Tag: Deduction and Induction

Fooled By Randomness

fooled by randomness

I don't want you to make the same mistake I did.

I waited too long before reading Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets by Nassim Taleb. He wrote the book before the Black Swan and Antifragile, which propelled him into intellectual celebrity. Interestingly, Fooled by Randomness contains semi-explored gems of the ideas that would later go on to become the best-selling books The Black Swan and Antifragile.

Hindsight Bias

Part of the argument that Fooled by Randomness presents is that when we look back at things that have happened we see them as less random than they actually were.

It is as if there were two planets: the one in which we actually live and the one, considerably more deterministic, on which people are convinced we live. It is as simple as that: Past events will always look less random than they were (it is called the hindsight bias). I would listen to someone’s discussion of his own past realizing that much of what he was saying was just backfit explanations concocted ex post by his deluded mind.

The Courage of Montaigne

Writing on Montaigne as the role model for the modern thinker, Taleb also addresses his courage:

It certainly takes bravery to remain skeptical; it takes inordinate courage to introspect, to confront oneself, to accept one’s limitations— scientists are seeing more and more evidence that we are specifically designed by mother nature to fool ourselves.


Fooled by Randomness is about probability, not in a mathematical way but as skepticism.

In this book probability is principally a branch of applied skepticism, not an engineering discipline. …

Probability is not a mere computation of odds on the dice or more complicated variants; it is the acceptance of the lack of certainty in our knowledge and the development of methods for dealing with our ignorance. Outside of textbooks and casinos, probability almost never presents itself as a mathematical problem or a brain teaser. Mother nature does not tell you how many holes there are on the roulette table , nor does she deliver problems in a textbook way (in the real world one has to guess the problem more than the solution).

Outside of textbooks and casinos, probability almost never presents itself as a mathematical problem” which is fascinating given how we tend to solve problems. In decisions under uncertainty, I discussed how risk and uncertainty are different things, which creates two types of ignorance.

Most decisions are not risk-based, they are uncertainty-based and you either know you are ignorant or you have no idea you are ignorant. There is a big distinction between the two. Trust me, you'd rather know you are ignorant.

Randomness Disguised as Non-Randomness

The core of the book is about luck that we understand as skill or “randomness disguised as non-randomness (that is determinism).”

This problem manifests itself most frequently in the lucky fool, “defined as a person who benefited from a disproportionate share of luck but attributes his success to some other, generally very precise, reason.”

Such confusion crops up in the most unexpected areas, even science, though not in such an accentuated and obvious manner as it does in the world of business. It is endemic in politics, as it can be encountered in the shape of a country’s president discoursing on the jobs that “he” created, “his” recovery, and “his predecessor’s” inflation.

These lucky fools are often fragilistas — they have no idea they are lucky fools. For example:

[W]e often have the mistaken impression that a strategy is an excellent strategy, or an entrepreneur a person endowed with “vision,” or a trader a talented trader, only to realize that 99.9% of their past performance is attributable to chance, and chance alone. Ask a profitable investor to explain the reasons for his success; he will offer some deep and convincing interpretation of the results. Frequently, these delusions are intentional and deserve to bear the name “charlatanism.”

This does not mean that all success is luck or randomness. There is a difference between “it is more random than we think” and “it is all random.”

Let me make it clear here : Of course chance favors the prepared! Hard work, showing up on time, wearing a clean (preferably white) shirt, using deodorant, and some such conventional things contribute to success— they are certainly necessary but may be insufficient as they do not cause success. The same applies to the conventional values of persistence, doggedness and perseverance: necessary, very necessary. One needs to go out and buy a lottery ticket in order to win. Does it mean that the work involved in the trip to the store caused the winning? Of course skills count, but they do count less in highly random environments than they do in dentistry.

No, I am not saying that what your grandmother told you about the value of work ethics is wrong! Furthermore, as most successes are caused by very few “windows of opportunity,” failing to grab one can be deadly for one’s career. Take your luck!

That last paragraph connects to something Charlie Munger once said: “Really good investment opportunities aren't going to come along too often and won't last too long, so you've got to be ready to act. Have a prepared mind.

Taleb thinks of success in terms of degrees, so mild success might be explained by skill and labour but outrageous success “is attributable variance.”

Luck Makes You Fragile

One thing Taleb hits on that really stuck with me is that “that which came with the help of luck could be taken away by luck (and often rapidly and unexpectedly at that). The flipside, which deserves to be considered as well (in fact it is even more of our concern), is that things that come with little help from luck are more resistant to randomness.” How Antifragile.

Taleb argues this is the problem of induction, “it does not matter how frequently something succeeds if failure is too costly to bear.”

Noise and Signal

We are confused between noise and signal.

…the literary mind can be intentionally prone to the confusion between noise and meaning, that is, between a randomly constructed arrangement and a precisely intended message. However, this causes little harm; few claim that art is a tool of investigation of the Truth— rather than an attempt to escape it or make it more palatable. Symbolism is the child of our inability and unwillingness to accept randomness; we give meaning to all manner of shapes; we detect human figures in inkblots.

All my life I have suffered the conflict between my love of literature and poetry and my profound allergy to most teachers of literature and “critics.” The French thinker and poet Paul Valery was surprised to listen to a commentary of his poems that found meanings that had until then escaped him (of course, it was pointed out to him that these were intended by his subconscious).

If we're concerned about situations where randomness is confused with non randomness should we also be concerned with situations where non randomness is mistaken for randomness, which would result in signal being ignored?

First, I am not overly worried about the existence of undetected patterns. We have been reading lengthy and complex messages in just about any manifestation of nature that presents jaggedness (such as the palm of a hand, the residues at the bottom of Turkish coffee cups, etc.). Armed with home supercomputers and chained processors, and helped by complexity and “chaos” theories, the scientists, semiscientists, and pseudoscientists will be able to find portents. Second, we need to take into account the costs of mistakes; in my opinion, mistaking the right column for the left one is not as costly as an error in the opposite direction. Even popular opinion warns that bad information is worse than no information at all.

If you haven't yet, pick up a copy of Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets. Don't make the same mistake I did and wait to read this important book.

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Mental Models: Deduction and Induction

The scientific method is based on logical reasoning. When you draw conclusions that support evidence gathered in an investigation, you are following logic. Two methods of reasoning that are involved in logic are inductive and deductive reasoning.

Inductive reasoning involves drawing a conclusion by moving from specific observations to general ones. Deductive reasoning, on the other hand, involves drawing conclusions by applying a generalization to a specific example.

In the Complete Idiot's Guide to Writing Well, Laurie Rozakis says:

“Inductive reasoning draws a logical conclusion from specific facts. It depends on drawing inferences from particular cases to support a generalization or claim. Many of our everyday conclusions are based on inductive reasoning. For example, if three people whose judgment you respect tell you that a particular movie is worth seeing, you'll conclude that the movie is most likely something you'll enjoy.

Therefore, the success of an essay built inductively depends on the strength of your examples….

Deductive reasoning moves in the opposite direction, from a general premise to particular conclusions. Sometimes it depends on a logical structure called a syllogism. Here is an example:

Major premise: All men are mortal.
Minor premise: Herman is a man.
Conclusion: Therefore, Herman is a mortal.

If you accept the major premise that all men will eventually kick the bucket and the minor premise that Herman is a man, then you have to accept the conclusion. Most written arguments collapse because the major premise isn't true.”

An example of an inductive argument:
5+7=12 and twelve is an even number. Therefore, an odd number added to another odd number will result in an even number.

Inductive reasoning can be strong or weak. A strong induction is thus an argument in which the truth of the premises would make the conclusion probable, but not necessarily guarantee it as being factual. In weak induction the logical means of connecting the premise and conclusion are faulty.