Fooled by randomness

More notes on previous books read this past year. This is another by the author of "The Black Swan" shown is a prior posting:

Nassim Nicholas Taleb, “”Fooled By Randomness,” (The Hidden Role of Chance in Life and in the Markets), 2nd edition, Random House 2005.

pg. 112: “Where statistics becomes complicated and fails us, is when we have distributions that are not symmetric. ...there is a small probability of finding a red ball in an urn dominated by black ones; our knowledge about the absence of red balls will increase very slowly—more slowly than at the expected square root of n rate. On the other hand, our knowledge of the presence of red balls will dramatically improve once one of them is found. This asymmetry in knowledge is not trivial; it is central to this book—it is a central philosophical problem for people such as Hume.

Pg. 117: “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.” as posed by John Stuart Mill rephrasing David Humes original problem.

Pg. 12: “Mild success can be explainable by skills and labor. Wild success is attributable to variance.”

Pg. 20: “Arguably, in expectation, a dentist is considerably richer than the rock musician who is driven in a pink Rolls Royce, the speculator who bids up the price of impressionist paintings, or the entrepreneur who collects private jets. For one cannot consider a profession without taking into account the average of the people who enter it, not the sample of those who have succeeded in it. This point can also be considered from the survivorship bias point of view. Also resistance to randomness: someone who wins the lottery will be unlikely to repeat this feat.

Pg. 23: 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). Such substitute courses of events are called alternative histories. Clearly, the quality of a decision cannot be solely judged based on its outcome, but such a point seems to be voiced only by people who fail (those who succeed attribute their success to the quality of their decision.) Such opinion—‘that I followed the best course’—is what politicians on their way out of office keep telling those members of the press who still listen to them—eliciting the customary commiserating ‘yes, we know’.

pg. 26: “Reality is far more vicious than Russian roulette. First, it delivers the fatal bullet rather infrequently, like a revolver that would have hundreds, even thousands, of chambers instead of six. After a few dozen tries, one forgets about the existence of a bullet, under a numbing false sense of security. The point is dubbed in this book the black swan problem.”

pg. 54: “I have noticed plenty of analogies between those who blew up in the stock market crash of 1987, those who blew up in the Japan meltdown of 1990, those who blew up in the bond market debacle of 1994, those who blew up in Russia in 1998, and those who blew up shorting Nasdaq stocks. They all made claims to the effect that ‘these times are different’ or that ‘their market was different,’ and offered seemingly well-constructed, intellectual arguments of an economic nature to justify their claims.”

pg. 56: “When you look at the past, the past will always be deterministic, since only one single observation took place. Our mind will interpret most events not with the preceding ones in mind, but the following ones. Our minds are not quite designed to understand how the world works, but, rather, to get out of trouble rapidly and have progeny.

pg. 86: “...at any point in time, the richest traders are often the worst traders.”
Pg. 94: “...Darwinian ideas are about reproductive fitness, not about survival.”

Pg. 97-99: The Median is Not the Message.
Assume I engage in a gambling strategy that has 999 chances in 1,000 of making $1 (event A) and 1 chance in 1,000 of losing $10,000 (event B). My expectation is a loss of close to $9 (obtained by multiplying the probabilities by the corresponding outcomes). The frequency or probability of the loss, in and by itself, it totally irrelevant; it needs to be judged in connection with the magnitude of the outcome. Here A is far more likely than B. Odds are that we would make money by betting for event A, but it is not a good idea to do so.

Event Probability Outcome Expectation
A 999/1000 $1 $0.999
B 1/1000 -$10,000 -$10
Total -$9.001
It is this same logic that makes the question of whether you are bearish or bullish on the market non-sensical especially if being bullish means you intend to buy. You may be bullish but the expectations lead you to SHORT the market as the magnitude may so lead you.

Pg. 126: There are only two types of theories:
1. Theories that are known to be wrong, as they were tested and adequately rejected.
2. Theories that have not yet been known to be wrong but are exposed to be proved wrong.
Why is a theory never right? Because we will never 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. A theory that does not present a set of conditions under which it would be considered wrong would be termed charlatanism—it would be impossible to reject otherwise.

Pg. 136: “I do not deny that if someone performed better than the crowd in the past, there is a presumption of his ability to do better in the future. But the presumption might be weak to the point of being useless in decision making. Why? Because it all depends on two factors: The randomness content of his profession and the number of monkeys in operation.” The greater number of businessmen, the greater the likelihood of one of them performing in a stellar manner just by luck.
There are other aspects to the monkey's problem; in real life, the other monkeys are not countable, let alone visible. They are hidden away, as one sees only the winners.

pg 137: ...a synthesis of the biases of randomness as discussed in the now abundant literature on the subject. These biases can be outlined as follows: (a) The survivorship biases (a.k.a. monkeys on a typewriter) arising from the fact that we see only winners and get a distorted view of the odds, (b) the fact that luck is most frequently the reason for extreme success, and (c) the biological handicap of our inability to understand probability.

Pg. 147: “A guru computed the success of a ‘Robin Hood’ policy of investing with the least successful manager in a given population of managers. It consists in switching down by taking money away from the winner mgr and allocating it to the loser money mgr. Doing so, their ‘paper strategy’ (they used past history, not real investments) derived considerably higher returns than if they stuck to the winning manager. Their analysis presents a problem. Their sample only had survivors. They simply forgot to take into account the mgrs who went out of business. True, their sample included managers who did poorly but only those managers who did poorly and recovered, without getting out of business.

Pg. 156: “Recall that the survivorship bias depends on the size of the initial population. The information that a person derived some profits in the past, just by itself is irrelevant. We need to know the size of the population from which he came. If the initial population includes only ten managers, then I would give the performer half my savings without a blink. If the initial population is composed of 10,000 managers, I would ignore the results.

Pg. 188: “Kahneman and Tversky started figuring out rules in humans that did not make them rational—but shortcuts humans take; rules which are called heuristics. Since the Kahneman and Tversky results, an entire discipline called behavioral finance and economics has flourished. It is in open contradiction with the orthodox so-called neoclassical economics taught in business schools and economics departments under the normative names of efficient markets, rational expectations, and other such concepts. A normative science (clearly a self-contradictory concept) offers prescriptive teachings; it studies how things should be. Some economists, for example, those of the efficient-market religion, believe that our studies should be based on the hypothesis that humans are rational and act rationally because it is the best thing for them to do (it is mathematically ‘optimal’). The opposite is a positive science, which is based on how people actually are observed to behave. Physics is an inherently positive science while economics, particularly microeconomics and financial economics, is predominantly a normative one. Normative economics is like religion without the aesthetics.”

Pg. 202: You perform a surgical ablation on a piece of someone’s brain with the sole resulting effect of an inability to register emotions, nothing else (the IQ and every other faculty remain the same). What you have done is a controlled experiment to separate someone’s intelligence from his emotions. Now you have a purely rational human being unencumbered with feelings and emotions. Let’s watch: Damasio reported that the purely unemotional man was incapable of making the simplest decision. He could not get out of bed in the morning and frittered away his days fruitlessly weighing decisions. Shock! This flies in the face of everything one would have expected. One cannot make a decision without emotion. Without emotions, we would not make a decision. But emotions are susceptible to biases.

Pg. 206: Example of biases in Understanding Probability. The following famous quiz was given to medical doctors:
A test of a disease presents a rate of 5% false positives. The disease strikes 1/1,000 of the population. People are tested at random, regardless of whether they are suspected of having the disease. A patient’s test is positive. What is the probability of the patient being stricken with the disease?
Most doctors answered 95%, simply taking into account the fact that the test has a 95% accuracy rate. The answer is the conditional probability that the patient is sick and the test shows it—close to 2%. Less than one in five professionals got it right.

I will simplify the answer (using the frequency approach). Assume no false negatives. Consider that out of 1,000 patients who are administered the test, one will be expected to be afflicted with the disease. Out of a population of the remaining 999 healthy patients, the test will erroneously identify about 50 with the disease (it is 95% accurate). The correct answer should be that the probability of being afflicted with the disease for someone selected at random who presented a positive test is the following ratio:
Number of afflicted persons
Number of true and false positives
here 1 in 51 or 2%
Think of the number of times you will be given a medication that carries damaging side effects for a given disease you were told you had when you many only have a 2% probability of being afflicted with it!

Note: Karl Popper had a great influence on Taleb’s thinking.