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

by Nassim Nicholas Taleb

There are other notions that have little or no significance outside of the Gaussian: correlation and, worse, regression. Yet they are deeply ingrained in our methods; it is hard to have a business conversation without hearing the word correlation.

consider the roster of his contemporaries: Saint-Simon (1760–1825), Pierre-Joseph Proudhon (1809–1865), and Karl Marx (1818–1883), each the source of a different version of socialism. Everyone in this post-Enlightenment moment was longing for the aurea mediocritas, the golden mean: in wealth, height, weight, and so on. This longing contains some element of wishful thinking mixed with a great deal of harmony and … Platonicity.

Assuming there is something desirable in

being an average man, he must have an unspecified specialty in which he would be more gifted than other people—he cannot be average in everything. A pianist would be better on average at playing the piano, but worse than the norm at, say, horseback riding. A

The notion of a man deemed average is different from that of a man who is averag...

In fact, the dream of a society with compressed outcomes is assumed to correspond to the aspirations of a rational human being facing a genetic lottery.

If you’re dealing with qualitative inference, such as in psychology or medicine, looking for yes/no answers to which magnitudes don’t apply, then you can assume you’re in Mediocristan without serious problems. The impact of the improbable cannot be too large. You have cancer or you don’t, you are pregnant or you are not, et cetera. Degrees of deadness or pregnancy are not relevant

standard deviation do not understand this point. Standard deviation is just a number that you scale things to, a matter of mere correspondence if phenomena were Gaussian.

As the reader can see, the main point of the Gaussian bell curve is, as I have been saying, that most observations hover around the mediocre, the mean, while the odds of a deviation decline faster and faster (exponentially) as you move away from the mean.

Recall our discussions in Chapter 14 on preferential attachment and cumulative advantage. Both theories assert that winning today makes you more likely to win in the future.

you cannot use one single measure for randomness called standard deviation (and call it “risk”); you cannot expect a simple answer to characterize uncertainty.

The great book of Nature lies ever open before our eyes and the true philosophy is written in it. … But we cannot read it unless we have first learned the language and the characters in which it is written. … It is written in mathematical language and the characters are triangles, circles and other geometric figures.

the fractal has numerical or statistical measures that are (somewhat) preserved across scales—the

you can always produce data “corroborating” that the underlying process is Gaussian by finding periods that do not have rare events,

just like you can find an afternoon during which no one killed anyone and use it as “evidence” of honest behavior.

have no precise idea what the exponent is because we cannot measure it directly. All we do is estimate from past data or rely on theories

Mark Buchanan’s Ubiquity, Philip Ball’s Critical Mass, and Paul Ormerod’s Why Most Things Fail. These

In the absence of a feedback process you look at models and think that they confirm reality.

“percolation models,” which address not the behavior of the individual, but rather the terrain in which he operates.

This tells me the following: I can make inferences about things that I do not see in my data, but these things should still belong to the realm of possibilities.

we handle matters that belong to Extremistan, but treated as if they belonged to Mediocristan, as an “approximation.”

considerably easier to understand than the conventional statistics. If you come fresh to the business, do not rely on the old theoretical tools, and do not have a high expectation of certainty.

it is contagion that determines the fate of a theory in social science, not its validity.

Locke’s definition of a madman: someone “reasoning correctly from erroneous premises.”

driven former barbers who gained clinical experience,

At their core, all theories built around the ludic fallacy ignore a layer of uncertainty. Worse, their proponents do not know

People can’t predict how long they will be happy with recently acquired objects, how long their marriages will last, how their new jobs will turn out, yet it’s subatomic particles that they cite as “limits of prediction.” They’re ignoring a mammoth standing in front of them in favor of matter even a microscope would not allow them to see.

I hope I’ve sufficiently drilled home the notion that, as a practitioner, my thinking is rooted in the belief that you cannot go from books to problems, but the reverse, from problems to books.

A scholar should not be a library’s tool for making another library, as in the joke by Daniel Dennett.

I am most often irritated by those who attack the bishop but somehow fall for the securities analyst—those who exercise their skepticism against religion but not against economists, social scientists, and phony statisticians.

Having plenty of data will not provide confirmation, but a single instance can disconfirm.

In finance, for instance, people use flimsy theories to manage their risks and put wild ideas under “rational” scrutiny.

Our tendency is to be very rational, except when it comes to the Black Swan.

Missing a train is only painful if you run after it!

Likewise, not matching the idea of success others expect from you is only painful if that’s what you are seeking.

I explained in Chapter 6, with the story of the old elephants, that the best teachers of wisdom are naturally the eldest, simply because they may have picked up invisible tricks and heuristics that escape our epistemic landscape, tricks that helped them survive in a world more complex than the one we think we can understand.

For instance, if a model used for risk assumes that the type of randomness under consideration is from Mediocristan, it will ignore

large deviations and encourage the building of a lot of risk that ignores large deviations; accordingly, risk management will be faulty. Hence the metaphor of “sitting on a barrel of dynamite” I used concerning Fannie Mae (now bust).

Mother Nature does not limit the interactions between entities; it just limits the size of its units.

Charles Tapiero and I have shown mathematically that a certain class of unforeseen errors and random shocks hurts large organisms vastly more than smaller ones.

spandrel effect, an auxiliary offshoot of a certain adaptation leads to a new function.

You don’t know today what may be needed tomorrow. This

The organism with the largest number of secondary uses is the one that will gain the most from environmental randomness and epistemic opacity!

Objects seem to have invisible but significant auxiliary functions that we are not aware of consciously, but that allow them to thrive—and on occasion, as with decorator books, the auxiliary function becomes the principal one.

that you can benefit from the randomness more than you can be hurt by it (an argument I call more technically convexity to uncertainty).

Our psychology conspires: people like to go to a precise destination, rather than face some degree of uncertainty, even if beneficial.

have, throughout this book, focused on the absence of practical distinctions between the various notions of luck, uncertainty, randomness, incompleteness of information, and fortuitous occurrences using the simple criterion of predictability, which makes them all functionally equal.

differences without a distinction. They can be brutally misleading. People use the same term, measuring, for measuring a table using a ruler, and for measuring risk—when the second is a forecast, or something of the sort.

we will see that we are psychologically very vulnerable to terms used and how things are framed. So if we used measuring for the table, and forecasting for risk, we would have fewer turkeys blowing up from Black Swans.

this is not an economics book, but a book on the incompleteness of knowledge and the effects of high-impact uncertainty—it just so happens that economists are the most Black-Swan-blind