Perhaps the main point is that what we often attribute to skill is really due to randomness. Sometimes, as with dentists or pianists, skill is required for success. However, in business, and particularly in finance, one can be successful simply because one’s business or trading temperament match the conditions of the market. Given the large number of traders, the probability of one of them being consistently successful over a multiple year period due to pure luck is very high. However, if their success is based on luck, sooner or later luck will do them in. As a long-term market trader, Taleb has gone through a large number of colleagues. Inevitably, the (many) stories of his colleagues end with the phrase “they blew up,” meaning that they lost substantially more money over the course of a few days (sometimes sooner) than the total sum of the money they had made over the course of the previous years.
Invariably, Taleb gives two reasons for the blow-ups. The first is that the traders thought their success was due to their system, whether it was an economic approach or an algorithmic approach; in reality, they just happened to be in the right place at the right time with the right skills and temperament. The second is that they failed to prepare for the case in which their system failed, because they did not believe their system could fail. If the trader lacked the brains to understand what they were doing, then they tended to blow up because of too much leverage (due to overconfidence in their supposed skills). On the other hand, if the trader trusted too much in brains, they followed their philosophy when the market conditions no longer matched the assumptions of the philosophy. By contrast, the few successful traders were less successful in the good times, but they constantly protected themselves. In fact, Taleb even specializes in making money off the rare, so-called “black swan” events: he tends to lose a little bit on most of his trades but makes a large amount on a few trades.
Taleb observes that most of our training in probability is with symmetrical distributions like the normal distribution. In these, the downside is usually equal (but opposite) to the upside. Often, however, this is not the case. In finance, in particular, the upside may be small but the downside very large (or vice-versa). So instead of looking at the probability of something happening, it is better to look at the expectation value, which will include the asymmetry. This idea of handling “black swan” events in one’s system of trading is one of the main themes.
Another theme is that people are not designed to correctly deal with probability: even the experts in probability make glaring errors. It appears that our emotions interfere with the rationality required for correct probabilistic thinking. These are necessary to allow us to decide between equally valued options: if the philosopher’s donkey had been randomly nearer either the hay or the water, the deadlock would have been broken. But in finance, we may have to trick ourselves, for instance, by not looking at our results very frequently, since losses are emotionally worse than the emotional high from an equal gain.
Our propensity for incorrect probabilistic thinking manifests itself in a few common ways. One major way is through the fallacy of thinking that repeated observations say something meaningful about the probability distribution. Repeated observations will typically not encounter the rare events, so if these are of substantial importance, observation does not yield useful information. Likewise, if the probability distribution is changing (as it does in finance, where everyone else in the market is constantly adjusting their strategy to exploit current anomalies), the previously collected information is no longer relevant.
Another major cause of incorrect thinking is due to success-bias. Often we attempt to derive causes for success from the successful population (the successes did X). This is meaningless unless we also include the failure population. The fact that the successful traders did X is not meaningful if there were a large number of failures who also did X. Likewise, if a medical diagnosis is 95% correct, the probability of having the disease if diagnosed with it is not 95%, it is 1/(50 incorrect + 1 correct) = 1/51 ~= 2%.
A final theme of the book is the author’s contempt for non-intellectual thinking. This is leveled most frequently against journalists, whom he claims are purveying entertainment but think they are providing information. However anyone without an emphasis on brains gets a dose: MBAs, overconfident financial traders, and the general public. While he has some good points, the arrogance practically drips off the page. I have held similar attitudes in the past (and to some extent, still in the present), but this attitude does not promote healthy relationships with other people. In fact, I get the feeling that, given enough time, the author will withdraw from the world, kind of like the citizens of C.S. Lewis’ fictional Hell in The Great Divorce, who are so full of themselves that they keep moving away from the other citizens because they cannot stand their company.
Fooled By Randomness is easy to read, due to the conversational style. Unfortunately, the lack of strong structure inhibits a context for the ideas, and the excessive parenthetical comments are rather distracting, as are the interesting but tangential ideas. However, the ideas are expressed simply but adequately in a way that is easy to comprehend. While this is not a 100 year book, it is a good primer in practical statistics, and the repeated lessons about not “blowing up” a caution to future investors.
The ideas are very good, but really need to be presented in a nicer way. The arrogance, while understandable, is tiring. The parenthetical comments are excessive and distracting; I have found that if I am tempted to put more than one parenthetical comment in a paragraph, I am well served to remove or rephrase it. Finally, the ideas are excellent, and could be worked into a 100 year book with some actual discipline, such as the editors whose advice he ignores.