Tonight’s episode seems more of a “staging” episode for future episodes. While I ponder the different sub-plots that went on tonight, I will provide a little bit of discussion on Black Swan Theory. Check in tomorrow for my synopsis of tonight’s episode.
Black Swan Theory
The Black Swan Theory (in Nassim Nicholas Taleb’s version) concerns high-impact, hard-to-predict, and rare events beyond the realm of normal expectations. Unlike the philosophical “black swan problem“, the “Black Swan Theory” (capitalized) refers only to events of large magnitude and consequence and their dominant role in history. “Black Swan” events are considered extreme outliers. Note that in his writings Taleb never uses the phrase “Black Swan Theory”; instead, he refers to “Black Swan Events” (capitalized).
The theory was described by Nassim Nicholas Taleb in his 2007 book The Black Swan. 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, and the September 11, 2001 attacks as examples of Black Swan events.
The term Black Swan comes from the 17th century European assumption that ‘All swans are white‘. In that context, a black swan was a symbol for something that was impossible or could not exist. In the 18th Century, the discovery of black swans in Western Australia metamorphosed the term to connote that a perceived impossibility may actually come to pass. Taleb notes that John Stuart Mill first used the Black Swan narrative to discuss falsification.
The main idea in Taleb’s book is not to attempt to predict Black Swan events, but to build robustness to the negative ones, while being able to exploit positive ones. Taleb contends that banks and trading firms are very vulnerable to hazardous Black Swan events and are exposed to losses beyond that predicted by their defective models.
Coping with Black Swan events
Taleb states that a Black Swan event depends on the observer—a Black Swan surprise for the turkey is not a Black Swan surprise for the butcher, hence his idea is to “avoid being the turkey” by finding out where one may be exposed to being a turkey and “turn the Black Swans white”.
Identifying a Black Swan event
Based on the author’s criteria:
The event is a surprise (to the observer).
The event has a major impact.
After the fact, the event is rationalized by hindsight, as if it had been expected.
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”. Before Taleb, those who dealt with the notion of the improbable, like David Hume, John Stuart Mill and Karl 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 in financial markets, where major players use value at risk models (which imply normal distributions) but market return distributions have fat tails.
More generally, decision theory based on a fixed universe or model of possible outcomes ignores and minimizes the impact of events which are “outside 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 market breakdowns following the September 11 attacks. A fixed model considers the “known unknowns”, but ignores the “unknown unknowns”.
Taleb notes that other distributions are not usable with precision, but often more descriptive, such as the fractal, power law, or scalable distributions; awareness of these might help to temper expectations. Beyond this, he emphasizes that many events are simply without precedent, undercutting the basis of this type of reasoning altogether. Taleb also argues for the use of counterfactual reasoning when considering risk.