The one percent of the one percent of the population is vastly more sensitive to inequality than total GDP growth (which explains why the superrich are doing well now, and should do better under globalization, and why it is a segment that doesn’t correlate well with the economy). For the super-rich, one point of GINI causes an increase equivalent to 6-10% increase in total income (say, GDP). More generally, the partial expectation in the tail is vastly more sensitive to changes in scale of the distribution than in its centering.
Sellers of luxury goods and products for the superwealthy profit from dispersion more than increase in total wealth or income. I looked at their case as a long optionality, benefit-from-volatility type of industry.
Another business that does not care about the average but rather the dispersion around the average is the luxury goods industry—jewelry, watches, art, expensive apartments in fancy locations, expensive collec – tor wines, gourmet farm – raised probiotic dog food, etc. Such businesses only cares about the pool of funds available to the very rich. If the population in the Western world had an average income of fifty thousand dollars, with no inequality at all, the luxury goods sellers would not survive. But if the average stays the same, with a high degree of inequality, with some incomes higher than two million dollars, and potentially some incomes higher than ten million, then the business has plenty of customers—even if such high incomes were offset with masses of people with lower incomes. The “tails” of the distribution on the higher end of the income brackets, the extreme, are much more determined by changes in inequality than changes in the average. It gains from dispersion, hence is antifragile.
This explains the bubble in real estate prices in Central London, determined by inequality in Russia and the Arabian Gulf and totally independent of the real estate dynamics in Britain. Some apartments, those for the very rich, sell for twenty times the average per square foot of a building a few blocks away.
Harvard’ s former president Larry Summers got in trouble explaining a version of the point and lost his job in the aftermath of the uproar. He was trying to say that males and females have equal intelligence, but the male population has more variations and dispersion (hence volatility), with more highly unintelligent men, and more highly intelligent ones. For Summers, this explained why men were overrepresented in the sci – entific and intellectual community (and also why men were overrepre – sented in jails or failures). The number of successful scientists depends on the “tails,” the extremes, rather than the average. Just as an option does not care about the adverse outcomes, or an author does not care about the haters.
A reader has sent in a copy of Nassim’s Lecture notes from when he was teaching a course at the University of Massachusetts, Amherst, MA in 2005. The course/lecture series are titled: Randomness, Decisions, and Human Nature (SOM 797R – SYLLABUS).
Unfortunately all the links within the PDF are missing, if anyone has a copy with all the working links to studies, research papers, books, articles, images, etc, please let us know!
A spurious tail is the performance of a certain number of operators that is entirely caused by luck, what is called the “lucky fool” in Taleb (2001). Because of winner-take-all-effects (from globalization), spurious performance increases with time and explodes under fat tails in alarming proportions. An operator starting today, no matter his skill level, and ability to predict prices, will be outcompeted by the spurious tail. This paper shows the effect of powerlaw distributions on such spurious tail.
The Green Lumber Problem, outlined in Nassim Taleb’s upcoming book Antifragile, is essentially misunderstanding which facts are relevant vs those which are not in regards decision making under uncertainty.
“In one of the rare noncharlatanic books in finance, descriptively called What I Learned Losing A Million Dollars, the protagonist makes a big discovery. He remarks that a fellow called Joe Siegel, the most active trader in a commodity called “green lumber” actually thought that it was lumber painted green (rather than freshly cut lumber, called green because it had not been dried). And he made a living, even a fortune trading the stuff! Meanwhile the narrator was into theories of what caused the price of commodities to move and went bust.
The fact is that predicting the orderflow in lumber and the price dynamics narrative had little to do with these details —not the same ting. Floor traders are selected in the most nonnarrative manner, just by evolution in the sense that nice arguments don’t make much difference.”
It covers his typical Black Swan message that we cannot predict these certain class of rare but consequential events and so we need to become robust to them rather then relying on forecasts. The economic models that we use to calculate the probability of these rare events needs to go out the window.
“What goes out of the window? The entire discipline of modern finance and portfolio theory (the theories named after Harry Markowitz, William Sharpe, Merton Miller), the model-based methods of Paul Samuelson, much of time series econometrics (which don’t appear to predict anything), along with papers and theories that are based on “optimization.” These bring fragility into the system.”