again and again. He said that he had no plan to break the contract with Blue Ribbon. He also claimed that meeting with other distributors had just been market research. As well, the idea of acquiring Blue Ribbon “was initiated by Phil Knight.”

James the Just was convinced that Blue Ribbon had been more truthful. In particular, Iwano seemed truthful, while Kitami didn’t. On the issue of trademarks, Blue Ribbon would retain all rights to the Boston and the Cortez.

A bit later, Hilliard offered $400,000. Finally, Blue Ribbon accepted. Knight was happy for Cousin Houser, who would get half. It was the largest payment in the history of his firm.

Knight, with help from Hayes, convinced Strasser to come work for Blue Ribbon. Strasser later accepted.

Japanese labor costs were rising. The yen was fluctuating. Knight decided Blue Ribbon needed to find factories outside of Japan. He looked at Taiwan, but shoe factories there weren’t quite ready. He looked next at Puerto Rico.

Then Knight went to the east coast to look for possible factories. The first factory owner laughed in Knight’s face.

The next empty factory Knight visited – with Johnson – the owner was willing to lease the third floor to Blue Ribbon. He suggested a local guy to manage the factory, Bill Giampietro. Giampietro turned out to be “a true shoe dog,” said Knight. All he’d ever done is make shoes, like his father. Perfect. Could he get the old Exeter factory up and running? How much would it cost? No problem. About $250,000. Deal.

Knight asked Johnson to run the new factory. Johnson said, “…what do I know about running a factory? I’d be in completely over my head.”

Knight couldn’t stop laughing: “Over your head? Over your head! We’re all in over our heads! Way over!”

Knight writes that, at Blue Ribbon, it wasn’t that they thought they couldn’t fail. On the contrary, they thought they would fail. But they believed they would fail fast, learn from it, and be better for it.

Finishing up 1974, the company was on track for $8 million in sales. Their contact at Bank of California, Perry Holland, kept telling them to slow down. So they sped up, as usual.

 

1975

Knight kept telling Hayes, “Pay Nissho first.” Blue Ribbon had a line of credit at the bank for $1 million. They had a second million from Nissho. That was absolutely essential.

…Grow or die, that’s what I believed, no matter the situation. Why cut your order from $3 million down to $2 million if you believed in your bones that demand out there was for $5 million? So I was forever pushing my conservative bankers to the brink, forcing them into a game of chicken. I’d order a number of shoes that seemed to them absurd, a number we’d need to stretch to pay for, and I’d always just barely pay for them, in the nick of time, and then just barely pay our other monthly bills, at the last minute, always doing just enough, and no more, to prevent the bankers from booting us. And then, at the end of the month, I’d empty our accounts to pay Nissho and start from zero again.

Demand was always greater than sales, so Knight concluded his approach was reasonable. There was a new manager at Nissho’s Portland office, Tadayuki Ito, in place of Sumeragi. (Sumeragi still helped with the Blue Ribbon account, though.)

One day in the spring of 1975, Blue Ribbon was $75,000 short of the $1 million they owed Nissho. Blue Ribbon would have to completely drain every other account to make up for the shortfall. Retail stores. Johnson’s Exeter factory. All of them.

(Illustration byLkeskinen0)

In Exeter, a mob of angry workers was at Johnson’s door. Giampetro drove with Johnson to see an old friend who owned a box company that depended on Blue Ribbon. Giampetro asked for a loan of $5,000 (more than $25,000 in 2018), which was outrageous. The man counted out fifty crisp hundred-dollar bills, says Knight.

Then Holland called Knight and Hayes to a meeting at the Bank of California. The bank would no longer do business with Blue Ribbon.

Knight was worried how Ito and Sumeragi, representing Nissho, would react. Ito and Sumeragi, after hearing what happened, said they would need to look at Blue Ribbon’s books.

On the weekend, Knight called a company-wide meeting to discuss the situation. The Exeter factory had been a secret kept from Nissho. But everyone agreed to give Nissho all information.

During this meeting, two creditors – owed $500,000 and $100,000 – called and were livid. They were on their way to Oregon to collect and cash out.

On Monday, Ito and Sumeragi arrived at Blue Ribbon’s office. Without a word, they went through the lobby to the conference room, sat down with the books and got to work. Then Ito came to information related to the Exeter factory. He did a slow double-take and then looked up at Knight. Knight nodded. Ito smiled. Knight:

I gave him a weak half smile in return, and in that brief wordless exchange countless fates and futures were decided.

It turned out that Sumeragi had been trying to help Blue Ribbon by hiding Nissho’s invoices in a drawer. Blue Ribbon had been stressing out trying to pay Nissho on time, but they’dnever paid them on time because Sumeragi thought he was helping, writes Knight.

Ito accused Sumeragi of working for Blue Ribbon. Sumeragi swore on his life that he’d acted independently. Ito asked why. Sumeragi answered that he thought Blue Ribbon would be a great success, perhaps a $20 million account. Ito eventually forgave Blue Ribbon. “There are worse things than ambition,” he said.

Ito accompanied Knight and Hayes to a meeting with the Bank of California. Only this time, Ito – whom Knight saw as a “mythic samurai, wielding a jeweled sword” – was on their side.

(Samurai, Photo by Esolex)

According to Knight, Ito opened the meeting and “went all in.” After confirming that Bank of California no longer wanted to handle Blue Ribbon’s account, Ito said Nissho wanted to pay off Blue Ribbon’s outstanding debt. He asked for the number and it was the same number he’d learned earlier. Ito already had a check made out for the amount and slid an envelope with the check across the table. Ito insisted the check be deposited immediately.

After the meeting, Knight and Hayes bowed to Ito. Ito remarked:

‘Such stupidity… I do not like such stupidity. People pay too much attention to numbers.’

***

Blue Ribbon still needed a bank. They started calling. “The first six hung up on us,” recalls Knight. First State Bank of Oregon didn’t hang up. They offered one million in credit.

Pre died in a tragic car accident at the age of twenty-four. At the time of his death, he held every American record from 2,000 to 10,000 meters, from two miles to six miles. People created a shrine where Pre had died. They left flowers, letters, notes, gifts. Knight, Johnson, and Woodell decided that Blue Ribbon would curate Pre’s rock, making it a holy site forever.

 

1976

Knight had several meetings early in 1976 with Woodell, Hayes, and Strasser about the company’s cash situation. Nissho was lending Blue Ribbon millions, but to keep up with demand, they needed millions more. The most logical solution was to go public. But Knight and the others felt that it just wasn’t who they were. No way.

They found other ways to raise money, including a million-dollar loan guaranteed by the U.S. Small Business Administration.

Meanwhile, Bowerman’s waffle trainer was getting even more popular.

(Nike 1976 waffle trainer)

With its unique outer sole, and its pillowy midsole cushion, and its below-market price ($24.95), the waffle trainer was continuing to capture the popular imagination like no previous shoe. It didn’t just feel different, or fit different – it looked different. Radically so. Bright red upper, fat white swoosh – it was a revolution in aesthetics. Its look was drawing hundreds of thousands of new customers into the Nike fold, and its performance was sealing their loyalty. It had better traction and cushioning than anything on the market.

Watching that shoe evolve in 1976 from popular accessory to cultural artifact, I had a thought. People might start wearing this thing to class.

And the office.

And the grocery store.

And throughout their everyday lives.

It was a rather grandiose idea… So I ordered the factories to start making the waffle trainer in blue, which would go better with jeans, and that’s when it really took off.

We couldn’t make enough. Retailers and sales reps were on their knees, pleading for all the waffle trainers we could ship. The soaring pair counts were transforming our company, not to mention the industry. We were seeing numbers that redefined our long-term goals, because they gave us something we’d always lacked – an identify. More than a brand, Nike was now becoming a household word, to such an extent that we would have to change the company name. Blue Ribbon, we decided, had run its course. We would have to incorporate as Nike, Inc.

They needed to ramp up production. Knight realized the time had come to visit Taiwan. To help with the Taiwan effort, Knight turned to Jim Gorman. Gorman had been raised in a series of foster homes. Nike was the family he’d never had.

…In every instance, Gorman had done a fine job and never uttered a sour word. He seemed the perfect candidate to take on the latest mission impossible – Taiwan. But first I needed to give him a crash course on Asia. So I scheduled a trip, just the two of us.

Gorman was full of questions for Knight and took notes on everything. Knight enjoyed teaching Gorman, partly because Knight himself could learn what he knew even better through the process of teaching.

Taiwan had a hundred smaller factories, whereas South Korea had a few larger ones. That’s why Nike needed to go to Taiwan at this juncture. Demand for Nikes was exploding, but their volume was still too low for a giant shoe factory. However, Knight knew it would be a challenge to get a shoe factory in Taiwan to improve its quality enough to be able to produce Nikes.

During the visit to various Taiwan shoe factories, Jerry Hsieh introduced himself to Knight and Gorman. Hsieh was a genuine shoe dog, but quite young, twenty-something. When Knight and Gorman found their way to Hsieh’s office – a room stuffed with shoes everywhere – Hsieh started sharing his deep knowledge of shoes. Also, Hsieh told them he knew the very best shoe factories in Taiwan and for a small fee, would be happy to introduce them. They agreed on a commission per pair.

The 1976 Olympic trials, again in Eugene. In the 10,000 meter race, all top three finishers wore Nikes. Some top finishers in other qualifying races also wore Nikes. Meanwhile, Penny created a great number of Nike T-shirts. People would see other people wearing the Nike T-shirts and want to buy one. The Nike employees heard people whispering.”Nike.” “Nike.” “Nike.”

At the close of 1976, Nike had doubled its sales to $14 million. The company still had no cash, though. Its bank accounts were often at zero.

The company’s biannual retreat was taking place. People called it Buttface.

Johnson coined the phrase, we think. At one of our earliest retreats he muttered: “How many multi-million dollar companies can you yell out, ‘Hey, Buttface,’ and the entire management team turns around?” It got a laugh. And then it stuck. And then it became a key part of our vernacular. Buttface referred to both the retreat and the retreaters, and it not only captured the informal mood of those retreats, where no idea was too sacred to be mocked, and no person was too important to be ridiculed, it also summed up the company spirit, mission and ethos.

Knight continues:

…The problems confronting us were grave, complex, insurmountable… And yet we were always laughing. Sometimes, after a really cathartic guffaw, I’d look around the table and feel overcome by emotion. Camaraderie, loyalty, gratitude. Even love. Surely love. But I also remember feeling shocked that these were the men I’d assembled. These were the founding fathers of a multi-million dollar company that sold athletic shoes? A paralyzed guy, two morbidly obese guys, a chain-smoking guy? It was bracing to realize that, in this group, the one with whom I had the most in common was… Johnson. And yet, it was undeniable. While everyone else was laughing, rioting, he’d be the sane one, sitting quietly in the middle of the table reading a book.

A bit later, Knight writes:

Undoubtedly we looked, to any casual observer, like a sorry, motley crew, hopelessly mismatched. But in fact we were more alike than different, and that gave a coherence to our goals and our efforts. We were mostly Oregon guys, which was important. We had an inborn need to prove ourselves, to show the world that we weren’t hicks and hayseeds. And we were nearly all merciless self-loathers, which kept the egos in check. There was none of that smartest-guy-in-the-room foolishness. Hayes, Strasser, Woodell, Johnson, each would have been the smartest guy in any room, but none believed of himself, or the next guy. Our meetings were defined by contempt, disdain, and heaps of abuse.

(Photo by Chris Dorney)

Knight records:

…Each of us had been misunderstood, misjudged, dismissed. Shunned by bosses, spurned by luck, rejected by society, short-changed by fate when looks and other natural graces were handed out. We’d each been forged by early failure. We’d each given ourselves to some quest, some attempt at validation or meaning, and fallen short.

…

I identified with the born loser in each Buttface, and vice versa, and I knew that together we could become winners…

Knight’s management style continued to be very hands-off, following Patton’s leadership belief:

Don’t tell people how to do things, tell them what to do and let them surprise you with their results.

Nike’s culture seemed to be working thus far. Since Bork, no one had gotten really upset, not even what they were paid, which is unusual, notes Knight. Knight created a culture he himself would have wanted: let people be, let people do, let people make their own mistakes.

 

1977

M. Frank Rudy, a former aerospace engineer, and his business partner, Bob Bogert, presented to Nike the idea of putting air in the soles of shoes. Great cushioning, great support, a wonderful ride. Knight tried wearing a pair Rudy showed him on a six-mile run. Unstable, but one great ride.

Strasser, who by this point had become Nike’s negotiator, offered Rudy 10 cents for every pair we sold. Rudy asked for twenty. They settled somewhere in the middle. Knight sent Rudy and his partner back to Exeter, which “was becoming our de facto Research and Development Department.”

Knight calls this time “an odd moment,” saying furthermore that “a second strange shoe dog showed up on our door step. His name was Sonny Vaccaro…”. Vaccaro had founded the Dapper Dan Classic, a high school all-star game that had become very popular. Though it, Vaccaro had gotten to know many coaches. Knight hired Vaccaro and sent him, with Strasser, to sign up college basketball coaches. Knight expected them to fail. But they succeeded.

Knight knew he had to meet again with Chuck Robinson, who’d served with distinction as a lieutenant commander on a battle ship in World War II. Chuck knew business better than anyone Knight had ever met. Recently, he’d been the number two guy under Henry Kissinger, so he wasn’t available for meetings. Now Chuck was free.

Chuck took a look at Nike’s financials and couldn’t stop laughing, saying, “Compositionally, you are a Japanese trading company – 90 percent debt!”

Chuck told Buck, “You can’t live like this.” The solution was to go public in order to raise a large amount of cash. Knight invited Chuck to join the board. Chuck agreed, to Knight’s surprise.

When Knight put the question of going public to a company vote, however, the consensus was still to remain private.

Then they received a letter from the U.S. Customs Service containing a bill for $25 million. Nike’s competitors, Converse and Keds – plus a few small factories – were behind it. They had been lobbying in Washington, DC, trying to slow Nike by enforcing the American Selling Price, an old law dating back to protectionist days.

(Photo by Ian Wilson)

ASP – American Selling Price – said that import duties on nylon shoes should be 20 percent of the shoe’s manufacturing cost. Unless there was a “similar shoe” made by a U.S. competitor. Then it should be 20 percent of that shoe’s selling price. Nike’s competitors just needed to make some shoes deemed “similar,” price them very high, and voila – high import duties for Nike.

They’d managed to pull the trick off, raising Nike’s import duties retroactively by 40 percent.

Near the end of 1977, Nike’s sales were approaching $70 million.

 

1978

Knight calls Strasser the “five-star general” in the battle with the U.S. government. But they knew they needed “a few good men.” Strasser suggested a friend of his, a young Portland lawyer, Richard Werschkul. Stanford undergrad, University of Oregon law. A sharp guy with a presence. And an eccentric streak. Some worried he was too serious and obsessive. But that seemed good to Knight. And Knight trusted Strasser. Werschkul was dispatched to Washington, DC.

Meanwhile, sales were on track for $140 million. Furthermore, Nike shoes were finally recognized as higher quality than Adidas shoes. Knight thought Nike had led in quality and innovation for years.

Nike had to start selling clothes, announced Knight at Buttface in 1978. First, Adidas sold more apparel than shoes. Second, it would be easier to get athletes into endorsement deals.

Knight decided to hire a young accountant, Bob Nelson, and put him in charge of the new line of Nike apparel. But Nelson had no sense of style, unfortunately. When he presented his ideas, they didn’t look good. Knight decided to transfer him to an accounting position, where he would excel. Knight writes:

…Then I quietly shifted Woodell to apparel. He did his typically flawless job, assembling a line that gained immediate attention and respect in the industry. I asked myself why I didn’t just let Woodell do everything.

Tailwind – a new Nike shoe with air – came out in late 1978. Then Nike had to recall it due to a design flaw. Knight concluded they’d learned a valuable lesson. “Don’t put twelve innovations into one shoe.”

Around this time, many seemed to be suffering from burnout, including Knight. And back in DC, Werschkul was becoming hyper obsessive. He’d tried to talk with everyone possible. They all told him to put something in writing so they could study it.

Werschkul spent months writing. It became hundreds of pages. “Without a shred of irony Werschkul called it: Werschkul on American Selling Price, Volume I.” Knight:

When you thought about it, when you really thought about it, what really scared you was that Volume I.

Knight sent Strasser to calm Werschkul down. Knight realized that he himself would have to go to DC. “Maybe the cure for any burnout… is just to work harder.”

 

1979

Senators Mark O. Hatfield and Bob Packwood helped Nike deal with the $25 million bill from U.S. Customs. Knight started the process of looking for a factory in China.

 

1980

Chuck Robinson suggested to Knight that Nike could go public but have two classes of stock, class A and class B. Nike insiders would own class A shares, which would allow them to name three-quarters of the board of directors. The Washington Post Company and a few other companies had done this.

Knight explained the idea – going public with two classes of stock – to colleagues at Nike. All agreed that it was time to go public to raise badly needed cash.

In China, Knight – with Strasser, Hayes, and others – signed a deal with China’s Ministry of Sports. Four years later, at the Olympics in Los Angeles, the Chinese track-and-field team entered the stadium wearing Nike shoes and warm-ups. Before leaving China, Nike signed a deal with two Chinese factories.

Knight then muses about “business”:

It seems wrong to call it “business.” It seems wrong to throw all those hectic days and sleepless nights, all those magnificent triumphs and desperate struggles, under that bland, generic banner: business. What we were doing felt like so much more. Each new day brought fifty new problems, fifty tough decisions that needed to be made, right now, and we were always acutely aware that one rash move, one wrong decision could be the end. The margin for error was forever getting narrower, while the stakes were forever creeping higher – and none of us wavered in the belief that “stakes” didn’t mean “money.” For some, I realize, business is the all-out pursuit of profits, period, full stop, but for us business was no more about making money than being human is about making blood. Yes, the human body needs blood. It needs to manufacture red and white cells and platelets and redistribute them evenly, smoothly, to all the right places, on time, or else. But that day-to-day mission of the human body isn’t our mission as human beings. It’s a basic process that enables our higher aims, and life always strives to transcend the basic processes of living – and at some point in the late 1970s, I did, too. I redefined winning, expanded it beyond my original definition of not losing, of merely staying alive. That was no longer enough to sustain me, or my company. We wanted, as all great businesses do, to create, to contribute, and we dared to say so aloud. When you make something, when you improve something, when you add to some new thing or service to the lives of strangers, making them happier, or healthier, or safer, or better, and when you do it all crisply and efficiently, smartly, the way everything should be done but so seldom is – you’re participating more fully in the whole grand human drama. More than simply alive, you’re helping others to live more fully, and if that’s business, all right, call me a businessman.

 

BOOLE MICROCAP FUND

An equal weighted group of micro caps generally far outperforms an equal weighted (or cap-weighted) group of larger stocks over time. See the historical chart here: https://boolefund.com/best-performers-microcap-stocks/

This outperformance increases significantly by focusing on cheap micro caps. Performance can be further boosted by isolating cheap microcap companies that show improving fundamentals. We rank microcap stocks based on these and similar criteria.

There are roughly 10-20 positions in the portfolio. The size of each position is determined by its rank. Typically the largest position is 15-20% (at cost), while the average position is 8-10% (at cost). Positions are held for 3 to 5 years unless a stock approachesintrinsic value sooner or an error has been discovered.

The mission of the Boole Fund is to outperform the S&P 500 Index by at least 5% per year (net of fees) over 5-year periods. We also aim to outpace the Russell Microcap Index by at least 2% per year (net). The Boole Fund has low fees.

 

If you are interested in finding out more, please e-mail me or leave a comment.

My e-mail: [email protected]

 

Disclosures: Past performance is not a guarantee or a reliable indicator of future results. All investments contain risk and may lose value. This material is distributed for informational purposes only. Forecasts, estimates, and certain information contained herein should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed.No part of this article may be reproduced in any form, or referred to in any other publication, without express written permission of Boole Capital, LLC.