Black swan theory

For Taleb's book on the subject, see The Black Swan.

The Black Swan (Cygnus atratus)

The Black Swan theory (in Nassim Nicholas Taleb 's version) refers to a large-impact, hard-to-predict, and rare event beyond the realm of normal expectations. Unlike the philosophical "black swan problem", the "Black Swan" theory (capitalized) refers only to events of large consequence and their dominant role in history.

Background

The theory was described by Nassim Nicholas Taleb in his 2007 book The Black Swan. Taleb regards many scientific discoveries as black swans—"undirected" and unpredicted. He gives the rise of the Internet, the personal computer, World War I, as well as the September 11, 2001 attacks as examples of Black Swan events.^[1]

The term black swan comes from the commonplace Western cultural assumption that 'All swans are white'. In that context, a black swan was a metaphor for something that could not exist. The 17th Century discovery of black swans in Australia metamorphosed the term to connote that the perceived impossibility actually came to pass. Taleb notes that John Stuart Mill first used the black swan narrative to discuss falsification.

Non-philosophical epistemological approach

Taleb's black swan is different from the earlier (philosophical) versions of the problem as it concerns a phenomenon with specific empirical/statistical properties which he calls "the fourth quadrant".^[2] Before Taleb, those who dealt with the notion of improbable, like Hume, Mill and Popper, focused on the problem of induction in logic, specifically that of drawing general conclusions from specific observations. Taleb's Black Swan has a central and unique attribute: the high impact. His claim is that almost all consequential events in history come from the unexpected—while humans convince themselves that these events are explainable in hindsight (bias).

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 can be predicted by extrapolating from variations in statistics based on past observations, especially when these statistics are assumed to represent samples from a bell curve. These concerns are often highly relevant to financial markets, where major players use value at risk models (which imply normal distributions) but market movements have fat tails.

Taleb notes that other functions are often more descriptive, such as the fractal, power law, or scalable distributions; awareness of these might help to temper expectations.^[3] Beyond this, he emphasizes that many events are simply without precedent, undercutting the basis of this sort of reasoning altogether. Taleb also argues for the use of counterfactual reasoning when considering risk.^[4][5]

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