Editor’s Note: Nassim Nicholas Taleb, the bestselling author of “The Black Swan,” has published a new book about politics and finance titled “Skin in the Game.” PBS NewsHour economics correspondent Paul Solman spoke with Taleb about the book, the 2008 financial crisis, and President Donald Trump. Their conversation is presented here, edited for length and clarity. Watch the full segment on Thursday’s NewsHour program.

PAUL SOLMAN: I first interviewed you in 2006. “Black Swan” hadn’t even come out yet. Then came “Black Swan,” the book. Then came the crash of ’08. You became famous for warning people, having warned people, about extreme events and how cataclysmic they could be, right?

NASSIM NICHOLAS TALEB: The reason people paid attention to my work was because I had skin the game at the time. I was involved. I was taking risks.

PAUL SOLMAN: You were a trader.

NASSIM NICHOLAS TALEB: I was involved. I was eating my risk. Owning my own risk, as I write in the book.

PAUL SOLMAN: Are there black swans on the horizon now? What are you betting on?

NASSIM NICHOLAS TALEB: The point is, the system is fragile because we had a lot of debt. Plus, there are a bunch of things that have been developing that I’m not comfortable with, developing over, say, the least 20 years, but mostly the last 10 years, I’m not comfortable with.

PAUL SOLMAN: They are?

NASSIM NICHOLAS TALEB: It’s that rise of the class, the no-skin-in-the-game class in decision-making.

PAUL SOLMAN: The no-skin-in-the-game class?

NASSIM NICHOLAS TALEB: Exactly. Decision-makers who can drag you into intervention, can drag you into policies that cosmetically feel good, but eventually, somebody pays a price and it’s not them.

There are two levels. The first one, and the most obvious one, is people who intervene in Iraq, thinking, “Hey, we’re going to bring democracy,” or some abstract concept. The thing falls apart, and they walk away from it. They’re not committed with living or owning the toy. They broke it. They don’t own it. Then, the same people make the same mistake with Libya and then now currently with Syria, the warmongers. In the past, historically, warmongers were soldiers. You could not rise in a senate if you didn’t have war experience. [Today if] you have a class of people who inflict risk on others without being affected by the outcome, that class of people is going to disrupt the system, causing some kind of collapse.

PAUL SOLMAN: Do you see some kind of collapse on the horizon?

NASSIM NICHOLAS TALEB: I can see some severe distortions now from that class of people deciding to “fix” things and, effectively, not paying the price.

PAUL SOLMAN: What risk are they posing to us now?

NASSIM NICHOLAS TALEB: Well, the system is loaded with debt that has benefited these bankers. The chairman of a certain bank now is making $23 million a year again in bonuses…. So, people don’t understand that we’re not learning from previous crises to force people to have skin in the game, so they can avoid stashing these risks.

Paul Solman: But if I’m a manager, CEO of a company and I have stock options, then I am punished if the stock goes down.

NASSIM NICHOLAS TALEB: No, not really, because you still have upside, net you have upside.

PAUL SOLMAN: You mean, I’m only going to be compensated, I’m never going to have money taken away from me.

NASSIM NICHOLAS TALEB: Exactly, whereas the taxpayer only has a downside of that trade. The taxpayer will never have the benefit of what’s going on, but we pay the price as taxpayers.

PAUL SOLMAN: Because we’re going to bail them out, you mean?

NASSIM NICHOLAS TALEB: Of course, so we are really the people who are owning the risk.

PAUL SOLMAN: So, what’s the cost?

NASSIM NICHOLAS TALEB: Let me take you back to the “Black Swan” and an idea I continued. In the “Black Swan,” I asked myself, “There are experts who are experts, and experts who aren’t. What marker is there? How would we know? We know very well that a pilot, a plane pilot, is an expert. Why, because there’s skin in the game, there’s some kind of contact with reality. A dentist is an expert. Your tailor is an expert. But you can never tell if an employee of the Federal Reserve Bank of the United States is an expert. As a matter of fact, I’m certain that they’re not experts. Economic forecasters, [but] they are not experts. So, they are what I call the “faux experts.”

We know where they are. It’s very simply someone who makes a decision that doesn’t have visible consequences for the person to be affected. And that’s what I call the no-skin-in-the-game expert.

PAUL SOLMAN: And it’s to the reaction against those experts that you attribute to Brexit and Donald Trump?

NASSIM NICHOLAS TALEB: Yes, of that rise of the class of pseudo experts running our affairs.

PAUL SOLMAN: My initial question was, “What black swans do you see now?” You said, ‘Hey, too many people with not enough skin in the game, is setting us up for…’ What?

NASSIM NICHOLAS TALEB: For a riot, because people understand. They have the Web, they have Twitter, they have access.

PAUL SOLMAN: What are they going to do?

NASSIM NICHOLAS TALEB: They are rioting. They elected Trump, they are electing all these governments… There’s a riot against the class of over-educated, Harvard, Ivy-league, Cambridge, Oxford, Ecole Normale in France, this whole class of people is no longer going to be able to run our affairs… The system laden with debt and with pseudo experts will collapse eventually.

PAUL SOLMAN: So, that’s the black swan, a collapse.

NASSIM NICHOLAS TALEB: A collapse, because we haven’t really remedied what happened in 2008. We haven’t fixed anything from 2008, what caused 2008. There’s still a lot of debt in the system… Now it may be, miraculously, under Trump, we may have a second wind and America may rise again, and pay the debt. Hopefully that would work.

PAUL SOLMAN: You mean huge economic growth?

NASSIM NICHOLAS TALEB: That’s my hope.

PAUL SOLMAN: Were you in favor of Donald Trump?

NASSIM NICHOLAS TALEB: I was not against. First of all I gave him higher odds, because of this. I was writing the chapter on the I-Y-I, the Intellectual Yet Idiot, and I was describing the mechanism. And I said people are rioting against that. And I said that anyone who makes more sense to your Chinese grocery store owner, just off the ship, more than to an intellectual, would win. That’s what happened. I gave Trump close to 50 percent chance at a time when it was not possible. Mostly for technical reasons, and also because I believe that you can see that he makes a lot of sense to merchants, to small business owners, but he doesn’t make sense to intellectuals. So, he has to be that person. But anyone would have been elected, had they played that same platform, of coming in and trying to address in simplistic, but very clear, no nonsense terms to the general public.

PAUL SOLMAN: By saying the people who have been running the show have been leading you astray.

NASSIM NICHOLAS TALEB: He got the disease right. Now whether he’s going to fix it, I don’t know.

The black swan theory or theory of black swan events is a metaphor that describes an event that comes as a surprise, has a major effect, and is often inappropriately rationalized after the fact with the benefit of hindsight. The term is based on an ancient saying that presumed black swans did not exist – a saying that became reinterpreted to teach a different lesson after black swans were discovered in the wild.

The theory was developed by Nassim Nicholas Taleb to explain:

1. The disproportionate role of high-profile, hard-to-predict, and rare events that are beyond the realm of normal expectations in history, science, finance, and technology.
2. The non-computability of the probability of the consequential rare events using scientific methods (owing to the very nature of small probabilities).
3. The psychological biases that blind people, both individually and collectively, to uncertainty and to a rare event's massive role in historical affairs.

Unlike the earlier and broader 'black swan problem' in philosophy (i.e. the problem of induction), Taleb's 'black swan theory' refers only to unexpected events of large magnitude and consequence and their dominant role in history. Such events, considered extreme outliers, collectively play vastly larger roles than regular occurrences.^[1]:xxi More technically, in the scientific monograph 'Silent Risk',^[2] Taleb mathematically defines the black swan problem as 'stemming from the use of degenerate metaprobability'.^[2]

Background[edit]

The phrase 'black swan' derives from a Latin expression; its oldest known occurrence is from the 2nd-century Roman poet Juvenal's characterization of something being 'rara avis in terris nigroque simillima cygno' ('a rare bird in the lands and very much like a black swan').^[3]:165 When the phrase was coined, the black swan was presumed not to exist. The importance of the metaphor lies in its analogy to the fragility of any system of thought. A set of conclusions is potentially undone once any of its fundamental postulates is disproved. In this case, the observation of a single black swan would be the undoing of the logic of any system of thought, as well as any reasoning that followed from that underlying logic.

Juvenal's phrase was a common expression in 16th century London as a statement of impossibility. The London expression derives from the Old World presumption that all swans must be white because all historical records of swans reported that they had white feathers.^[4] In that context, a black swan was impossible or at least nonexistent.

However, in 1697, Dutch explorers led by Willem de Vlamingh became the first Europeans to see black swans, in Western Australia.^[5] The term subsequently metamorphosed to connote the idea that a perceived impossibility might later be disproven. Taleb notes that in the 19th century, John Stuart Mill used the black swan logical fallacy as a new term to identify falsification.^[6]

Black swan events were discussed by Nassim Nicholas Taleb in his 2001 book Fooled By Randomness, which concerned financial events. His 2007 book The Black Swan extended the metaphor to events outside of financial markets. Taleb regards almost all major scientific discoveries, historical events, and artistic accomplishments as 'black swans'—undirected and unpredicted. He gives the rise of the Internet, the personal computer, World War I, the dissolution of the Soviet Union, and the September 11, 2001 attacks as examples of black swan events.^[1]:prologue

Taleb asserts:^[7]

What we call here a Black Swan (and capitalize it) is an event with the following three attributes.

First, it is an outlier, as it lies outside the realm of regular expectations, because nothing in the past can convincingly point to its possibility. Second, it carries an extreme 'impact'. Third, in spite of its outlier status, human nature makes us concoct explanations for its occurrence after the fact, making it explainable and predictable.

I stop and summarize the triplet: rarity, extreme 'impact', and retrospective (though not prospective) predictability. A small number of Black Swans explains almost everything in our world, from the success of ideas and religions, to the dynamics of historical events, to elements of our own personal lives.

Identifying[edit]

Based on the author's criteria:

1. The event is a surprise (to the observer).
2. The event has a major effect.
3. After the first recorded instance of the event, it is rationalized by hindsight, as if it could have been expected; that is, the relevant data were available but unaccounted for in risk mitigation programs. The same is true for the personal perception by individuals.

Coping with[edit]

The practical aim of Taleb's book is not to attempt to predict events which are unpredictable, but to build robustness against negative events while still exploiting positive events. Taleb contends that banks and trading firms are very vulnerable to hazardous black swan events and are exposed to unpredictable losses. On the subject of business, and quantitative finance in particular, Taleb critiques the widespread use of the normal distribution model employed in financial engineering, calling it a Great Intellectual Fraud. Taleb elaborates the robustness concept as a central topic of his later book, Antifragile: Things That Gain From Disorder.

In the second edition of The Black Swan, Taleb provides 'Ten Principles for a Black-Swan-Robust Society'.^{[1]:374–78[8]}

Taleb states that a black swan event depends on the observer. For example, what may be a black swan surprise for a turkey is not a black swan surprise to its butcher; hence the objective should be to 'avoid being the turkey' by identifying areas of vulnerability in order to 'turn the Black Swans white'.^[9]

Epistemological approach[edit]

Taleb's black swan is different from the earlier philosophical versions of the problem, specifically in epistemology, as it concerns a phenomenon with specific empirical and statistical properties which he calls, 'the fourth quadrant'.^[10]

Taleb's problem is about epistemic limitations in some parts of the areas covered in decision making. Scheduling a ged test. These limitations are twofold: philosophical (mathematical) and empirical (human known epistemic biases). The philosophical problem is about the decrease in knowledge when it comes to rare events as these are not visible in past samples and therefore require a strong a priori, or an extrapolating theory; accordingly predictions of events depend more and more on theories when their probability is small. In the fourth quadrant, knowledge is uncertain and consequences are large, requiring more robustness.^{[citation needed]}

According to Taleb,^[11] thinkers who came before him who dealt with the notion of the improbable, such as Hume, Mill, and Popper focused on the problem of induction in logic, specifically, that of drawing general conclusions from specific observations. The central and unique attribute of Taleb's black swan event is that it is high-profile. His claim is that almost all consequential events in history come from the unexpected — yet humans later convince themselves that these events are explainable in hindsight.

One problem, labeled the ludic fallacy by Taleb, is the belief that the unstructured randomness found in life resembles the structured randomness found in games. This stems from the assumption that the unexpected may be predicted by extrapolating from variations in statistics based on past observations, especially when these statistics are presumed to represent samples from a normal distribution. These concerns often are highly relevant in financial markets, where major players sometimes assume normal distributions when using value at risk models, although market returns typically have fat tail distributions.^[12]

Taleb said 'I don't particularly care about the usual. If you want to get an idea of a friend's temperament, ethics, and personal elegance, you need to look at him under the tests of severe circumstances, not under the regular rosy glow of daily life. Can you assess the danger a criminal poses by examining only what he does on an ordinary day? Can we understand health without considering wild diseases and epidemics? Indeed the normal is often irrelevant. Almost everything in social life is produced by rare but consequential shocks and jumps; all the while almost everything studied about social life focuses on the 'normal,' particularly with 'bell curve' methods of inference that tell you close to nothing. Why? Because the bell curve ignores large deviations, cannot handle them, yet makes us confident that we have tamed uncertainty. Its nickname in this book is GIF, Great Intellectual Fraud.'

More generally, decision theory, which is based on a fixed universe or a model of possible outcomes, ignores and minimizes the effect of events that are 'outside the model'. For instance, a simple model of daily stock market returns may include extreme moves such as Black Monday (1987), but might not model the breakdown of markets following the 9/11 attacks. A fixed model considers the 'known unknowns', but ignores the 'unknown unknowns', made famous by a statement of Donald Rumsfeld.^[13] The term 'unknown unknowns' appeared in a 1982 New Yorker article on the aerospace industry, which cites the example of metal fatigue, the cause of crashes in Comet airliners in the 1950s.^[14]

Taleb notes that other distributions are not usable with precision, but often are more descriptive, such as the fractal, power law, or scalable distributions and that awareness of these might help to temper expectations.^[15]

Beyond this, he emphasizes that many events simply are without precedent, undercutting the basis of this type of reasoning altogether.

Taleb also argues for the use of counterfactual reasoning when considering risk.^{[7]:p. xvii[16]}

See also[edit]

References[edit]

1. ^ ^abcTaleb, Nassim Nicholas (2010) [2007]. The Black Swan: the impact of the highly improbable (2nd ed.). London: Penguin. ISBN978-0-14103459-1. Retrieved 23 May 2012.
2. ^ ^abTaleb, Nassim Nicholas (2015), Doing Statistics Under Fat Tails: The Program, retrieved 20 January 2016
3. ^Puhvel, Jaan (Summer 1984). 'The Origin of Etruscan tusna ('Swan')'. The American Journal of Philology. Johns Hopkins University Press. 105 (2): 209–212. doi:10.2307/294875. JSTOR294875.
4. ^Taleb, Nassim Nicholas. 'Opacity'. Fooled by randomness. Retrieved 20 January 2016.
5. ^'Black Swan Unique to Western Australia', Parliament, AU: Curriculum, archived from the original on 13 September 2009.
6. ^Hammond, Peter (October 2009), 'Adapting to the entirely unpredictable: black swans, fat tails, aberrant events, and hubristic models', WERI Bulletin, UK: Warwick (1), retrieved 20 January 2016
7. ^ ^abTaleb, Nassim Nicholas (22 April 2007). 'The Black Swan: Chapter 1: The Impact of the Highly Improbable'. The New York Times. Retrieved 20 January 2016.
8. ^Taleb, Nassim Nicholas (7 April 2009), Ten Principles for a Black Swan Robust World(PDF), Fooled by randomness, retrieved 20 January 2016
9. ^Webb, Allen (December 2008). 'Taking improbable events seriously: An interview with the author of The Black Swan (Corporate Finance)'(PDF). McKinsey Quarterly. McKinsey. p. 3. Archived from the original(Interview; PDF) on 7 September 2012. Retrieved 23 May 2012. Taleb: In fact, I tried in The Black Swan to turn a lot of black swans white! That’s why I kept going on and on against financial theories, financial-risk managers, and people who do quantitative finance.
10. ^Taleb, Nassim Nicholas (September 2008), The Fourth Quadrant: A Map of the Limits of Statistics, Third Culture, The Edge Foundation, retrieved 23 May 2012
11. ^Taleb, Nassim Nicholas (April 2007). The Black Swan: The Impact of the Highly Improbable (1st ed.). London: Penguin. p. 400. ISBN1-84614045-5. Retrieved 23 May 2012.
12. ^Trevir Nath, 'Fat Tail Risk: What It Means and Why You Should Be Aware Of It', NASDAQ, 2015
13. ^DoD News Briefing - Secretary Rumsfeld and Gen. Myer, February 12, 2002 11:30 AM EDTArchived 3 September 2014 at the Wayback Machine
14. ^Newhouse, J. (14 June 1982), 'A reporter at large: a sporty game: i-betting the company', The New Yorker, pp. 48–105
15. ^Gelman, Andrew (April 2007). 'Nassim Taleb's 'The Black Swan''. Statistical Modeling, Causal Inference, and Social Science. Columbia University. Retrieved 23 May 2012.
16. ^Gangahar, Anuj (16 April 2008). 'Market Risk: Mispriced risk tests market faith in a prized formula'. The Financial Times. New York. Archived from the original on 20 April 2008. Retrieved 23 May 2012.

Bibliography[edit]

• Taleb, Nassim Nicholas (2010) [2007], The Black Swan: the impact of the highly improbable (2nd ed.), London: Penguin, ISBN978-0-14103459-1, retrieved 26 February 2017.
• Taleb, Nassim Nicholas (September 2008), 'The Fourth Quadrant: A Map of the Limits of Statistics', Third Culture, The Edge Foundation, retrieved 23 May 2012.

External links[edit]

• David, Dr. Gil, Black Swans in the Cyber Domain, Israel defense, archived from the original on 31 October 2012 No content .
• McGee, Suzanne (5 December 2012), Black Swan Stocks Could Make Your Portfolio a Turkey, Fiscal Times, CNBC, retrieved 20 January 2016.