Fooled by Randomness by Nassim Nicholas Taleb

Fooled by Randomness by Nassim Nicholas Taleb

The book contains many interesting examples of common biases and logical fallacies, but it’s buried in a lot of bluster and fluff about how smart the author is. While it was likely groundbreaking when it was published in 2004, its ideas have since permeated into the mainstream. Reading it in 2018, the ideas feel neither novel nor original. Thinking Fast and Slow covers the same material with more depth and better writing.


What I Liked

  • Contains many interesting examples of ways people typically derive meaning from random noise or misapply statistics:

What I Disliked

  • The most smug book I’ve ever read.
    • Wastes so many pages mocking financial authors and TV personalities.
  • The author makes a specific point in the preface about how he eschewed the advice of editors and felt validated by the sales of his book, but I noticed many instances that could be improved with editing.
    • I felt like the book succeeded despite editing.
  • The author puts far too much faith into Monte Carlo simulations
    • There are several instances where the author builds his own Monte Carlo simulation of the market, then concludes that a certain trading style is more profitable than another.
    • I am highly skeptical that anyone could get meaningful results by attempting to model the behavior of every market participant.

Key Takeaways

  • Black swan problem
    • No matter how many white swans you see, you can never conclude that all swans are white because seeing a single black swan would disprove this.
    • People tend to underestimate the probability of black swan events and make themselves vulnerable when such events occur.
    • “…it does not matter how frequently something succeeds if failure is too costly to bear.”
  • People tend to be overconfident that events won’t occur if they never or rarely occurred in the past.
    • Rare events are by nature unexpected and unpredictable, so lack of predictors for an event doesn’t mean that it can’t occur.
  • More experienced traders tend naturally to be more resistant to rare events.
    • A longer trading lifespan means that they’ve likely experienced previous rare events and survived them.
  • Sampling rate has a strong effect on satisfaction with an investment strategy.
    • People feel the pain of loss more acutely than the joy of gain.
    • Even a profitable investment strategy has periods of losing money due to random fluctuations in the market.
    • A person will be overall less happy with their investment strategy if they check it obsessively because they’ll observe many losses and gains due to random fluctuations, but the losses will feel more severe than gains.
  • When people talk about life expectancy being 73, that’s the unconditional life expectancy at birth.
    • This number is based on the average of the entire population.
    • As a person ages, their life expectancy increases based on the fact that they haven’t died.
    • A 35-year-old’s life expectancy is the average lifespan of everyone who survived to at least age 35, not the average lifespan of anyone who was ever born.
  • When we ask, “What are the habits and qualities of successful people?” we rarely ask how common those same traits are in unsuccessful people.
    • e.g., one could conclude that it’s a good strategy to buy lottery tickets if one surveyed several lottery winners and recognized that their common trait is purchasing lottery tickets regularly.
    • Sometimes successful people are just those who followed a poor strategy but were in the lucky minority for whom it paid off.
  • Herbert Simon introduced the idea that humans are “boundedly rational.”.
    • If people were perfectly rational, it would take too long to weigh all factors in a decision.
    • Instead humans “satisfice” (satisfy + suffice) — they stop thinking about a decision when the result is good enough.
  • People are bad at forecasting events that are abstract.
    • Scenario 1 is abstract but includes Scenario 2. Despite this, people typically assign a higher likelihood to Scenario 2 because it’s a more vivid picture.
      • Scenario 1: A massive flood somewhere in North America next year, in which more than 1,000 people drown
      • Scenario 2: An earthquake in California sometime next year, causing a flood in which more than 1,000 people drown
  • News media is biased toward stories
    • News outlets often create stories when none exists because nobody will read/watch if they attribute events to random noise.
  • “The Median is Not The Message”
    • People put too much faith in means or medians without considering the probability distribution of an event
      • e.g., a person diagnosed with mesothelioma has a median life expectancy of eight months, but a review of the probability distribution reveals that eight months doesn’t mean much — many people die soon after a diagnosis, but many also live for decades
  • Traders must consider both the probability and magnitude of an event
    • Even if you think a stock has a 95% chance of increasing in value, it can still be worth betting against it if it has a 95% chance of increasing by $1 and a 5% chance of dropping by $30.
    • The 5% chance can be even more profitable because markets tend to underestimate the probability of a rare event.
  • “A mistake is not something to be determined after the fact, but in the light of the information available until that point.”
  • A possible evolutionary psychology explanation for human biases is that most of human evolution did not require an understanding of probabilities
    • For most of human history, humans consumed a very small amount of information.
      • Humans knew a relatively small number of people around them, news from outside the community came in small spurts
    • Humans could survive without an understanding of probabilities
      • e.g., if a crime was committed, it was usually obvious who did it.
  • One of George Soros’ most valuable qualities is that he’s comfortable being wrong.
    • He frequently changes his opinion rather than clinging to past ideas and rationalizing them.
  • Taleb loves philosopher Karl Popper.
    • Popper believes that the only real scientific theories are those that have been disproven and those that will be disproven in the future.
  • Imagine 10,000 investors have a 50/50 chance of making or losing money each year
    • In five years, we could expect 313 of them to have made money each year by sheer luck, but everyone would applaud their investing skill.
    • Even if they only have a 45% chance of making money, at the end of the five years, 184 will still have made money every year.
  • Buridan’s donkey
    • “Imagine a donkey equally hungry and thirsty placed at exactly equal distance from sources of food and water. In such a framework, he would die of both thirst and hunger as he would be unable to decide which one to get to first.”
    • Injecting randomness into the decision solves the problem because the donkey can make a decision even though it’s not necessarily optimal.
  • Nassim Taleb would like you to know that Nassim Taleb is intelligent and sophisticated.

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