The Black Swan: The Impact of the Highly Improbable (Incerto, #2)

by Nassim Nicholas Taleb

destiny. I have taught myself to resist running to keep on schedule. This may seem a very small piece of advice, but it registered. In refusing to run to catch trains, I have felt the true value of elegance and aesthetics in behavior, a sense of being in control of my time, my schedule, and my life.

learning. Historia, just the recording of facts, was inferior to philosophia or scientia. Even philosophy, until then, had more to do with decision-making wisdom than it does today, not with impressing a tenure committee, and

You can test a child for underdevelopment of the theory of mind using a variant of the “false-belief task.” Two children are introduced. One child puts a toy under the bed and leaves the room. During his absence, the second child—the subject—removes it and hides it in a box. You ask the subject: Where, upon returning to the room, will the other child look for the toy? Those under, say, the age of four (when the theory of mind starts developing), choose the box, while older children correctly say that the other child will look under the bed. At around that age, children start realizing that another person can be deprived of some of the information they have, and can hold beliefs that are different from their own.

Subjective probability was formulated by Frank Plumpton Ramsey in 1925 and Bruno de Finetti in 1937. The take on probability by these two intellectual giants is that it can be represented as a quantification of the degree of belief (you set a number between 0 and 1 that corresponds to the strength of your belief in the occurrence of a given event), subjective to the observer, who expresses it as rationally as he wishes under some constraints. These constraints of consistency in decision making are obvious: you cannot bet there is a 60 percent chance of snow tomorrow and a 50 percent chance that there will be no snow. The agent needs to avoid violating something called the Dutch book constraint: that is, you cannot express your probabilities inconsistently by engaging in a series of bets that lock in a certain loss, for example, by acting as if the probabilities of separable contingencies can add up to more than 100 percent.

Furthermore, to be more aggressive, while limits like those attributed to Gödel bear massive philosophical consequences, but we can’t do much about them, I believe that the limits to empirical and statistical knowledge I have shown have sensible (if not vital) importance and we can do a lot with them in terms of solutions, by categorizing decisions based on the severity of the potential estimation error of the pair probability times consequence. For instance, we can use it to build a safer society—to robustify what lies in the Fourth Quadrant.

We thus need a prior model representation for that; the rarer the event, the higher the error in estimation from standard inductive methods (say, frequency sampling from counting past occurrences), hence the higher the dependence on an a priori representation that extrapolates into the space of low-probability events (which necessarily are not seen often).*

the more severe the consequences (just consider that the hundred-year flood is more severe, and less frequent, than the ten-year flood; the bestseller of the decade ships more copies than the bestseller of the year), our estimation of the contribution of the rare event is going to be massively faulty (contribution is probability times effect; multiply that by estimation error); and nothing can remedy it.*

in real life we do not observe probability distributions. We just observe events.

This point—that things have a bias to appear more stable and less risky in the past, leading us to surprises—needs to be taken seriously, particularly in the medical field. The history of epidemics, narrowly studied, does not suggest the risks of the great plague to come that will dominate the planet.

Implication: the need to avoid exposure to small probabilities in a certain domain. We simply cannot compute them.

Accordingly, Ferguson’s methodology (mentioned in Chapter 1) in looking at the prediction of events as expressed in the price of war bonds is sounder than simply counting predictions, because a bond, reflecting the costs to the governments involved in a war, is priced to cover the probability of an event times its consequences, not just the probability of an event. So we should not focus on whether someone “predicted” an event without his statement having consequences attached to it.

My experience of stress testing is that it reveals little about the risks—but the risks can be used to assess the degree of model error.

It is much more sound to take risks you can measure than to measure the risks you are taking.

risk. Numerous experiments provide evidence that professionals are significantly influenced by numbers that they know to be irrelevant to their decision, like writing down the last four digits of one’s social security number before making a numerical estimate of potential market moves. German judges, very respectable people, who rolled dice before sentencing issued sentences 50 percent longer when the dice showed a high number, without being conscious of it.

How do you live long? By avoiding death. Yet people do not realize that success consists mainly in avoiding losses, not in trying to derive profits.

Redundancy (in terms of having savings and cash under the mattress) is the opposite of debt. Psychologists tell us that getting rich does not bring happiness—if you spend your savings. But if you hide it under the mattress, you are less vulnerable to a Black Swan. Also, for example, one can buy insurance, or construct it, to robustify a portfolio.

There are suckers’ methods called “scenario analysis” and “stress testing”—usually based on the past (or on some “make sense” theory). Yet (I showed earlier how) past shortfalls do not predict subsequent shortfalls, so we do not know what exactly to stress-test for. Likewise, “prediction markets” do not function here, since bets do not protect an open-ended exposure. They might work for a binary election, but not in the Fourth Quadrant.

Life expectancy of humans is not as long as we suspect (under globalization) because the data are missing something central: the big epidemic (which far outweighs the gains from cures). The same, as we saw, with the return on risky investments.

Conventional metrics using volatility as an indicator of stability fool us, because the evolution into Extremistan is marked by a lowering of volatility—and a greater risk of big jumps. This has fooled a chairman of the Federal Reserve called Ben Bernanke—as well as the entire banking system. It will fool again.

a method that consists of taking both a defensive attitude and an excessively aggressive one at the same time, by protecting assets from all sources of uncertainty while allocating a small portion for high-risk strategies.