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@@ -150,6 +150,257 @@ For commentary see the companion
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#### A Note About Black Swans
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The *exsupero ursus* fallacy is reinforced by authors of very popular books
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who seem to depend heavily on some version of the fallacy.
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One such author is former Wall Street trader and mathematician Nassim Taleb.
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He wrote *The Black Swan* and other books critical of common practice in risk management,
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especially in (but not limited to) the financial world,
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as well as the nonquantitative hubris of Wall Street.
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A heretic of financial convention, he argues that Nobel Prize---
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winning modern portfolio theory and options theory (briefly mentioned in chapter 5)
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are fundamentally flawed and are in fact no better than astrology.
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In fact, Taleb considers this prize is itself an intellectual fraud.
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After all, as he rightly points out, it was not established in the will of Alfred Nobel,
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but by the Royal Bank of Sweden seventy-five years after Nobel's death.
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He even claims that once, in a public forum, he riled up one such prizewinner
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to the point of red-faced, fist-pounding anger.
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Taleb bases a lot of his thesis on the fact that the impact of chance
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is unappreciated by mostly everyone.
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He sees the most significant events in history as being completely unforeseeable.
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He calls these events *black swans* in reference to an old European expression
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that went something like "That's about as likely as finding a black swan."
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The expression was based on the fact that no European
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had ever seen a swan that was black---until Europeans traveled to Australia.
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Until the first black swans were sighted, black swans were a metaphor for impossibility.
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Taleb puts September 11, 2001, stock market crashes, major scientific discoveries,
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and the rise of Google in his set of black swans.
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Each event, he argues, was not only unforeseen
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but *utterly unforeseeable* based on our previous experience.
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People will routinely confuse luck with competence
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and they will presume that the lack of seeing an unusual event to date
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is somehow proof that the event cannot occur.
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Managers, traders, and the media seem to be especially susceptible to these errors.
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Out of a large number of managers,
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some managers will have made several good choices in a row by chance alone.
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This is what I called the Red Baron effect in a previous chapter.
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Such managers will see their past success
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as indicators of competence and, unfortunately,
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will act with high confidence on equally erroneous thinking in the future.
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Taleb recognizes the problems of overconfidence researched by Kahneman and others.
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Indeed, Taleb says Kahneman is the only Economics Nobel Prize winner he respects.
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I think part of Taleb's skepticism is refreshing and on point.
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I agree with many of Taleb's observations on the misplaced faith in some models
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and will discuss this further in the next chapter.
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I might even include Taleb as one source of inspiration
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for identifying new categories of fallacies
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(and giving it a Latin name in order to sound official).
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Taleb coined a fallacy he refers to as the *ludic fallacy*,
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derived from the Latin word for "games of chance."
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Taleb defines the ludic fallacy as the assumption that the real world
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necessarily follows the same rules as well-defined games of chance.
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Now, here is where Taleb errs.
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He doesn't just argue that risk management is flawed.
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He argues that risk management itself is impossible
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and that all we can do is make ourselves *antifragile*.
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I think he is just using a very different definition of risk management---
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which even he uses inconsistently.
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No matter what he calls it, he is promoting a particular set of (vaguely defined) methods
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that have the objective of reducing risk.
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This reduction in risk will require resources.
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Using the definition I propose in [chapter 6],
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determining how to use resources to reduce risk is part of risk management.
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He actually contradicts himself on this point
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when he promotes redundancy as a method of becoming antifragile
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and refers to it as the "central risk management property of natural systems."
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So, yes, we are both talking about risk management.
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He focuses on particular approaches to it, but it is risk management just the same.
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Confusion and inconsistency about whether managing fragility is, in practice,
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part of managing risks is not the only problem in his thesis.
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Taleb commits every form of the *exsupero ursus* fallacy
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throughout most of what he writes.
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Specifically,
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(1) he presumes the lack of perfection of one model
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automatically necessitates use of the other regardless of relative performance,
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(2) he commits the anecdotal fallacy
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when looking for evidence of relative performance, and
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(3) he presumes that a given model was even being used
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when he identifies them as the culprit in major risk events.
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In an interview for *Fortune* Taleb claimed,
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"No model is better than a faulty model."
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Again, having no model is never an option.
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One way or another, a model is being used.
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Taleb's model is simply his common sense,
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which is, as Albert Einstein defines it,
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"merely the deposit of prejudice laid down in the human mind before the age of eighteen."
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As with every other model, common sense has its own special errors.
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We've seen the research that shows overwhelming evidence
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of the flaws of unaided intuition compared to even simple statistical models,
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and Taleb offers no empirical data to the contrary.
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Taleb does briefly mention the work of Meehl but dismisses it.
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Without making any mention of the huge numbers
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of conclusive results by Meehl and his colleagues,
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Taleb claims the entire body of research is invalid
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by claiming "that these researchers did not have a clear idea
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of where the burden of empirical evidence lies"
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and goes on to suggest that they lacked "rigorous empiricism."
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He offers no details about how more than one hundred peer-reviewed,
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published studies by several researchers veers from the required rigorous empiricism.
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Kahneman, who actually is a psychologist like Meehl,
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would apparently disagree with Taleb on Meehl's methods.
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Taleb considers Kahneman a significant influence on his work,
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but who does Kahneman consider to be a significant influence on his work?
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<u>Meehl</u>.
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I wouldn't presume to speak for Kahneman
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but I wonder if he might point out to Taleb
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how the burden of proof was accepted and met overwhelmingly by Meehl,
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whereas Taleb's evidence merely amounts to, at best,
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selected anecdotes of shortcomings or entirely imagined straw man arguments.
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Taleb even sometimes cites the work of Phil Tetlock
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to support some other point he makes
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but never references Tetlock's enormous twenty-year study
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where he concluded that it was "impossible"
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to find a domain where humans clearly outperformed algorithms.
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Instead of relying on large controlled studies,
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Taleb commits the error of arguing that single events
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effectively disprove a probabilistic model.
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He uses the apparent unforeseeability of specific events
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as evidence of a flaw in risk analysis.
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The implication is that if quantitative analysis worked,
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then we could make exact predictions of specific and extraordinary events
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such as 9/11 or the rise of Google.
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When arguing against the use of various statistical models in economics
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he states that "the simple argument that Black Swans and tail events
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run the socioeconomic world---and these events cannot be predicted---
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is sufficient to invalidate their statistics."[^09-12]
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Yes, the rare events---black swans---
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are individually impossible to predict precisely.
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But unless he can show that his alternative model (apparently his intuition)
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would also have predicted such events exactly,
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then he commits *exsupero ursus* when he says imperfection alone
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is sufficient to prefer intuition over statistics.
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In addition to Kahneman,
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it is worth pointing out others whose work Taleb cites to make a point but who,
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if you actually looked at what they are doing, would contradict Taleb.
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For example, Taleb says he admires the mathematician Edward Thorp,
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who developed a mathematically sound basis for card counting in blackjack in the 1960s.
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Now, if the objective of card counting was to predict every hand,
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even the most extraordinarily rare combinations as Taleb would seem to require,
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then Ed Thorp's method certainly fails.
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But Ed Thorp's method works---that's why the casinos quit letting him play---
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because his system resulted in better bets on average after a large number of hands.
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Taleb is also a fan of the mathematician Benoit Mandelbrot,
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who used the mathematics of *fractals* to model financial markets.
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Similar to Thorp and Taleb,
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Mandelbrot was equally unable to predict specific extraordinary events exactly,
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but his models are preferred by some
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because they seem to generate more realistic patterns
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that look like they *could* be from real data.
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If anecdotal evidence were sufficient to compare model performance,
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one could simply point out that Taleb's investment firm, Empirica Capital LLC,
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closed in 2004 after several years of mediocre returns.[^09-13]
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He had one very good year in 2000 (a 60 percent return)
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because while everyone else was betting on dot-com, he bet on *dot-bomb*.
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But the returns the following years were far enough below the market average
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that the good times couldn't outweigh the bad for his fund.
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Similar to the news pundits rejecting Nate Silver's findings
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or the sportscasters rejecting the methods used by the Oakland A's,
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Taleb merely shows that it is possible to find an error in a model
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if one looks hard enough.
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Again, the question is not whether to model (intuition is a model, too)
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or whether one model is imperfect (both models are imperfect)
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but which measurably outperforms the other
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and does so in many trials not just single anecdotes.
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Finally, Taleb makes the error of presuming what methods
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were actually being used when he blames them for an event.
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He argues, for example,
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that the downfall of long-term capital management (LTCM) disproves options theory.
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Recall that options theory won the Nobel Prize for Robert Merton and Myron Scholes,
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both of whom were on the board of directors for LTCM.
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The theory was presumably the basis of the trading strategy of the firm.
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But an analysis of the failure of LTCM shows that a big reason for its downfall
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was the excessive use of leverage in trades---an issue that isn't even part of options theory.
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That appeared to be based on intuition.
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Taleb also states that the crash of 1987 disproved modern portfolio theory (MPT),
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which would seem to presume
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that at least some significant proportion of fund managers used the method.
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I find fund managers to be tight-lipped about their specific methods,
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but one fund manager did tell me how
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"learning the theory is important as a foundation
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but 'real-world' decisions have to be based on practical experience, too."
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In fact, I found no fund managers who didn't rely partly, if not mostly, on intuition.
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Finally, if we are looking for explanations of the mortgage crisis,
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neither MPT nor options theory had anything to do with the practice
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of giving out mortgages to large numbers of people lacking the ability to pay them.
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That was more of a function of a system
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that incentivized banks to give risky loans without actually accepting the risk.
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Finally, Taleb seems to make a variety of other points
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that, similar to the previous points, seem so inconsistent
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he ends up undermining the point he makes.
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For example, explaining the outcomes
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in terms of the narrative fallacy committed by others
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is sometimes itself a narrative fallacy.
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Arguing that "experts" don't know so much is not supported by quoting other experts.
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He argues that rare events defy quantitative models,
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but then gives specific examples of computing rare events with quantitative models
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(he shows the odds of getting the same result in a coin flip many times in a row
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and argues the benefits of Mandelbrot's mathematical models
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in the analysis of market fluctuations).
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Taleb criticizes the use of historical data in forecasts
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but apparently sees no irony in his argument.
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He looks at several examples in which history was a poor predictor.
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In other words, he is assessing the validity of using historical examples
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by using *historical examples*.
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What Taleb and others prove with such examples
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is merely that what I will call a *naive* historical analysis can be very misleading.
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Taleb demonstrates his point by using the example of a turkey.
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The turkey had a great life right up until Thanksgiving.
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So, for that turkey, history was a poor indicator.
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So how is Taleb able to see this problem?
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He simply looks at the larger history of turkeys.
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All he is doing is using what we may call a *history of histories*,
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or *meta-historical analysis*, to show how wrong naive historical analysis can be.
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The error in historical analysis in a stock price, for example,
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is to look only at the history of *that* stock and only for recent history.
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If we look at all historical analysis for a very long period of time,
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we find how often naive historical analysis can be wrong.
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Taleb's own "experience," as extensive as it might be (at least in finance),
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is also just a historical analysis---just a very informal type
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with lots of errors in both recall and analysis, as shown in [chapter 7].
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No thinking person can ever honestly claim
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to have formed any idea totally independent of previous observations.
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It just doesn't happen.
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Even Taleb's ludic fallacy seems to be a fallacy itself.
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Sam Savage calls it the "ludic fallacy-fallacy."
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As Savage describes it, we cannot rationally address real-world problems of uncertainty
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"*without* first understanding the simple arithmetic of dice, cards, and spinners."
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Of course, Taleb is right when he says we shouldn't *assume*
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that we have defined any problem perfectly.
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That certainly would be an error, and if that were Taleb's point, that would be valid.
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But, again, whether a particular model is perfect is not the right question.
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The most relevant question is whether a probabilistic model---even a simple one---
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outperforms the alternative model, such as intuition.
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#### Major Mathematical Misconceptions
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#### We're Special: The Belief That Risk Analysis Might Work, But Not Here
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@@ -182,6 +433,14 @@ For commentary see the companion
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#### Using Data For Initial Benchmarks
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##### It's Been Measured Before
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##### You Have More Data Than You Think
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##### You Need Less Data Than You Think
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##### A Reference Class Error: Revisiting the Turkey
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#### Checking The Substitution
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#### Simple Risk Management
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