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

Taleb introduces Carlos, a man who made his living in emerging market bonds. Taleb explains that emerging markets are a euphemism for developing countries’ markets and that the bonds are financial instruments for these countries to foster more development. Taleb describes Carlos as intelligent and a deep thinker. According to Taleb, Carlos had the characteristics needed for success and in the early 1990s, he began to climb in his career. Carlos was particularly skillful at buying during panics over emerging bond prices. Carlos persevered through a series of occasional setbacks which over time led to an inflated sense of confidence. Carlos had accumulated $80 million for his bank throughout his career, but in 1998, he lost $300 million. Taleb explains that Carlos insisted on holding onto Russian bonds way past what many analysts considered was the sell point. When the bonds continued to dip, Carlos insisted that at some point, the price would recover, but it did not. As his bank became doubtful of Carlos’s position, he pointed to the failure of other banks and tried to reframe the losses comparatively. This tactic did not work. Taleb says that one of Carlos’s biggest mistakes was putting too much emphasis on what other traders were doing, something that he describes as the “firehouse effect.” Taleb says that a “trader’s mental construction should direct him to do precisely what other people do not do” (120). Carlos did not adhere to this principle. Eventually, Carlos was fired. Taleb says that he is no longer involved in the market.

Taleb reintroduces John from Chapter 1 and provides a more comprehensive background, beginning with John’s formal education. John graduated from Pace University’s Lubin School of Business and upon graduation, began experiencing immediate success as a trader. John specialized in “high-yield” trading, which Taleb describes in some detail before introducing Henry, an associate of John’s whom he relied upon for his math skills. With Henry adding brains to John’s methods, John soon began to accumulate great wealth for those who employed him. His wealth also grew exponentially in a relatively short amount of time. By the age of 35, John’s net worth was $16 million (123). In 1998, as was the case with Carlos, John also lost everything in the blink of an eye. Taleb says of the dramatic loss John suffered: “Markets went into a volatile phase during which nearly everything he had invested in went against him at the same time” (124). At first, John insisted that the market would resolve in his favor, but after a week, it did not. Taleb suggests that while by all appearances, John’s perfect track record should have enabled him to carry through, the fact that it did not could be attributable to randomness. Taleb asserts that John will never recover from the blow-up that he experienced, even though he was still able to retain a million dollars of his own wealth. The reason Taleb gives is that even though most traders accept losing money as an inevitability, a blow-up of the kind experienced by John is not. Taleb also argues that John was not truly skilled in the first place. He merely happened to have the right high-yield strategy at the precise time that it was most likely to succeed. Taleb mentions that not all emerging markets or high-yield traders behave like Carlos and John. Paradoxically, Taleb says that only the most successful ones are like Carlos and John and that they were successful because they were in the right place at the right time with a particular strategy that optimized success in the short term. In short, they were lucky.

Taleb breaks down the series of mistakes that John and Carlos made. It is an exhaustive list that includes “An overestimation of the accuracy of their beliefs”; “A tendency to get married to positions”; “Absence of critical thinking expressed in absence of revision of their stance”; and “Denial” (126-29). Even though Carlos and John made this slew of mistakes, they still experienced great short-term success. Taleb argues that this is simply because “One can make money in the financial markets totally out of randomness” (129).

Taleb closes this chapter with a discussion of Darwinism, specifically the common misunderstanding of the concept of the survival of the fittest. Evolution, he explains, takes place over a long timescale and does not indicate what many think it does. As an example, Taleb points out that just because a company moves continuously toward success, this does not prove that it is somehow stronger than others. It does not survive simply for this reason. Taleb points out that “Darwinian ideas are about reproductive fitness, not about survival. The problem comes, as everything else in this book, from randomness” (130). He then breaks down what this means from an evolutionary biology perspective, and then connects it to economist perspectives. He says: “Darwinian fitness applies to species developing over a very long time, not observed over a short term—time aggregation eliminates much of the effects of randomness” (131). Taleb reminds readers that John, while profitable over the short term, was a pure loser over the long term, all of which he attributes to randomness.

The chapter begins with an anecdote that explains the difference between the expected and the median. Taleb describes the cancer diagnosis that had been given to writer Stephen Jay Gould in which the median survival was approximately eight months. In statistics, the median is the value lying at the midpoint of the distribution of data. That means that 50% of the outcomes will by definition be lower than the median, and 50% will be above. What Gould realized after research is that among the 50% who survived more than the median eight months, many could live a long and almost normal life well into their seventies. Taleb defines this as asymmetry and claims that “Whenever there is an asymmetry in outcomes, the average survival has nothing to do with the median survival” (135). Taleb then discusses the statistical mean, which is determined by adding all values together and dividing by the number of values. He presents a table in which there are two different events, each with different probabilities, outcomes, and expectations. Event A shows a stable but small gain bet and Event B shows a lower probability, higher gain bet. He says that in finance, people often make the mistake of not properly considering the mean rather than the median. He attributes this to conditioning acquired in education to only consider symmetrical environments.

Taleb pivots to a discussion of the popular terms “bearish” and “bullish” often used to describe financial markets. Taleb is loath to use such terminology and explains that these terms have no real application when asymmetrical outcomes are the norm. Taleb argues that for those practicing in uncertainty, the words bullish and bearish do not offer clarity and should be avoided, and insists that in finance, “How frequent the profit is irrelevant; it is the magnitude of the outcome that counts” (139).

Taleb continues his discussion of asymmetry and how it is undervalued both in finance and climate science. He discusses how in many fields, such as education and the tabulation of grades, the extreme numbers at either end are considered outliers and because these can skew the average one way or the other, they are dropped from the calculation. Taleb asserts that this is problematic when studying the climate, pointing out that it “may be a good idea to take out the extremes when computing the average temperatures for vacation scheduling. But it does not work when we study the physical properties of the weather—particularly when one cares about a cumulative effect” (143). Taleb says that initially, scientists underestimated the effect temperature had on the polar icecaps because they underappreciated the impact of temperature spikes.

Taleb explains how asymmetry can upend predictive tools like time series charts. He says that while such a tool can be useful in some circumstances in finance, it can also “be meaningless” and “could on occasion mislead you and take you in the opposite direction” (145). This mistake stems from an overreliance on the past when trying to predict the future.

Taleb transitions into an in-depth discussion of the rare event. Taleb reveals that the way he looks at rare events is to “label a rare event as any behavior where the adage ‘beware of calm waters’ can hold” (147). A rare event is most likely to take place when things appear stable. Taleb lays out the ways rare events can affect a single security or an entire portfolio. This leads to a more detailed explanation of the “Peso Problem,” which is an investing strategy that does not take into account historical periods of instability, especially during times of stability.

Taleb addresses why statistics do not predict rare events. He says that “[c]ommon statistical method is based on the steady augmentation of the confidence level” (150). Statistics fail when “distributions […] are not symmetric” (150). He uses a visual analogy to help explain his point, describing red and black balls in an urn. Taleb says that if the probability of finding a red ball is low, then the observer’s attention will be drawn to the absence of red balls. However, if a red ball is found, then that increases the observer’s propensity to believe that others will be found. Taleb notes that this “asymmetry in knowledge is not trivial; it is central” (151). Taleb pivots into a discussion of econometrics, noting that it is a science that “consists of the application of statistics to samples taken at different periods of time” (152). As the chapter concludes, Taleb shows that financial engineering uses history to predict the future but does not consider the impact of randomness as a variable the way that it should.

The final chapter of Part 1 centers on the work of 20th-century philosopher Karl Popper. Taleb lays out the history of an issue in the philosophy of science called the problem of induction. Inductive reasoning takes particular observations and extrapolates universal conclusions from them. The 16th-century philosopher Francis Bacon, one of the main figures in the Scientific Revolution, championed inductive reasoning in his original discussions of what would become the scientific method. In the 18th century, philosopher David Hume argued that there was no rational basis for inductive conclusions, arguing that they were unreliable. Taleb agrees with Hume’s critique of empiricism and inductive reasoning, calling Hume “the first modern epistemologist” and claiming that Hume “never believed that a link between two items could be truly established as being causal” (155).

Taleb then segues into the story of Victor Niederhoffer, a statistician and hedge fund manager. As a professor of finance in the 1960s, he argued that “any ‘testable’ statement should be tested, as our minds make plenty of empirical mistakes when relying on vague impressions” (156). Taleb demonstrates how this proposition can be applied to empirical statements. He poses these two statements side-by-side: “Statement A: No swan is black because I looked at four thousand swans and found none. Statement B: Not all swans are white” (157). Taleb says that the first statement is illogical and shows the limitations of empirical knowledge gained from direct observation. Taleb argues that the second statement can be disproven, or falsified, which it was. Taleb points out the influence of Popper here and says that the asymmetry of these juxtaposed statements “lies in the foundation of knowledge” (157). Niederhoffer’s investment firms experienced catastrophic losses in the Asian financial crisis. Taleb attributes the blow-up to a view of the past that resembles Statement A above: Niederhoffer assumed that “what he saw in the past was an exact generalization about what could happen in the future,” a view that made him vulnerable to a black swan event such as the Asian market collapse (158). Taleb also points out that Niederhoffer was highly competitive and was an accomplished squash player. He suggests that unlike games, which operate according to discrete rules, reality follows a different system; therefore, a competitive spirit, while fine for games, is not a good trait for an effective trader.

Taleb shifts to describing his encounter with the work of Karl Popper. Though Popper’s work did not resonate with Taleb when he first encountered it in college, he describes being engrossed by the philosopher’s work when he rediscovered it later. Popper argued that any so-called scientific theory must be falsifiable, otherwise “it can only be ‘provisionally accepted’” (164). Taleb admires Popper’s work primarily because Popper insisted that a scientist should attempt to find flaws in their ideas, which demands rigor. Popper’s work challenged an approach to scientific truth as positivism, which argues that everything works according to knowable, generalizable rules. Popper objected to positivism’s tenet that all truth should be verifiable by either logic or observation. Taleb points out that Popper’s views were in direct contradiction to this movement, pointing out that for many statements—such as “all swans are white”—“verification is not possible” (166). Popper’s concept of “falsification” holds that a scientific theory cannot be verified; it can only not have been falsified yet. From here, Taleb examines how Popper’s ideas facilitate an open society and rule out any kind of utopian society because “it chokes its own refutations” (167).