By Nassim Nicholas Taleb, Random House, April 17, 2007, 978-1400063512

Nassim Nicholas Taleb makes us face that our statistics are out of whack with our observations. Taleb is a “hyper-skeptic” along with other skeptics such as Popper, Poincare, and Mandelbrot. His thesis is that our statistical techniques are based on induction and therefore part of the problem. Gaussian distributions do not exist in our natural world. Buffet & Gates are not an anomolies, they must be part of the distribution. Mandelbrot taught us about power laws, and we ignored him except for the pretty pictures of fractals that are so ubiquitous.

Taleb wants us to look for counterfactuals: data that disaffirms our hypothesis. Instead, we all look for confirmation: BMW drivers read BMW ads. We need to think about how we think about our data. It’s all too easy to pat ourselves on the back when we find a correlation after throwing out a few outliers – that really don’t matter, do they? Just the opposite: The outliers are Black Swans which effect change. Try a lot of things and keep what works is about the outliers, not the average of all the things. We have to expose ourselves to as many Black Swans with possible positive outcomes, and avoid as many Black Swans with possible negative outcomes.

[p xxi] So I disagree with the followers of Marx and those of Adam Smith: the reason free markets work is because they allow people to be lucky, thanks to aggressive trial and error, not by giving rewards or “incentives” for skill. The strategy is, then, to tinker as much as possible and try to collect as many Black Swan opportunities as you can.

[p xxi] We do not spontaneously learn that we_don’t_learn_that_we_don’t_learn. The problem lies in the structure of our minds: we don’t learn rules, just facts, and only facts. Metarules (such as the rule that we have a tendency to not learn rules) we don’t seem to be good at getting. We scorn the abstract; we scorn it with passion.

[p xxvii] There is a contradiction; this book is a story, and I prefer to use stories and vignettes to illustrate our gullibility about stories and our preference for the dangerous compression of narratives. You need a story to displace a story. Metaphors and stories are far more potent (alas) than ideas; they are also easier to remember and more fun to read. If I have to go after what I call the narrative disciplines, my best tool is a narrative.

Ideas come and go, stories stay.

[p30] Some people naively believe that the process of unfairness started with the gramophone, according to the logic that I just presented. I disagree. I am convinced that the process started much, much earlier, with our DNA, which stores information about our selves and allows us to repeat our performance without our being there by spreading our genes down the generations. Evolution is scalable: the DNA that wins (whether by luck or survival advantage) will reproduce itself, like a bestselling book or a successful record, and become pervasive. Other DNA will vanish. Just consider the difference between us humans (excluding financial economists and businessmen) and other living beings on our planet.

[p30] In the arts-say the cinema-things are far more vicious. What we call “talent” generally comes from success, rather than its opposite. A great deal of empiricism has been done on the subject, most notably by Art De [p31] Vany, an insightful and original thinker who singlemindedly studied wild uncertainty in the movies. He showed that, sadly, much of what we ascribe to skills is an after-the-fact attribution. The movie makes the actor, he claims–and a large dose of nonlinear luck makes the movie.

The success of movies depends severely on contagions. Such contagions do not just apply to the movies: they seem to affect a wide range of cultural products. It is hard for us to accept that people do not fall in love with works of art only for their own sake, but also in order to feel that they belong to a community. By imitating, we get closer to others–that is, other imitators. It fights solitude.

[p32] In the utopian province of Mediocristan, particular events don’t contribute much individually-only collectively. I can state the supreme law of Mediocristan as follows: When_your_sample_is_large,_no_single_instance_will_significantly change_the_aggregate_or_the_total. The largest observation will remain impressive, but eventually insignificant, to the sum.

[p33] Try it also with academic citations (the mention of one academic by another academic in a formal publication), media references, income, company size, and so on. Let us call these social matters, as they are manmade, as opposed to physical ones, like the size of waistlines.

In_Extremistan,_inequalities_are_such_that_one_single_observation_can disproportionately_impact_the_aggregate,_or_the_total.

So while weight, height, and calorie consumption are from Mediocristan, wealth is not. Almost all social matters are from Extremistan. Another way to say it is that social quantities are informational, not physical: you cannot touch them. Money in a bank account is something important, but certainly not_physical. As such it can take any value without necessitating the expenditure of energy. It is just a number!

[p43] So it took just one summer to figure out that this was a sucker’s business and that all their earnings came from a very risky game. All that while the bankers led everyone, especially themselves, into believing that they were “conservative.” They are not conservative; just phenomenally skilled at self-deception by burying tbe possibility of a large, devastating loss under the rug.

[p5] Now, there are other themes arising from our blindness to the Black Swan:

a. We focus on preselected segments of the seen and generalize from it to the unseen: the error of confirmation.

b. We fool ourselves with stories that cater to our Platonic thirst for distinct patterns: the narrative fallacy.

c. We behave as if the Black Swan does not exist: human nature is not programmed for Black Swans.

d. What we see is not necessarily all that is there. History hides Black Swans from us and gives us a mistaken idea about the odds of these events: this is the distortion of silent evidence.

e. We “tunnel”: that is, we focus on a few well-defined sources of uncertainty, on too specific a list of Black Swans (at the expense of the others that do not easily come to mind).

[p54] For another illustration of the way we can be ludicrously domainspecific in daily life, go to the luxury Reebok Sports Club in New York City, and look at the number of people who, after riding the escalator for a couple of floors, head directly to the StairMasters.

This domain specificity of our inferences and reactions works both ways: some problems we can understand in their applications but not in textbooks; others we are better at capturing in the textbook than in the practical application. People can manage to effortlessly solve a problem in a social situation but struggle when it is presented as an abstract logical problem. We tend to use different mental machinery-so-called modulesin different situations: our brain lacks a central all-purpose computer that starts with logical rules and applies them equally to all possible situations.

[p58] But_it_remains_the_case_that_you_know_what_is_wrong_with_a_lot more_confidence_than_you_know_what_is_right. All pieces of information are not equal in importance.

Popper introduced the mechanism of conjectures and refutations, which works as follows: you formulate a (bold) conjecture and you start looking for the observation that would prove you wrong. This is the alternative to our search for confirmatory instances. If you think the task is easy, you will be disappointed_few humans have a natural ability to do this. I confess that I am not one of them; it does not come naturally to me.

Counting to Three

Cognitive scientists have studied our natural tendency to look only for corroboration; they call this vulnerability to the corroboration error the confirmation_bias. There are some experiments showing that people focus only on the books read in Umberto Eco’s library. You can test a given rule either directly, by looking at instances where it works, or indirectly, by focusing on where it does not work. As we saw earlier, disconfirming instances are far more powerful in establishing truth. Yet we tend to not be aware of this property.

[p69] We, members of the human variety of primates, have a hunger for rules because we need to reduce the dimension of matters so they can get into our heads. Or, rather, sadly, so we can squeeze them into our heads. The more random information is, the greater the dimensionality, and thus the more difficult to summarize. The more you summarize, the more order it. you put in, the less randomness. Hence the_same condition_that_makes_us_simplify_pushes_us_to_think_that_the_world_is less_random_than_it_actually_is.

And the Black Swan is what we leave out of simplification.

[p73] How can you get rid of such a persistent throb? Don’t try to willingly avoid thinking about it: this will almost surely backfire. A more appropriate solution is to make the event appear more unavoidable. Hey, it was bound to take place and it seems futile to agonize over it. How can you do so? Well, with_a_narrative. Patients who spend fifteen minutes every day writing an account of their daily troubles feel indeed better about what has befallen them. You feel less guilty for not having avoided certain events; you feel less responsible for it. Things appear as if they were bound to happen.

If you work in a randomness-laden profession, as we see, you are likely to suffer burnout effects from that constant second-guessing of your past actions in terms of what played out subsequently. Keeping a diary is the least you can do in these circumstances.

[p89] These nonlinear relationships are ubiquitous in life. Linear relationships are truly the exception; we only focus on them in classrooms and textbooks because they are easier to understand. Yesterday afternoon I tried to take a fresh look around me to catalog what I could see during my day that was linear. I could not find anything, no more. than someone hunting for squares or triangles could find them in the rain forest-or, as we will see in Part Three, any more than someone looking for bell-shape randomness finding it in socioeconomic phenomena.

[p90] It is my great hope someday to see science and decision makers rediscover what the ancients have always known, namely that our highest currency is respect.

Even economically, the individual Black Swan hunters are not the ones who make the bucks. The researcher Thomas Astebro has shown that returns on independent inventions (you take the cemetery into account) are far lower than those on venture capital. Some blindness to the odds or an obsession with their own positive Black Swan is necessary for entrepreneurs to function. The venture capitalist is the one who gets the shekels. The economist William Baumol calls this “a touch of madness.” This may indeed apply to all concentrated businesses: when you look at the empirical record, you not only see that venture capitalists do better than entrepreneurs, but publishers do better than writers, dealers do better than artists, and science does better than scientists (about 50 percent of scientific and scholarly papers, costing months, sometimes years, of effort, are never truly read). The person involved in such gambles is paid in a currency other than material success: hope.

[p91] Making $1 million in one year, but nothing in the preceding nine, does not bring the same pleasure as having the total evenly distributed over the same period, that is, $100,000 every year for ten years in a row. The same applies to the inverse order-making a bundle the first year, then nothing for the remaining period. Somehow, your pleasure system will be saturated rather quickly, and it will not carry forward the hedonic balance like a sum on a tax return. As a matter of fact, your happiness depends far more on the number of instances of positive feelings, what psychologists call ,”positive affect,” than on their intensity when they hit. In other words, good news is good news first; how good matters rather little. So to have a pleasant life you should spread these small “affects” across time as evenly as possible. Plenty of mildly good news is preferable to one single lump of great news.

[p116] Once again, I am not dismissing the idea of risk taking, having been involved in it myself. I am only critical of the encouragement of uninformed risk taking. The iiberpsychologist Danny Kahneman has given us evidence that we generally take risks not out of bravado but out of ignorance and blindness to probability! The next few chapters will show in more depth how we tend to dismiss outliers and adverse outcomes when projecting the future. But I insist on the following: that_we_got_here_by_accident_does_not_mean_that_we_should_continue to_take_the_same_risks. We are mature enough a race to realize this point, enjoy our blessings, and try to preserve, by becoming more conservative, what we got by luck. We have been playing Russian roulette; now let’s stop and get a real job.

[p118] Once when I returned to Lebanon during the war, at the age of eighteen, I felt episodes of extraordinary fatigue and cold chills in spite of the summer heat. It was typhoid fever. Had it not been for the discovery of antibiotics, only a few decades earlier, I would not be here today. I was also later “cured” of another severe disease that would have left me for dead, thanks to a treatment that depends on another recent medical technology. As a human being alive here in the age of the Internet, capable of writing and reaching an audience, I have also benefited from society’s luck and the remarkable absence of recent large-scale war. In addition, I am the result of the rise of the human race, itself an accidental event.

My being here is a consequential low-probability occurrence, and I tend to forget it.

[p120] My biggest problem with the educational system lies precisely in that it forces students to squeeze explanations out of subject matters and shames them for withholding judgment, for uttering the “I don’t know.” Why did the Cold War end? Why did the Persians lose the battle of Salamis? Why did Hannibal get his behind kicked? Why did Casanova bounce back from hardship? In each of these examples, we are taking a condition, survival, and looking for the explanations, instead of flipping the argument on its head and stating that conditional_on_such_survival, one cannot read that much into the process, and should learn instead to invoke some measure of randomness (randomness is what we don’t know; to invoke randomness is to plead ignorance). It is not just your college professor who gives
you bad habits. I showed in Chapter 6 how newspapers need to stuff their texts with causal links to make you enjoy the narratives. But have the integrity to deliver your “because” very sparingly; try to limit it to situations where the “because” is derived from experiments, not backward-looking history.

Note here that I am not saying causes do not exist; do not use this argument to avoid trying to learn from history. All I am saying is that it is not_so_simple; be suspicious of the “because” and handle it with careparticularly in situations where you suspect silent evidence.

[p129] In a beautiful treatise now vanished from our consciousness, Dissertation_on_the_Search_for_Truth, published in 1673, the polemist Simon Foucher exposed our psychological predilection for certainties. He teaches us the art of doubting, how to position ourselves between doubting and believing. He writes: “One needs to exit doubt in order to produce science-but few people heed the importance of not exiting from it prematurely …. It is a fact that one usually exits doubt without realizing it.” He warns us further: “We are dogma-prone from our mother’s wombs.”

By the confirmation error discussed in Chapter 5, we use the example of games, which probability theory was successful at tracking, and claim that this is a general case. Furthermore, just as we tend to underestimate the role of luck in life in general, we tend to overestimate it in games of chance.

[p132] Alas, we are not manufactured, in our current edition of the human race, to understand abstract matters-;we need context. Randomness and uncertainty are abstractions. We respect what has happened, ignoring what could_have happened. In other words, we are naturally shallow and superficial-and we do not know it. This is not a psychological problem; it comes from the main property of information. The dark side of the moon is harder to see; beaming light on it costs energy. In the same way, beaming light on the unseen is costly in both computational and mental effort.

[p138] Why on earth do we predict so much? Worse, even, and more interesting: Why don’t we talk about our record in predicting? Why don’t we see how we (almost) always miss the big events? I call this the scandal of prediction.

[p143] When you are employed, hence dependent on other people’s judgment, looking busy can help you claim responsibility for the results in a random environment. The appearance of busyness reinforces the perception of causality, of the link between results and one’s role in them. This of course applies even more to the CEOs of large companies who need to trumpet a link between their “presence” and “leadership” and the results of the company. I am not aware of any studies that probe the usefulness of their time being invested in conversations and the absorption of small-time information-nor have too many writers had the guts to question how large the CEO’s role is in a corporation’s success.

Let us discuss one main effect of information: impediment to knowledge.

[p144] The more information you give someone, the more hypotheses they will formulate along the way, and the worse off they will be. They see more random noise and mistake it for information.

The problem is that our ideas are sticky: once we produce a theory, we are not likely to change our minds-so those who delay developing their theories are better off. When you develop your opinions on the basis of weak evidence, you will have difficulty interpreting subsequent information that contradicts these opinions, even if this new information is obviously more accurate. Two mechanisms are at play here: the confirmation bias that we saw in Chapter 5, and belief perseverance, the tendency not to reverse opinions you already have. Remember that we treat ideas like possessions, and it will be hard for us to part with them.

[p149] One elementary empirical test is to compare these star economists to a hypothetical cabdriver (the equivalent of Mikhail from Chapter 1): you create a synthetic agent, someone who takes the most recent number as the best predictor of the next, while assuming that he does not know anything. Then all you have to do is compare the error rates of the hotshot economists and your synthetic agent. The problem is that when you are swayed by stories you forget about the necessity of such testing.

[p151] Tetlock studied the business of political and economic “experts.” He asked various specialists to judge the likelihood of a number of political, economic, and military events occurring within a specified time frame (about five years ahead). The outcomes represented a total number of around twenty-seven thousand predictions, involving close to three hundred specialists. Economists represented about a quarter of his sample. The study revealed that experts’ error rates were clearly many times what they had estimated. His study exposed an expert problem: there was no difference in results whether one had a PhD or an undergraduate degree. Well-published professors had no advantage over journalists. The only regularity Tetlock found was the negative effect of reputation on prediction: those who had a big reputation were worse predictors than those who had none.

[p155] Here again, you see the narrative fallacy at work, except that in place of journalistic stories you have the more dire situation of the “scientists” with a Russian accent looking in the rearview mirror, narrating with equations, and refusing to look ahead because he may get too dizzy. The econometrician Robert Engel, an otherwise charming gentleman, invented a very complicated statistical method called GARCH and got a Nobel for it. No one tested it to see if it has any validity in real life. Simpler, less sexy methods fare exceedingly better, but they do not take you to Stockholm. You have an expert problem in Stockholm, and I will discuss it in Chapter 17.

This unfitness of complicated methods seems to apply to all methods.

[p166] The managers flew across the world in order to meet: Barcelona, Hong Kong, et cetera. A lot of miles for a lot of verbiage. Needless to say they were usually sleep-deprived. Being an executive does not require very developed frontal lobes, but rather a combination of charisma, a capacity to sustain boredom, and the ability to shallowly perform on harrying schedules. Add to these tasks the “duty” of attending opera performances.

The managers sat down to brainstorm during these meetings, about, of course, the medium-term future-they wanted to have “vision.” But then

an event occurred that was not in the previous five-year plan: the Black Swan of the Russian financial default of 1998 and the accompanying meltdown of the values of Latin American debt markets. It had such an effect on the firm that, although the institution had a sticky employment policy of retaining managers, none of the five was still employed there a month after the sketch of the 1998 five-year plan.

Yet I am confident that today their replacements are still meeting to work on the next “five-year plan.” We never learn.

[p167] We forget about unpredictability when it is our turn to predict. This is why people can read this chapter and similar accounts, agree entirely with them, yet fail to heed their arguments when thinking about the future.

Take this dramatic example of a serendipitous discovery. Alexander Fleming was cleaning up his laboratory when he found that penicillium mold had contaminated one of his old experiments. He thus happened upon the antibacterial properties of penicillin, the reason many of us are alive today (including, as I said in Chapter 8, myself, for typhoid fever is often fatal when untreated). True, Fleming was looking for “something,” but the actual discovery was simply serendipitous.

[p168] As happens so often in discovery, those looking for evidence did not find it; those not looking for it found it and were hailed as discoverers.

[p169] Engineers tend to develop tools for the pleasure of developing tools, not to reduce nature to yield its secrets. It so happens that some of these tools bring us more knowledge; because of the silent evidence effect, we forget consider tools that accomplished nothing but keeping engineers off the streets. Tools lead to unexpected discoveries, which themselves lead to other unexpected discoveries. But rarely do our tools seem to work as intended; it is only the engineer’s gusto and love for the building of toys and chines that contribute to the augmentation of our knowledge. Knowledge does not progress from tools designed to verify or help theories, but rather the opposite. The computer was not built to allow us to develop new, visual, geometric mathematics, but for some other purpose. It happened to allow us to discover mathematical objects that few cared to look for. Nor was the computer invented to let you chat with your friends in Siberia, but it has caused some long-distance relationships to bloom. As an essayist, I can attest that the Internet has helped me to spread my ideas by bypassing journalists. But this was not the stated purpose of its military designer.

[p170] Yet just consider the effects of the laser in the world around you: compact disks, eyesight corrections, microsurgery, data storage and retrieval-all unforeseen applications of the technology.

We build toys. Some of those toys change the world.

[p170] “Luck favors the prepared,” Pasteur said, and, like all great discoverers, he knew something about accidental discoveries. The best way to get maximal exposure is to keep researching. Collect opportunities–on that, later.

[p172] This point can be generalized to all forms of knowledge. There is actually a law in statistics called the law_of_iterated_expectations, which I outline here in its strong form: if I expect to expect something at some date in the future, then I already expect that something at present.

[p172] This incapacity is not trivial. The mere knowledge that something has been invented often leads to a series of inventions of a similar nature, even though not a single detail of this invention has been disseminated–there is no need to find the spies and hang them publicly. In mathematics, once a proof of an arcane theorem has been announced, we frequently witness the proliferation of similar proofs coming out of nowhere, with occasional accusations of leakage and plagiarism. There may be no plagiarism: the information that the solution exists is itself a big piece of the solution.

[p178] This multiplicative difficulty leading to the need for greater and greater precision in assumptions can be illustrated with the following simple exercise concerning the prediction of the movements of billiard balls on a table. I use the example as computed by the mathematician Michael Berry. If you know a set of basic parameters concerning the ball at rest, can compute the resistance of the table (quite elementary), and can gauge the strength of the impact, then it is rather easy to predict what would happen at the first hit. The second impact becomes more complicated, but possible; you need to be more careful Clbout your knowledge of the initial states, and more precision is called for. The problem is that to correctly compute the ninth impact, you need to take into account the gravitational pull of someone standing next to the table (modestly, Berry’s computations use a weight of less than 150 pounds). And to compute the fifty-sixth impact, every single elementary particle of the universe needs to be present in your assumptions! An electron at the edge of the universe, separated from us by 10 billion light-years, must figure in the calculations, since it exerts a meaningful effect on the outcome. Now, consider the additional burden of having to incorporate predictions about where_these_variables_will_be_in_the_future. Forecasting the motion of a billiard ball on a pool table requires knowledge of the dynamics of the entire universe, down to every single atom! We can easily predict the movements of large objects like planets (though not too far into the future), but the smaller entities can be difficult to figure out–and there are so many more of them.

[p178] Poincare proposed that we can only work with qualitative [p179] matters-some property of systems can be discussed, but not computed. You can think rigorously, but you cannot use numbers. Poincare even invented a field for this, analysis in situ, now part of topology. Prediction and forecasting are a more complicated business. than is commonly accepted, but it takes someone who knows mathematics to understand that. To accept it takes both understanding and courage.

[p185] Alas, it turns out that it was [Paul] Samuelson and most of his followers who did not know much math, or did not know how to use what math they knew, how to apply it to reality. They only knew enough math to be blinded by it.

[p189] We have a natural tendency to listen to the exper, even in fields where there may be no experts.

[p192] Epistemocracy

Everyone has an idea of utopia. For many it means equality, universal justice, freedom from oppression, freedom from work (for some it may be the more modest, though no more attainable, society with commuter trains free of lawyers on cell phones). To me utopia is an epistemocracy, a society in which anyone of rank is an epistemocrat, and where epistemocrats manage to be elected. It would be a society governed from the basis of the awareness of ignorance, not knowledge.

Alas, one cannot assert authority by accepting one’s own fallibility. Simply, people need to be blinded by knowledge-we are made to follow leaders who can gather people together because the advantages of being in groups trump the disadvantages of being alone. It has been more profitable for us to bind together in the wrong direction than to be alone in the right one. Those who have followed the assertive idiot rather than the introspective wise person have passed us some of their genes. This is apparent from a social pathology: psychopaths rally followers.

[p202] Where I beg to differ with the great man [Bertrand Russell] is that I do not believe in the track record of advice-giving “philosophy” in helping us deal with the problem; nor do I believe that virtues can be easily taught; nor do I urge people to strain in order to avoid making a judgment. Why? Because we have to deal with humans as humans. We cannot teach people to withhold judgment; judgments are embedded in the way we view objects. I do not see a “tree”; I see a pleasant or an ugly tree. It is not possible without great, paralyzing effort to strip these small values we attach to matters. Likewise, it is not possible to hold a situation in one’s head without some element of bias.

[p203] Know how to rank beliefs not according to their plausibility but by the harm they may cause.

The bottom line: be prepared! Narrow-minded prediction has an analgesic or therapeutic effect. Be aware of the numbing effect of magic numbers. Be prepared for all relevant eventualities.


Recall the empirics, those members of the Greek school of empirical medicine. They considered that you should be open-minded in your medical diagnoses to let luck play a role.

[p204] In Japanese culture, which is ill-adapted to randomness and badly equipped to understand that bad performance can come from bad luck, losses can severely tarnish someone’s reputation. People hate volatility, thus engage in strategies exposed to blowups, leading to occasional suicides after a big loss.

Furthermore, this trade-off between volatility and risk can show up in

[p205] careers that give the appearance of being stable, like jobs at IBM until the 1990s. When laid off, the employee faces a total void: he is no longer fit for anything else. The same holds for those in protected industries. On the other hand, consultants can have volatile earnings as their clients’ earnings go up and down, but face a lower risk of starvation, since their skills match demand-fluetuat nec mergitur (fluctuates but doesn’t sink).

[p205] [footnote] Make sure that you have plenty of these small bets; avoid being blinded by the vividness of one single Black Swan. Have as many of these small bets as you can conceivably have. Even venture capital firms fall for the narrative fallacy with a few stories that “make sense” to them; they do not have as many bets as they should. If venture capital firms are profitable, it is not because of the stories they have in their heads, but because they are exposed to unplanned rare events.

[p206] Here are the (modest) tricks. But note that the more modest they are, the more effective they will be.

a. First,_make_adistinction_between positive contingencies _and negative ones. Learn to distinguish between those human undertakings, in which the lack of predictability can be (or has been) extremely beneficial and those where the failure to understand the future caused harm. There are both positive and negative Black Swans.

[p208] b. Don’t_look_for_the_precise_and_the_local. Simply, do not be narrowminded. The great discoverer Pasteur, who came up with the notion that chance favors the prepared, understood that you do not look for something particular every morning but work hard to let contingency enter your working life. As Yogi Berra, another great thinker, said, “You got to be very careful if you don’t know where you’re going, because you might not get there.”

[p208] Remember that infinite vigilance is just not possible.

c. Seize_any_opportunity,_or_anything_that_looks_like opportunity. They are rare, much rarer than you think. Remember that positive Black Swans have a necessary first step: you need to be exposed to them. Many people do not realize that they are getting a lucky break in life when they get it. If a big publisher (or a big art dealer or a movie executive or a hotshot banker or a big thinker) suggests [p209] an appointment, cancel anything you have planned: you may never see such a window open up again.

[p209] d. Beware_of_precise_plans_by_governments. As discussed in Chapter 10, let governments predict (it makes officials feel better about themselves and justifies their existence) but do not set much store by what they say. Remember that the interest of these civil servants is to survive and self-perpetuate–not to get to the truth. It does not mean that governments are useless, only that you need to keep a vigilant eye on their side effects.

[p210] e. “There are some people who, if they don’t already know, you can’t tell ‘em,” as the great philosopher of uncertainty Yogi Berra once said. Do_not_waste_your_time_trying_to_fight_forecasters,_stock analysts,_economists,_and_social_scientists,_except_to_play_pranks_on _them. They are considerably easy to make fun of, and many get angry quite readily. It is ineffective to moan about unpredictability: people will continue to predict foolishly, especially if they are paid for it, and you cannot put an end to institutionalized frauds., If you ever do have to heed a forecast, keep in mind that its accuracy degrades rapidly as you extend it through time.

[p210] The Great Asymmetry

All these recommendations have one point in common: asymmetry. Put yourself in situations where favorable consequences are much larger than unfavorable ones.

Indeed, the notion of asymmetric outcomes as the central idea of this book: I will never get to know the unknown since, by definition, it is unknown. However, I can always guess how it might affect me, and I should base my decisions around that.

[p211] We can have a clear idea of the consequences of an event, even if we do not know how likely it is to occur. I don’t know the odds of an earthquake, but I can imagine how San Francisco might be affected by one. This idea that in order to make a decision you need to focus on the consequences (which you can know) rather than the probability (which you can’t know) is the central idea of uncertainty. Much of my life is based on it.

[p211] The next chapter shows why I am optimistic that the academy is losing its power and ability to put knowledge in straitjackets and that more out-of-the-box knowledge will be generated Wiki-style.

[p220] What people call “memes,” ideas that spread and that compete with one another using people as carriers, are not truly like genes. Ideas spread because, alas, they have for carriers self-serving agents who are interested in them, and interested in distorting them in the replication process. You do not make a cake for the sake of merely replicating a recipe-you try to make your own cake, using ideas from others to improve it. We humans are not photocopiers.

[p225] In sum, the long tail is a by-product of Extremistan that makes it somewhat less unfair: the world is made no less unfair for the little guy, but it now becomes extremely unfair for the big man. Nobody is truly established. The little guy is very subversive.


There is, inevitably, a mounting tension between our society, full of concentration, and our classical idea of aurea mediocritas, the golden mean, so it is conceivable that efforts may be made to reverse such concentration. We live in a society of one person, one vote, where progressive taxes have been enacted precisely to weaken the winners. Indeed, the rules of society can be easily rewritten by those at the bottom of the pyramid to prevent concentration from hurting them. But it does not require voting to do soreligion could soften the problem. Consider that before Christianity, in many societies the powerful had many wives, thus preventing those at the bottom from accessing wombs, a condition that is not too different from the reproductive exclusivity of alpha males in many species. But Christianity reversed this, thanks to the one man-one woman rule. Later, Islam came to limit the number of wives to four. Judaism, which had been polygenic, became monogamous in the Middle Ages. One can say that such a strategy has been successful-the institution of tightly monogamous marriage (with no official concubine, as in the Greco-Roman days), even when practiced the “French way,” provides social stability since there is no pool of angry, sexually deprived men at the bottom fomenting a revolution just so they can have the chance to mate.

But I find the emphasis on economic inequality, at the expense of other types of inequality, extremely bothersome. Fairness is not exclusively an economic matter; it becomes less and less so when we are satisfying our basic material needs. It is pecking order that matters! The superstars will always be there. The Soviets may have flattened the economic structure, but they encouraged their own brand of iibermensch. What is poorly understood, or denied (owing to its unsettling implications), is the absence of a role for the average in intellectual production. The disproportionate share of the very few in intellectual influence is even more unsettling than the unequal distribution of wealth-unsettling because, unlike the income gap, no social policy can eliminate it. Communism could conceal or compress income discrepancies, but it could not eliminate the superstar system in intellectual life.

[p228] Winners kill their peers as those in a steep social gradient live shorter lives, regardless of their economic condition.

I do not know how to remedy this (except through religious beliefs). Is insurance against your peers’ demoralizing success possible? Should the Nobel Prize be banned? Granted the Nobel medal in economics has not been good for society or knowledge, but even those rewarded for real contributions in medicine and physics too rapidly displace others from our consciousness, and steal longevity away from them. Extremistan is here to stay, so we have to live with it, and find the tricks that make it more palatable.

[p228] I had time to kill at the airport and it was a great opportunity for me to buy dark European chocolate, especially since I have managed to successfully convince myself that airport calories don’t count.

[p232] [footnote] One of the most misunderstood aspects of a Gaussian is its fragility and vulnerability in the estimation of tail events. The odds of a 4 sigma move are twice that of a 4.15 sigma. The odds of a 20 sigma are a trillion times higher than those of a 21 sigma! It means that a small measurement error of the sigma will lead to a massive under-estimation of the probability. We can be a trillion times wrong about some events.

[p237] I’ve had plenty of cups of coffee in my life (it’s my principal addiction).

[p245] This explains why empirical psychology and its insights on human nature, which I presented in the earlier parts of this book, are robust to the mistake of using the bell curve; they are also lucky, since most of their variables allow for the application of conventional Gaussian statistics. When measuring how many people in a sample have a bias, or make a mistake, these studies generally elicit a yeslno type of result. No single observation, by itself, can disrupt their overall findings.

[p250] Those Comforting Assumptions

Note the central assumptions we made in the coin-flip game that led to the proto-Gaussian, or mild randomness.

First_central_assumption: the flips are independent of one another. The conin no memory. The fact that you got heads or tails on the previous flip does not change the odds of your getting heads or tails on the next one. You do not become a “better” coin flipper over time. If you introduce memory, or skills in flipping, the entire Gaussian business becomes shaky.

[p251] Second_central_assumption: no “wild” jump. The step size in the bu~lding block of the basic random walk is always known, namely one step. There is no uncertainty as to the size of the step. We did not encounter situations in which the move varied wildly.

Remember that if either of these two central assumptions is not met, your moves (or coin tosses) will not cumulatively lead to the bell curve. Depending on what happens, they can lead to the wild Mandelbrotian-style scale-invariant randomness.

[p252] I sometimes get a little emotional because I’ve spent a large part of my life thinking about this problem. Since I started thinking about it, and conducting a variety of thought experiments as I have above, I have not for the life of me been able to find anyone around me in the business and statistical world who was intellectually consistent in that he both accepted the Black Swan and rejected the Gaussian and Gaussian tools. Many people accepted my Black Swan idea but could not take it to its logical conclusion, which is that you cannot use one single measure for randomness called standard deviation (and call it “risk”); you cannot expect a simple answer to characterize uncertainty. To go the extra step requires courage, commitment, an ability to connect the dots, a desire to understand randomness fully. It also means not accepting other people’s wisdom as gospel. Then I started finding physicists who had rejected the Gaussian tools but fell for another sin: gullibility about precise predictive models, mostly elaborations around the preferential attachment of Chapter 14another form of Pia tonicity. I could not find anyone with depth and scientific technique who looked at the world of randomness and understood its nature, who looked at calculations as an aid, not a principal aim. It took me close to a decade and a half to find that thinker, the man who made many swans gray: Mandelbrot-the great Benoit Mandelbrot.

[p268] [N]early everyone who works with data but doesn’t make decisions on the basis of these data tends to be guilty of the same sin, a variation of the narrative fallacy. In the absence of a feedback process you look at models and think that they confirm reality. […] As a matter of fact, complexity theory should make us more suspicious of scientific claims of precise models of reality. It does not make all the swans white; that is predictable: it makes them gray, and only gray.

[p269] I thought that finance and economics were just a place where one learned from various empirical phenomena and filled up one’s bank account with f***_you cash before leaving for bigger and better things. Mandelbrot’s answer was, “Data, a gold mine of data.” Indeed, everyone forgets that he started in economics before moving on to physics and the geometry of nature. Working with such abundant data humbles us; it provides the intuition of the following error: traveling the road between representation and reality in the wrong direction.

[p284] I care about the premises more than the theories, and I want to minimize reliance on theories, stay light on my feet, and reduce my surprises. I want to be broadly right rather than precisely wrong. Elegance in the theories is often indicative of Platonicity and weakness–it invites you to seek elegance for elegance’s sake. A theory is like medicine (or government): often useless, sometimes necessary, always self-serving, and on occasion lethal. So it needs to be used with care, moderation, and close adult supervision.

[p290] 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. This approach incapacitates much of that career-building verbiage.

[p297] It is more difficult to be a loser in a game you set up yourself.

In Black Swan terms, this means that you are exposed to the improbable only if you let it control you. You always control what you do; so make this your end.

[298] Stop looking a gift horse in the mouth–remeber that you are a Black Swan. And thank you for reading my book