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Book Notes: “Fooled by Randomness” by Nassim Nicholas Taleb

Summary

Fooled by Randomness by Nassim Nicholas Taleb (2004) is a book about the role of luck and uncertainty in our daily lives. The central thesis of the book is that humans are poor judges of randomness and grossly underestimate its role and influence in everything. As such, we are prone to false narratives, cause-and-effect explanations and story-telling to make sense of the world; we are inclined towards deterministic thinking and poorly wired for probabilistic thinking.

This tension between deterministic thinking and probabilistic thinking is a central theme in Taleb’s book. Deterministic thinking involves certainty, causality, deduction, and an emphasis on skills and performance. Probabilistic thinking acknowledges luck, induction, coincidence, bias and cognitive blindspots, and uncertainty. Taleb doesn’t argue for one approach over the other, per se. There’s clearly a role for both, particularly where competent dentists are involved (a profession Taleb appears to admire and that is relatively resistant to randomness). Rather, Taleb’s insists that we are dangerously over-indexed on the former (determinism) and woefully ignorant of the latter (probability). Taleb’s objective, therefore, is to inform and equip the reader with the vast array of probabilistic thinking tools the author has collected over a lifetime of study and experience.

The range of ideas and thinking tools introduced by Taleb are dizzying. Over the course of 14 chapters readers will learn about Monte Carlo simulations, ergodicity, induction and the black swan problem, asymmetry and skewness, nonlinearity, skepticism and much more. Individually, any of these topics might warrant a standalone book. Suffice it to say, this is one book for which taking notes is imperative. I know I will be referencing mine (see below) regularly for years to come.

Given the topical breadth of Fooled by Randomness, it’s no surprise that—despite being ridiculously informative and even funny at times—the book feels overwhelming and is a bit of a structural mess. The material is simply too expansive (as a reader, I’m glad I read Charles Wheelan’s Naked Statistics first—it gave me a firmer statistical basis going into Taleb). Since its publication, Fooled by Randomness has published four more books, The Black Swan (2010), The Bed of Procrustes (2010), Antifragile (2012), and Skin in the Game (2018). Together these titles comprise Taleb’s Incerto—a sequence of explorations the rich topic of uncertainty. In this context, Fooled by Randomness, the first in the sequence, can be viewed as an introduction to the writings of Taleb; readers receive a nice sampling of his ideas, but to get a fuller picture, one must dive into the rest of his philosophical corpus.

Pros: Offers a window into a fascinating mind and an excellent introduction to probabilistic thinking and uncertainty.

Cons: I find some of Taleb’s definitions and explanations of statistical concepts like regression analysis and expected value lacking. This is a minor nit that’s overcome by ad hoc Google searches.

Verdict: 8/10


Highlights

Prologue

  • “We underestimate the share of randomness in about everything.”
  • The book is about luck disguised as “non-luck” (e.g. skills) and randomness disguised as non-randomness (e.g. determinism).
  • The lucky fool: A person who benefits from disproportionate luck, but attributes their success to non-random reasons. The manifestation of randomness fooling us.
  • People are prone to narrative, after-the-fact rationalization, and story-telling to make sense of the world.
  • Our social and individual belief systems are predicated on ascribing cause-and-effect explanations even when there are none to be had or they are false.
  • “The literary mind can be intentionally prone to the confusion between noise and meaning, that is, between a randomly constructed arrangement and a precisely intended message.”
  • Symbolism: Exhibits our inability to accept randomness. We imbue meaning in whatever we can.
  • The human mind is not equipped to handle probabilistic thinking. Examples of this confusion (consider the left vs. right term as opposites on the spectrum between randomness and determinism).
Luck					Skills
Randomness				Determinism
Probability				Certainty
Belief, conjecture			Knowledge, certitude
Theory					Reality
Anectdote, coincidence			Causality, law
Forecast				Prophecy

Lucky idiot				Skilled investor
Survivorship bias			Market outperformance
Volatility				Return
Noise					Signal
Induction				Deduction
  • Thinkers whose work embodies uncertainty:

    • Karl Popper
    • Friedrich Hayek
    • Milton Friedman
    • Adam Smith
    • Herbert Simon
    • Amos Tversky and Daniel Kahneman
    • George Soros
    • Charles Sanders Pierce
  • Two camps of thinkers:

    • Those who see easy, clear-cut answers.
    • Those who see complexity and nuance and the distortions introduced by oversimplification.

Part I: Solon’s Warning (Skewness, Asymmetry, Induction)

  • Solon’s Warning (to the rich King Croesus): “The observation of the numerous misfortunes that attend all conditions forbids us to grow insolent upon our present enjoyments or to admire a man’s happiness that may yet, in course of time, suffer change. For the uncertain future has yet to come...”
  • That which is gained by chance, can also be taken away by luck.
  • Things that require little luck are more resistant to randomness.
  • The problem of induction: Taleb calls this the problem of the black swan or rare event.
  • The problem of skewness: “It does not matter how frequently something succeeds if failure is too costly to bear.”

Chapter 1: If You’re So Rich, Why Aren’t You So Smart?

  • “Mild success can be explainable by skills and labor. Wild success is attributable to variance.”

  • Risk-conscious hard work and discipline can, with high probability, result in a comfortable life.

  • Taleb suggests a different way of viewing outcomes: consider the outcome in light of all the other possible alternative outcomes.

    • Future events are about what may happen.
    • Events observed in the past are subject to hindsight bias since they occurred and are 100% certain.

Chapter 2: A Bizarre Accounting Method

  • “One cannot judge a performance in any given field (war, politics, medicine, investments) by the results, but by the costs of the alternative (i.e. if history played out in a different way).”

  • “$10 million earned through Russian roulette does not have the same value as $10 million earned through the diligent and artful practice of dentistry. They are the same, can buy the same goods, except that one’s dependence on randomness is greater than the other.”

  • Risks in reality (real life) are harder to determine than in games with understood probabilities (like Russian roulette).

  • People confuse and conflate forecasting (probabilistic thinking) with prophecy (deterministic thinking) all the time.

  • People are terrible at assessing general (or abstract) risk compared to specific risks.

    • The general risks are more likely, but easier to misread and ignore.
    • Example from the research of Kahneman and Taversky: Travelers are more likely to pay for insurance in the event of death from a terrorist act than were they to die during a trip (the latter is more general and encompasses terrorist acts).
  • “Our brain tends to go for superficial clues when it comes to risk and probability, these clues being largely determined by what emotions they elicit or the ease with which they come to mind.”

  • Risk is mediated by our emotional mind, not our rational mind.

  • When we employ rational thinking to address risk, it is often used to rationalize our thinking in order to create a logical narrative to our actions.

  • “Beware the confusion between correctness and intelligibility.” This reminds me of the problem of accuracy and precision recounted in Naked Statistics by Charles Wheelan.

  • Theatrical displays are a common pattern: “The existence of a risk manager has less to do with actual risk reduction than it has to do with the impression of risk reduction.”

Chapter 3: A Mathematical Meditation on History

  • Monte Carlo generator (aka Monte Carlo experiments or Monte Carlo simulations) is a method for modeling the probability of different outcomes in hard-to-predict situations (with multiple random variables).

    • Taleb relies on it as a tool for thinking (“to meditate”) more than as a computational method.
    • It’s a way to “toy with uncertainty.”
  • The Monte Carlo method creates an artificial history:

    • Alternative sample paths: Alternative outcomes for a given scenario.

    • Sample paths can be random or deterministic.

    • Random sample paths: Virtual historical events subject to varying levels of uncertainty. Random sample paths vary in probability—some are more likely than others.

      • Example: A stock price simulation’s year-end closing price. Stock price starts at $100. Simulation might envision one outcome where the stock ends up at $20 (after a high of $220). Another scenario might show a closing price of $145 (after a low of $10).
    • Stochastic processes: The dynamics of events unfolding over the course of time. Specifically: the study of successive random events as they evolve.

    • Computer programs are ideally suited to running Monte Carlo simulations. Using a computer, a person can run millions of random sample paths (for example with a virtual roulette wheel simulation). The results of the simulation can be studied for insights.

  • The Monte Carlo method offers a way to compare the realized outcome against the non-realized ones.

  • “Learning from history does not come naturally to us humans, a fact that is so visible in the endless repetitions of identically configured booms and busts in modern markets.”

  • “Characteristically, blown-up traders [those who incur huge, unexpected losses] think that they knew enough about the world to reject the possibility of the adverse event taking place: There was no courage in their taking such risks, just ignorance.”

  • Historical determinism: The flawed view that history appears to be deterministic because only one single, observable outcome took place. We can create rationalizations and potentially flawed causal event chains to make sense of these events (and discount randomness in the equation).

    • Hindsight bias: The overestimation of knowledge due to subsequent information (aka “I knew it all along”).
    • “A mistake is not something to be determined after the fact, but in the light of the information until that point.” [Reminds me of Annie Duke’s admonition to separate process from results in her book “Thinking in Bets]
  • Ergodicity: Wikipedia definition: “In mathematics, ergodicity expresses the idea that a point of a moving system, either a dynamical system or a stochastic process, will eventually visit all parts of the space that the system moves in, in a uniform and random sense.”

    • Random samples from a process will represent the average statistical properties of said process.
    • In the short-run, the lucky fool might benefit from randomness. Over the long-run, his lucky would regress to the mean.
  • People have difficulty separating signal from noise in markets and the world of information.

    • Robert Schiller (economist): Influenced thinking about the negative value of information on society.

      • Markets are not as efficient as we believe.
      • Prices do not rationally reflect the long-term value of securities. They “overshoot” in one direction or the other.
    • “Prominent media journalism is a thoughtless process of providing the noise that can capture people’s attention...”

  • Randomness has a scaling property that is time dependent.

    • Taleb considers minute-by-minute stock market price performance reviews vs. annual ones. The minute-scale yields far more noise than an annual review.
    • In the short timeframe, one observes variance (and little else).
    • Humans are poorly equipped to understand the above idea (it requires rational perspective). [Me: The parable of the Taoist farmer reminds me of this idea]
    • “People who look too closely at randomness burn out, their emotions drained by the series of pangs they experience.”

Chapter 4: Randomness, Nonsense, and the Scientific Intellectual

  • Deductive reasoning: Knowledge that stems from defined, axiomatic thinking. Example: 2+2=4.

  • Inductive reasoning: Knowledge that flows from verifiable experience or observation. Example: It rains in Spain.

  • “Inductive statements may turn out to be difficult, even impossible, to verify, as we will see with the black swan problem—and empiricism can be worse than any other form of hogwash when it gives someone confidence.”

  • “We do not need to be rational and scientific when it comes to the details of our daily life—only in those that can harm us and threaten our survival.”

    • Taleb justifies his love for poetry based on this belief.
    • Taleb unleashes his scorn for those who listen to the analyses of television gurus and tips from neighbors based on this belief as well.

Chapter 5: Survival of the Least Fit—Can Evolution Be Fooled by Randomness?

  • The firehouse effect (a type of group think): “Firemen with much downtime who talk to each other for too long come to agree on many things that an outside, impartial observer would find ludicrous...”

  • The cross-sectional problem: At a given time, the most successful traders in the market are those whose investing-style best fits the current cycle. Their approach benefits from randomness (it’s susceptible to randomness).

    • Remember Solon’s warning: What randomness gives, randomness can also take away.
  • Common traits in financial professionals fooled by randomness:

    • An overestimation of the accuracy of their beliefs (e.g. economic, statistical).
    • The US dollar was overvalued (or foreign currencies were undervalued).
    • Emotional over-attachment to investments or ideas.
    • Changing your story (to justify changing circumstances).
    • No precise advance contingency plan (for dealing with losses).
    • Denial.
  • “We tend to think that traders were successful because they are good. Perhaps we have turned the causality on its head; we consider them good just because they make money. One can make money in the financial markets totally out of randomness.”

  • Many people have an erroneous one-way view of Darwinian natural selection:

    • They believe that Darwinian evolution only tends towards perfection.
    • They forget that the phenomenon is random and results in diversions, dead-ends, and regression as well.
    • Steven Jay Gould (biologist): Popularized the idea of “genetic noise” and “negative mutations.”
    • This erroneous thinking also infects our views of randomness: we see continuity, convergence, and regularity where there is none.
  • “Just as an animal could have survived because its sample path was lucky, the ‘best’ operators in a given business can come from a subset of operators who survived because of over-fitness to a sample path—a sample path that was free of the evolutionary rare event.”

    • The animal becomes more vulnerable to the rare event (that would make them extinct).
    • “Evolution means fitness to one and only one time series, not the average of all possible environments.”

Chapter 6: Skewness and Asymmetry

  • Expected value vs. median:

    • Median: The midpoint of a frequency distribution of observed values or quantities, such that there is an equal probability of falling above or below it.
    • Expected value: A weighted average of possible outcomes.
  • Asymmetry: A lack of equality or equivalence between parts or aspects of something.

    • “Whenever there is asymmetry in outcomes, the average has nothing to do with the median.”
    • Asymmetric odds mean the probabilities are not 50% for each event. They are weighted in favor of certain outcomes.
    • Asymmetric outcomes means the payoff probabilities are not equal.
  • When there is asymmetry: “The frequency or probability of the loss is totally irrelevant; it needs to be judged in connection with the magnitude of the outcome.”

  • People commonly confuse/conflate probability with probability * payoff. “People’s schooling comes from examples in symmetric environments.”

  • “Bullish or bearish are terms used by people who do not engage in practicing uncertainty, like the television commentators, or those who have no experience in handling risk.”

  • “It is not how likely an event is to happen that matters, it is how much is made when it happens that should be the consideration. How frequent the profit is irrelevant; it is the magnitude of the outcome that counts.”

  • “My lifelong business in the market is ‘skewed bets’, that is, I try to benefit from rare events, events that do not tend to repeat themselves frequently, but, accordingly, present a large payoff when they occur.”

  • “Rare events are not fairly valued, and that the rarer the event, the more undervalued it will be in price.”

  • “History teaches us that things that never happened before do happen. It can teach us a lot outside of the narrowly defined time series; the broader the look, the better the lesson.”

  • In inductive environments, more information can be dangerous.

    • The more information you have, the greater confidence in your conclusion(s).
    • Asymmetric or random distributions create problems.
    • “The problem of stationarity”: Consider a hollow urn containing mostly black balls and an occasional red ball. Unaware to the observer pulling out balls, a mischievous actor is adding balls of one color or the other to the urn. The added balls upend the observer’s inference.
  • Robert Lucas (economist): “If people were rational then their rationality would cause them to figure out predictable patterns from the past and adapt, so that past information would be completely useless for predicting the future.”

Chapter 7: The Problem of Induction

  • David Hume’s explanation of the induction problem: “No amount of observations of white swans can allow the inference that all swans are white, but the observation of a single black swan is sufficient to refute that conclusion.”

    • Scholasticism was based largely on deductive reasoning (no observation of the real world).
    • Empiricism was based on observation and experience (Francis Bacon).
  • Hume’s critique: “Without a proper method, empirical observations can lead you astray.”

  • Naive empiricism: Data can be used to disprove a proposition. It cannot be used to prove one.

    • Example: “The market never goes down 20% in a given three-month period.” The example can be rejected by finding counterexamples.

    • Example: Consider the following statements:

      1. No swan is black. I looked at 4000 swans and found none.
      2. Not all swans are white.
    • Taleb views this as an inherent asymmetry between the two types of knowledge.

  • Karl Popper and the answer to the induction problem:

    • There are only two types of theories:

      1. Theories that are known to be wrong. These theories have been tested, rejected and are “falsified.”
      2. Theories that have not yet been known to be wrong. They have the potential to be falsified at some point in the future.
    • A theory is never absolutely right because of the induction problem (we can’t know if all the swans are white).

    • “A theory cannot be verified...it can only be provisionally accepted. A theory that falls outside of these two categories is not a theory.”

  • “Popper is the antidote to positivism. To him, verification is not possible. Verificationism is more dangerous than anything else. Taken to the extreme, Popper’s ideas appear naive and primitive—but they work.”

  • “No rare event should harm me. In fact, I would like all conceivable rare events to help me.”

  • “What is easier to remember, a collection of random facts glued together, or a story, something that offers a series of logical links. Causality is easier to commit to memory.”

    • Induction takes many particulars and merges them into a general theory.
    • This act of “compression” reduces our ability to detect randomness.
  • Pascal’s Wager: The optimal strategy for humans is to believe in the existence of God. The upside is tremendous if he exists. The downside is minimal if he doesn’t. This is a classic thought experiment for probability theory and decision-making.

Part II: Monkeys on Typewriters (Survivorship and Other Biases)

  • Thought experiment: Take and infinite number of monkeys with typewriters. The monkeys all bang away at their typewriter. One will type out an exact version of the Iliad.

    • The probability of this happening is incredibly low, but it is an example of ergodicity.
    • More importantly: would you be wiling to bet on the same monkey writing the Odyssey next? In other words: Is past performance relevant for future performance?
    • Sample size matters: With infinite monkeys, the writing of the Iliad is unlikely and unimpressive when it occurs (it is owed to randomness). With a small sample of 5 monkeys, the observer might be impressed.
  • In real life, we don’t know how many monkeys there are, but the winners are visible to us.

Chapter 8: Too Many Millionaires Next Door

  • Survivorship bias: We only see winners. Because of this, our understanding of the odds are skewed.

  • Our opinions are often defined by our sample sets. Failure to understand this phenomenon results in flawed conclusions.

    • Example: A couple that lives in a wealthy neighborhood might feel unsuccessful because they are surrounded by other successful people. Their sample set only includes winners.
    • Sample sets often include “visible observations.” We must try to also find the “invisible” data that provides a complete picture.
  • “We tend to mistake one realization among all possible random histories as the most representative one, forgetting that there may be others.”

  • “Survivorship bias implies that the highest performing realization will be the most visible. Why? Because the losers do not show up.”

Chapter 9: It Is Easier to Buy and Sell than Fry an Egg

  • There are fields where skill is the driving force and randomness is low. Taleb highlights dentistry and musical performers. The challenge is identifying those disciplines that exhibit low randomness.

  • “Machiavelli ascribed to luck at least a 50% role in life (the rest was cunning and bravura), and that was before the creation of modern markets.”

  • Variations of performance records and historical time series: aka “situations where the performance is exaggerated by the observer, owing to a misperception of the importance of randomness.”

    • Survivorship bias
    • Data mining
    • Data snooping
    • Over-fitting
    • Regression to the mean
  • Thought experiment: A population of 10,000 investment managers.

    • Assume a perfectly fair game in which half make $10k and half lose $10k at the end of a year.

    • Managers who lose money are removed from the manager pool.

    • At the end of year 1, we have 5,000 successful managers. At the end of year 2, we have 2,500 successful managers and so on.

    • By year 5 we have 313 successful managers.

    • In the real world, we would view the surviving successful managers as extremely skilled and look to study their success.

    • Important: In most cases we are unaware of the true sample population (in this case, the 10,000 managers).

    • Variation: Rerun the experiment with a cohort of incompetent managers.

      • This time the game is not fair, but skewed towards failure. Regardless, the resulting outcome after 5 years will still yield a group of “winners” from a pool of “losers.”
      • “A population entirely composed of bad managers will produce a small amount of great track records.”
  • The hot hand (basketball) is an example of a random sequence: a large sample of players and one player with an inordinately long streak of luck.

  • “The larger the deviation from the norm, the larger the probability of it coming from luck rather than skills.”

  • Regression to the mean: Reversion for large outliers. Note that when the deviation is disproportionate, the reversion effect will be more potent.

  • Not all deviations are the result of randomness. But luck does account for a large proportion of them.

  • Human nature ascribes luck to failure and skill to individual success.

  • Coincidences aren’t as impressive as we think they are.

    • Example: “The Birthday Paradox.” There is a one in 365.25 chance that you share a birthday with someone you meet randomly. In a room with 23 people, there is a 50% chance that some pair shares the same birthday. In this case, any pair can be matched.
    • Many coincidences lack a priori specificity. That is, the coincidence is based upon any arbitrary connection.
  • Book review blurbs and survivorship bias:

    • We confuse book reviews with the best book reviews.
    • Publishers select only positive comments to print on their books.
    • Would-be readers only see these cherry-picked comments of praise.
  • Data snooping (aka “data dredging”): The misuse of data analysis to find patterns in data that can be presented as statistically significant, thus dramatically increasing and understating the risk of false positives.

  • Scientific research and publications are subject to survivorship bias. Research that is inconclusive or results in no positive results tends not to be published.

    • “There may be great information in the fact that nothing took place.”

Chapter 10: Loser Takes All—On the Nonlinearities of Life

  • “Life is unfair in a nonlinear way.”

    • Nonlinearity: a small additional input yields a disproportionate result.
    • Example: A small advantage can lead to a disproportionate outcome.
    • Example: No advantage, but a small help from randomness can result in huge, asymmetrical outcomes.
  • Path dependence: When decisions presented to people are dependent on previous decisions or experiences from the past.

  • “Our brain is not cut out for nonlinearities. People think that if, say, two variables are causally linked, then a steady input in one variable should always yield a result in the other one...but reality rarely gives us the privilege of a satisfying linear positive progression...”

    • This is one reason for our inability to comprehend rare events.

**Chapter 11: Randomness and Our Mind: We Are Probability Blind

  • We have markedly different perceptions of the two sides of probability. “Consumers consider a 75% fat-free hamburger to be different from a 25% fat one.”

  • Herbert Simon (economist): Known for theories of “bounded rationality” and “satisficing.”

    • Satisficing: A portmanteau of “satisfy” and “suffice.” It means arriving at an adequate result rather than the optimal one.
  • Kahneman and Tversky and behavioral economics opened up the study of irrational decision-making and inefficient markets.

  • Normative vs. positive science:

    • Normative: A prescriptive approach. Considers how things should be.
    • Positive: A descriptive approach. Considers how things actually are.
    • Example: A normative economist might say that someone with $1,000,000 should be happy. But a positive economist might understand that would not be the case for any number of reasons: The person might have started with $5,000,000 or they might compare their $1,000,000 to their peers who have more.
  • System 1 vs. System 2 thinking:

    • System 1: Effortless, automatic, associative, rapid thinking.
    • System 2: Slow, deliberate, deductive, controlled thinking.
  • Taleb also considers the impact of probabilistic thinking and uncertainty in fields like evolutionary biology, psychology and neurobiology.

  • Different shades of probability:

    • Conditional: A measure of the probability of an event occurring, given that another event has already occurred.

    • Unconditional: A measure of probability irrespective of another event.

    • Joint probability: The compounding probability of evidence.

      • Example: The chance of being diagnosed with respiratory tract cancer and being run over by a pink Cadillac in the same year are independently 1/100,000. Together they are 1/10,000,000,000.
      • The probability of a joint event is lower than a single event.
  • “People overvalue their knowledge and underestimate the probability of their being wrong.”

  • We commonly mix up absence of evidence and evidence of absence:

    • Absence of evidence: Evidence not yet found or unavailable. Inconclusive.
    • Evidence of absence: Evidence or observation that something or some effect does not exist. More conclusive.
    • Example: You publish a paper showing no evidence of increases in survival from a specific therapy (absence of evidence, inconclusive). Further research is needed. A Medical journalist erroneously writes that the paper finds evidence that the therapy does not help (evidence of absence, conclusive).
  • Filtering signal from the noise:

    • Significance: Determining if something is meaningful or important.
    • Causality: Determining if there is cause-and-effect. Causality is difficult to determine and often confused with correlation.
    • Confidence level: Determining the reliability of a statistical conclusion.
    • Taleb’s rule of thumb: “Look only at the large percentage changes. Unless something moves by more than its usual daily percentage change, the event is deemed to be noise.”

Part III: Wax in My Ears

  • “The epiphany I had in my career in randomness came when I understood that I was not intelligent enough, not strong enough, to even try to fight my emotions.”

  • Taleb’s recommendation for noise and news: ignore it.

  • Conditional information: “Unless the source of the statement has extremely high qualifications, the statement will be more revealing of the author than the information intended by him.”

    • Wittgenstein’s ruler: “Unless you have confidence in the ruler’s reliability, if you use the ruler to measure a table you may also be using the table to measure the ruler.”
    • Practical implications: An online comment post, a user review. These things say more about the person who wrote them than the thing they are discussing. [Me: moreover, why trust or bother with some random opinion from a source you are unfamiliar with that is ONLY in front of you by virtue of a computer algorithm or mere availability. It’s a kind of indiscriminate consumption that the internet facilitates.]

Chapter 12: Gamblers’ Ticks and Pigeons in a Box

  • “Gamblers are known to develop some behavioral distortions as a result of some pathological association between a betting outcome and some physical move.”

  • B.F. Skinner (psychologist) and his “Skinner Box”:

    • Experiment to study behavior in animals.
    • Skinner created a box that delivered food randomly to birds.
    • The birds developed “dances” in an attempt to influence the delivery of food. [Me: compare this with the concept of ‘cargo cults’ among humans]
  • “We are not made to view things as independent from each other. When viewing two events A and B, it is hard not to assume that A causes B, B causes A, or both cause each other. Our bias is immediately to establish a causal link.”

Chapter 13: Carneades Comes to Rome: On Probability and Skepticism

  • “Probability is not about the odds, but about the belief in the existence of an alternative outcome, cause, or motive.”

  • Skepticism: Nothing can be treated with certainty. Various degrees of probability can be used to draw conclusions.

  • “Beliefs are said to be path dependent if the sequence of ideas is such that the first one dominates.”

  • “We may be programmed to build a loyalty to ideas in which we have invested time.”

  • Flexible thinking and the ability to abandon ideas are rare traits.

    • Consider how often people are publicly pilloried for “flip-flopping” in modern politics.
    • Consider the strong disincentives to not devalue your past work or efforts by denouncing or repudiating them in the present.
  • “People confuse science and scientists. Science is great, but individual scientists are dangerous. They are human; they are marred by biases humans have.”

  • “Science is better than scientists. It was said that science evolves from funeral to funeral.”

Chapter 14: Bacchus Abandons Antony

  • Stoicism as the “attempt by man to get even with probability.”
  • “The stoic is a person who combines the qualities of wisdom, upright dealing, and courage. The Stoic will thus be immune from life’s gyrations as he will be superior to the wounds from some of life’s dirty tricks.”


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