Black Swan

Topics: Nassim Nicholas Taleb, Black swan theory, Ludic fallacy Pages: 8 (3189 words) Published: February 9, 2013
The Innovation Journal: The Public Sector Innovation Journal, Volume 13(3), 2008, article 14. 1

Book Review Nassim Nicholas Taleb. The Black Swan. The Impact of the Highly Improbable. New York: Random House, 2007. Reviewed by James Iain Gow Université de Montréal, Canada This book has had quite an impact since it was published in 2007. According to Wikipedia, it has sold over 270, 000 copies in its first year, was on the New York Times best-seller list for 17 week and had been translated into 27 languages. It is being reviewed here since I believe it directly engages those of us who take an interest in public administration and public sector innovation. Taleb is a multi-talented person, who has divided his time between the practice of investment and the science and psychology of forecasting and decision-making. He is professor in the sciences of uncertainty at the University of Massachusetts, Amherst. His book is written in an open and accessible style, passing from more general, philosophical and psychological considerations to a technical thesis attacking the usefulness of the statistical bell-curve (when the book becomes very technical the author invites the general reader to skip-over to the next not-so-technical section). Intended also for academics and people already learned in the field, with 18 pages of notes and 27 of bibliography drawn from philosophy, literature, social science and finance, it is a serious study dressed up as a best seller. There are two main branches to Taleb’s argument. First, he argues that there is a lot more randomness than we generally admit. Second, we have trouble dealing with randomness because of the way we think. The “Black Swan” of the title is the one that was discovered to be present in Australia when previously all observations demonstrated that swans were white. This example is used to show that no amount of induction can ever lead to certainty. A single exception invalidates the rule. For Taleb, a Black Swan has three characteristics: it is an outlier (very improbable); has an extreme impact; and humans create doubtful explanations after it has happened (p. xviii). Examples are the destruction of the “Lebanese paradise” after 1975, the stock market crash of 1987, the breakup of the USSR, 9/11, and the development of the internet. Some Black Swans are fortunate, such as an unexpected monster success of a book, film or record or the luck of the investor who benefits hugely from an unforeseen market swing. In the author’s view, the modern world is dominated by Black Swans, not that there are more of them than in the past, but that their consequences are more extreme. People in general, and social scientists in particular, do not recognize the existence of the realities that Taleb calls Extremistan and Mediocristan (p. 36). Mediocristan is the dull, predictable world that most of us live in most of the time. Jobs in it are rewarded in some relation to the amount of time, effort and skill that earners put into them; extremes of income have little effect on averages; some kind of equality is possible. In Extremistan, 1

The Innovation Journal: The Public Sector Innovation Journal, Volume 13(3), 2008, article 14. 2

rewards have no relation to time, effort and skill, but are based on luck. Payoffs are huge, but there are few big winners and many losers. As a result, there is no typical member but increasing inequality. The trouble comes from the refusal of the social sciences to admit the existence of Extremistan and the increasing importance of outliers. Social scientist and many “experts” overestimate what they know, whereas what they don’t know is far more important. They create the illusion of understanding, relying on “the beastly method of collecting selective corroborating evidence” or the thoughts of dead thinkers (p. xxvii). The origin of the problem lies in how we think. We indulge in story-telling, the “narrative fallacy”, thinking that the world is less random than...
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