Not so Fooled by Randomness

“Why do I want everybody to learn some statistics?” (p.215)

After reading and commenting on The Black Swan, I decided to spend the lavish stipend provided by my Associate gains on Amazon on Fooled by Randomness by Nassim Nicolas Taleb, in connection with another positive review by Andrew Gelman. Obviously, after being put off by The Black Swan, I started Fooled by Randomness with a strong bias, not helped by the fact that the book is written in almost exactly the same infuriating style, with endless repetitions and ceaseless discursions. From the prologue, “this book has two purposes: to defend science (…) and to attack the scientist when he strays from his course” which sounds fairly ambitious a priori and not achieved a posteriori. The style is however both less egocentric than The Black Swan and more scientific in that those “fooled by randomness” mostly are those not accounting for randomness, rather than those using (inadequate) random models as in The Black Swan. I thus had less of a hard time reading it in the metro (took me less than a week!), despite the author’s aggravating style using small facts, key figures and personal introspections to advance one’s theory. As in The Black Swan, Karl Popper once again pops in even before page one! And the baseball coach Yogi Berra familiar to readers of The Black Swan is not far behind. The overlap with The Black Swan is far from negligible, the notion of black swans is already repeated in Fooled by Randomness many times, with the initial metaphor about the unpredictable black swan attributed to Hume, as are trader stories and barbs at econometricians, Nobel Prize winners, bell curves, financial experts and most economists (but much less at the bell curve, Frenchmen, textbooks and statisticians).

“These “thinkers” should be given an undergraduate class on statistical sampling…” (p. 75)

As written above, the main difference with The Black Swan is however that the tone is not antagonistic towards probability theory, quite the opposite, and there is even a chapter that is an ode to Monte Carlo mathematics(!). I am not convinced this Monte Carlo chapter makes much sense to anyone who has never heard of Monte Carlo computer simulation, though, because there hardly is any mention of an underlying model driving the simulation. (Simulating Russian roulette on a computer does not sound much appealing either to the lambda reader.) Part I also contains sensible warnings against the survivor bias and similar issues. The warnings about apparent coincidences and other apparently unlikely events are correctly argumented (including a reference to Persi Diaconis) and are the central theme of the book, finding patterns where there is none. Another improvement [when compared with The Black Swan] is that non-stationarity and regime switch are explicitely recognised as a cause for poor prediction. There is nonetheless an underlying argument that statistical inference (learning from experience) is in essence impossible on most real phenomema, because there is basically no way to check (or “to falsify” in Popper’s lingua) that your model is completely correct, especially in the tails. While this is done in a most obscure way, there even are favourable references to sujective probabilities, to priors and not so obscure to Keynes’ A Treatise On Probability. (Made me think of using this book for an historical reading course next year!)

In conclusion, and in my opinion, the author should not have written The Black Swan after this mostly reasonable (if highly repetitive) book! The excessively aggressive tone adopted against modelling in The Black Swan makes Fooled by Randomness appear almost like its opposite at times. (I will discuss in a later post some minor points of contention I have with the book.)

One Response to “Not so Fooled by Randomness”

  1. […] or a Bayesian sense) is enough to conclude about the falsity of a theory (calling, guess who?!, on Popper himself!). But, even in a scientific perspective, the rejection of an hypothesis must be followed […]

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