Fooled By Randomness: The Hidden Role of Chance in Life and in the Markets

Taleb begins this chapter by introducing another character, a 40-year-old named Marc who is a Harvard- and Yale-educated lawyer, husband, and father of three. He lives in an expensive neighborhood in Manhattan and makes half a million dollars a year. He is a “workaholic” and his family life has suffered because of his demanding work life, as has his health. Marc’s first wife left him after feeling undervalued in comparison to his work. His current wife, Janet, feels out of place in the Park Avenue neighborhood in which they live. All her daily acquaintances are far, far wealthier than she and Marc. In comparison to these immensely wealthy people, Janet begins to feel as though Marc is inadequate and not doing enough to make as much money as her peers’ husbands. Taleb points out that this example shows the emotional effect of survivorship bias: “Janet feels that her husband is a failure, by comparison, but she is miscomputing the probabilities in a gross manner—she is using the wrong distribution to derive a rank” (177-78). Compared to the general population, Marc would be considered very successful, yet within the smaller group of Janet’s extremely wealthy acquaintances, he is not. Taleb refers to this as “comparative failure” (178). In a plethora of other samples, Marc would be considered a success. Taleb advises that if status matters to Janet, then the best thing that she and Marc can do is to move out of the Park Avenue neighborhood and into a new area where the comparisons would reveal Marc to be successful.

Taleb moves on to discuss “Double Survivorship Biases” (179). According to the book The Millionaire Next Door, Taleb says, the wealthiest people are those who are least suspected of being wealthy. The book advocates a strategy of accumulation of wealth rather than conspicuous consumption. Taleb criticizes the book’s thesis, saying that the authors selected only rich people who were winners. Secondly, Taleb points out that the rich people in the book experienced success randomly, noting that their accumulated gains happened at a time when the market was bullish. He says that survivorship bias is “chronic” because “we are trained to take advantage of the information that is lying in front of our eyes, ignoring the information that we do not see” (182). Taleb pivots to a discussion of investment gurus, zeroing in on one in particular, though he does not reveal the person’s name. He presents the guru’s ideas and then reveals that they focus only on successes, not taking into account failures. Taleb closes the section with a brief mention of the word optimism. He claims that optimism is a predictor of both success and failure. While optimistic people are often overconfident, and while their level of confidence can sometimes lead to success, it can also cause failure.

Taleb presents three commonplace scenarios from his own life: going to the dentist, attending a piano performance at Carnegie Hall, and visiting the Victoria and Albert Museum in London. Taleb argues that past performance provides him with reliably predictable expectations of his dentist visit and piano performance. At the museum, as he views works by 18th-century Italian sculptor Antonio Carnova, he wonders if randomness played a role in their creation. He then mentions that he will be having a meeting the following day with a fund manager seeking his help finding investors. Taleb argues that unlike the dentist visit or piano performance, this manager’s past successes at making money do not provide reliable evidence that he will continue to make money in the future. He says that while a proven track record does merit consideration, it should be kept in perspective. Taleb uses these scenarios as an entry point into his discussions of “survivorship bias, data mining, data snooping, over-fitting, regression to the mean, etc., basically, situations where the performance is exaggerated by the observer” (187), all of which he examines in the chapter.

To begin, Taleb describes an experiment using the Monte Carlo simulator which he calls “Placebo Investors” (187). The experiment is an exercise in probability that shows how investing is more prone to randomness than many in finance want to think it is. Winners will always emerge, and though these winners only represent 2% of the total number of theoretical investors, they garner all the focus. Taleb asks, what about the other 98%? He argues that “the number of managers with great track records in a given market depends far more on the number of people who started in the investment business” (190).

Taleb moves on to discuss regression to the mean using a basketball analogy to help illustrate the concept: “It is very likely in a large sample of players for one of them to have an inordinately lengthy lucky streak” (191). Taleb argues that the larger the variation from the mean, the more likely that variation is a product of randomness. Taleb then discusses ergodicity, a term used to describe the way that time can dilute the effect of randomness. He contextualizes both concepts as extensions of survivorship bias and concludes that when someone informs him of their great track record, Taleb sees very little merit or value in this claim.

Taleb turns to coincidence, beginning with a description of a scam in which the scammer manipulates people into false investments. He discusses other extensions of survivorship bias, including its opposite, reverse survivors, in which the average performance of a sample is pushed down by removing the exceptionally successful. He also discusses data mining, in which data is used to rationalize an outcome in hindsight. He discusses data snooping, a process in which the more one looks for patterns in random connections, the more one is likely to find them. Taleb examines the way statistics and medicine overlap. Because of data mining, medical studies can yield odd results, such as one study that found cigarette smoking has been linked to a reduction in breast cancer rates. This example shows the limitations of data mining and illustrates that over-manipulation of data can yield unreliable results.

Taleb concludes this chapter with a discussion of comparative luck. He recalls watching a television commercial in which a former throat cancer patient claimed to have been saved by the use of vitamins. Taleb then examines how some cancers go into spontaneous remission and that was more likely the cause of the patient surviving cancer than the product he was promoting. Taleb also provides the probability figures for this. He mentions ways that people have tried to predict anomalies and tame randomness, almost all of which have proven futile, except in the casino industry.

Taleb begins the chapter by stating that he will address the adage that life is unfair. One small thing can cause calamity. However, the inverse of this can also be true: Someone who experiences a small advantage can reap tremendous benefits from it. As a way of showing how one seemingly insignificant event can cause a catastrophe, Taleb describes building a sandcastle. As the castle grows vertically, eventually it collapses due to one grain of sand. He identifies these as nonlinear dynamics, which in academic circles is referred to as chaos theory. Taleb provides examples of different applications of this theory: In population models, one small variation can cause a growth explosion or extinction. In show business, a particular actor happening to land a leading role will experience dramatic success, while those who do not land the role will not. While skill might play some part in the selection, Taleb argues that more likely, that actor landed the role because he was lucky and in the right place at the right time. This initial stroke of luck then compounds upon itself which leads to exponential growth. He mentions Bill Gates as an example of someone who was initially lucky, and whose status compounded over time.

Taleb uses this to segue into a discussion of a mathematical model called the Polya process, which is a version of the urn experiment. In the Polya urn model, a number of black and white balls are put into an urn. Every time a white ball is taken out, the likelihood of pulling out a black ball gets higher, due to the ratio of white to black balls changing with each removal. Taleb asks the reader to imagine playing roulette. If the player wins, does that mean it will impact their chances of winning a second time? Conventional models would say no; however, Taleb argues that “in a Polya process case it does” (213). Taleb explains that this concept is difficult to work with because “the notion of independence […] is violated” (214). Taleb asks rhetorically what went wrong with economics as a science and then posits answers that hinge on a well-intentioned rush to incorporate mathematics into the science as evidence of rigor. Taleb is critical of the way conventional mathematical processes are used in economics, concluding that “mathematics is merely a way of thinking and meditating, little more, in our world of randomness” (215).

Taleb pivots toward a discussion of the human brain, noting that it is not adept at dealing with nonlinearity. Our emotions cause us to think linearly. He gives the example of a student who studies relentlessly for an extended period; if the student is not able to sense growth or development, they become increasingly likely to give up. Because of this tendency, people are often unable to recognize the rare event. Taleb again refers to a student, this time of the piano. A student who spends forever learning the piano and makes no progress toward learning even a basic song like “Chopsticks” suddenly one day can play Rachmaninov. Taleb argues that this kind of thing does indeed happen; however, most people do not have the stamina to keep working toward the goal if they are unable to discern progress.

Taleb discusses Buridan’s Donkey. The concept involves positioning a donkey equidistant from food and water. The donkey will remain in place, unable to choose between food or water. Once an added element of randomness is thrown in, say with a gentle shove in the direction of food, the donkey will move toward food and then circle around afterward to get water. Buridan’s Donkey describes a common scenario in life when a person is stuck between two decisions, unable to make up their mind. Much like the shove introduces randomness to the donkey, people will do similar things such as flipping a coin and going with whatever the predetermined course of action might have been depending on heads or tails.

Taleb asks the reader to imagine two different vacation options: one in Paris and the other in the Bahamas. Taleb points out that a person can only visualize themselves in one place at a time. This is because our brains “can properly handle one and only one state at once” (219). Taleb uses this to suggest that human beings are not rational. Taleb shifts gears and reintroduces Nero from earlier chapters. He reveals that Nero has received a cancer diagnosis. He learns that his survival rate is 72%. He feels encouraged by this information. Taleb points out that in mathematical terms, there is no difference between a 72% survival rate and a 28% death rate. Nero’s focus is only on the 72% survival rate because the “emotional apparatus that jolts us into action does not understand such nuances” (222).

Taleb pivots to a discussion of rules. He presents a hypothetical scenario involving a socialist bureaucrat and a person who wishes to export chocolate. The bureaucrat does not consider the economic implications of exporting chocolate; instead, he runs through a checklist to see that all rules are followed. The rules here have value because they save the bureaucrat time and energy and act as a shortcut. Taleb suggests that our brains have developed similar shortcuts for survival purposes, pointing out that the first humans could not afford to consider which kind of tiger was chasing them. Instead, they saw a tiger and ran for their lives.

Taleb discusses Herbert Simon, a Nobel Prize-winning economist who claimed that “if we were to optimize at every step in life, then it would cost us an infinite amount of time and energy” (224). Therefore, rules that govern approximation are human essentials. Simon coined the term “satisficing,” a melding of satisfy and suffice, to describe the way humans make approximate decisions. Taleb then introduces two Israeli psychologists, Daniel Kahneman and Amos Tversky, whose work in heuristics led to the development of behavioral finance. Their work also revealed that biases are a side effect of the shortcuts Taleb discusses in this chapter. Taleb moves to a discussion of the differences between normative science, which studies of how things should be, and positive science, which studies how things are through observation. While physics is a positive science, economics is primarily normative.

Taleb says that our brains operate by “disconnected rules” and asks the reader to imagine the brain as partially consisting of a rulebook. Reactions often depend on randomly landing on a particular rule in our internal rulebook. As an analogy, Taleb discusses the Russian legal system after the collapse of the Soviet Union. Because laws were created piecemeal, they were often contradictory. Taleb mentions that in Napoleon’s time, there were similar problems in the legal system, so to rectify that, Napoleon created a top-down system. Taleb argues that this is what our brains do not have.

Taleb details various heuristics in the next section of this chapter, beginning with “I’m As Good As My Last Trade” (228). This rule allows people to arbitrarily reset their mental rubric to a particular point. Taleb uses gambling as an example of this heuristic at work: gamblers move the goalposts to justify continuing to gamble. Eventually, in trading, this leads to looking only at changes in profit. Taleb claims that it is easier for the brain to look at changes and variations and reminds the reader of Marc and Janet from an earlier chapter. He continues his discussion of other heuristics: the availability heuristic, the representative heuristic, the simulation heuristic, and the affect heuristic. Taleb further describes people’s decision-making system, of which there are two parts. The first is automatic and often made while the person is unaware. The second is more deliberate and requires effort and calculation. He uses chess players as an illustration. When a person first learns how to play, they depend on system two; however, as the person gains skill, they eventually move toward a more automatic decision-making style.

Taleb shifts to a discussion of evolutionary biology and notes that for much of human history, the need to determine probabilities was quite limited. Taleb examines the views of neurobiologists, who believe that humans have three brains: the very ancient brain, the limbic system which deals with emotion, and the neocortex where cognitive activity resides. He discusses brain mapping and concludes that there is strong evidence showing that emotion is required for decision-making and that the limbic system has more influence on the neocortex than the other way around.

In the O. J. Simpson trial, Taleb asserts, probability was misrepresented by his legal team and misunderstood by the trial jury. Taleb sees potential advantages to some individuals living in a society where people commonly misunderstand probability, but he states that living in such a society is unnerving. Taleb additionally references Kafka’s The Trial as a means of further highlighting the absurdity of the Simpson trial, especially about the misuse of probability.

Taleb concludes this chapter with a section devoted to the media. He says that journalists are trained to express their thoughts and many journalists, medical journalists especially, do not understand probability when they report on it. He then poses a hypothetical scenario in which a chemotherapy drug is found to barely outperform a placebo. A journalist might see the news and suggest that the new treatment does not lead to improved results, and this might have a trickle-down effect where other doctors who read this journalist might develop an inherent block against the new medicine simply because the journalist jumped to conclusions and made the pronouncement. Taleb further explores the tendency of journalists to oversimplify messages. He discusses television financial analysts and examines how these people frequently argue a singular cause for some anomaly in the stock market when the truth is that the cause is complex and has many explanations. Finally, Taleb spends a brief interval discussing ways he has learned to isolate a signal from the noise, the latter of which is compounded by journalists.