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Nassim Taleb's Article from: Risk Management March 2010 – Issue 18 CHAIRSPERSON’SGENERAL CORNER Should Actuaries Get Another Job? Nassim Taleb’s work and it’s significance for actuaries By Alan Mills INTRODUCTION Nassim Nicholas Taleb is not kind to forecasters. In fact, Perhaps we should pay attention he states—with characteristic candor—that forecasters are little better than “fools or liars,” that they “can cause “Taleb has changed the way many people think more damage to society than criminals,” and that they about uncertainty, particularly in the financial mar- should “get another job.”[1] Because much of actuarial kets. His book, The Black Swan, is an original and work involves forecasting, this article examines Taleb’s audacious analysis of the ways in which humans try assertions in detail, the justifications for them, and their to make sense of unexpected events.” significance for actuaries. Most importantly, I will submit Danel Kahneman, Nobel Laureate that, rather than search for other employment, perhaps we Foreign Policy July/August 2008 should approach Taleb’s work as a challenge to improve our work as actuaries. I conclude this article with sug- “I think Taleb is the real thing. … [he] rightly under- gestions for how we might incorporate Taleb’s ideas in stands that what’s brought the global banking sys- our work. tem to its knees isn’t simply greed or wickedness, but—and this is far more frightening—intellectual Drawing on Taleb’s books, articles, presentations and hubris.” interviews, this article distills the results of his work that John Gray, British philosopher apply to actuaries. Because his focus is the finance sector, Quoted by Will Self in Nassim Taleb and not specifically insurance or pensions, the comments GQ May 2009 in this article relating to actuarial work are mine and not Taleb’s. Indeed, in his work Taleb only mentions actuar- “Taleb is now the hottest thinker in the world. ies once, as a model for the wrong kind of forecaster (the … with two books—Fooled by Randomness: The pathetic Dr. John in The Black Swan). Concerning insur- Hidden Role of Chance in the Markets and in Life, ance and pensions, in Fooled by Randomness, he writes and The Black Swan—and a stream of academic derisively, “… pension funds and insurance companies papers, he turned himself into one of the giants of in the United States and in Europe somehow bought the modern thought.” argument that ‘in the long term equities always pay off Brian Appleyard 9%’ and back it up with statistics.” We may safely con- The Sunday Times June 1, 2008 clude that actuaries are not Taleb’s heroes. Be forewarned: it is not easy to reach the germ of Taleb’s ideas, partly because Taleb himself—and, by extension, mer Wall Street trader special- his writing—is unusually multilayered, complex, and, izing in derivatives, as well as Alan Mills, FSA, MAAA, ND, is a yes, entertaining. Perhaps more importantly, though, it is a polyglot (but because he was family practice physician. He can not easy to communicate paradigm-shifting ideas. As one born in Lebanon, and grew up be reached at Alan.Mills@ critic stated, “His writing is full of irrelevances, asides partly in France, he is naturally earthlink.net. and colloquialisms, reading like the conversation of a more comfortable in Arabic raconteur rather than a tightly argued thesis.”[2] Since and French than English.) He Taleb says that his hero of heroes is Montaigne, it is hardly characterizes his books The surprising that his style is that of a raconteur, mixing Black Swan and Fooled by autobiographical material, philosophy, narrative fiction, Randomness as literary works, rather than technical and history with science and statistics. Indeed, Taleb calls expositions, and he encourages serious students to read himself a literary essayist and epistemologist.[3] But he is his scholarly works (many of which are referenced on also a researcher, a professor of Risk Analysis, and a for- his Web site, www.FooledByRandomness.com). I concur. CONTINUED ON PAGE 12 Risk management | MARCH 2010 | 11 GENERAL Should Actuaries Get Another Job? | from Page 11 WE ARE SUCKERS Simple payoffs are binary, true or false. For example, to determine headcounts for a population census, it only Taleb’s main point is that our most important financial, matters whether a person is alive or dead. Very alive or political and other social decisions are based on forecasts very dead does not matter. Simple payoffs only depend on that share a fatal flaw, thus leading to disastrous conse- the zeroth moment, the event probability. (In a moment, quences. Or, as he says more concisely, “We are suckers.” we’ll look at the importance of moments.) For complex His contribution is to vividly and vociferously expose this payoffs, frequency and magnitude both matter. Thus, with flaw, and then suggest how to mitigate its negative impact. complex payoffs, there is another layer of uncertainty. Actuarial work typically supports decisions with complex Specifically, Taleb says that forecasts are flawed when payoffs, such as decisions related to medical expenditures, applied to support decisions in the “fourth quadrant.” He life insurance proceeds, property and casualty claims, and divides the decision-making domain into four quadrants, pension payouts. For complex payoffs with linear magni- as shown in Table 1.[4] tudes, payoffs depend on the first moment, whereas for non-linear magnitudes (such as highly-leveraged reinsur- Table 1: Four quadrants of the decision-making ance) higher moments are important. domain U n d e r l y i n g Payoff Borrowing from the work of Benoit Mandelbrot, Taleb divides probability distributions into Type I and Type II (Mandelbrot probability Simple (binary) Complex distribution calls them, respectively, mild chance and wild chance[5]). Type I distributions are thin-tailed distributions common Type I I II to the Gaussian family of probability distributions (normal, (safe) (safe) Poisson, etc.). Type II distributions are fat-tailed distributions Type II III IV (such as Power-law, Pareto, or Lévy distributions). Type (safe) (dangerous) II distributions are commonly found in complex adaptive systems such as social economies, health care systems, and Taleb divides the decision-making domain according to property/casualty disasters (earthquakes, hurricanes, etc.) .[6] whether the decision payoff, or result, is simple or com- Importantly, for fat-tailed distributions, higher moments are plex, and whether the underlying probability distribution often unstable over time, or are undefined; they are wildly (or frequency) of relevant events on which the decision is different from thin-tailed distribution moments. And, for based is Type I or Type II. Type II distributions, the Central Limit Theorem fails: aggre- gations of fat-tailed distributions are often fat-tailed.[4] Figure 1: Type 1 (Gaussian) noise and Type 2 (Power-law) noise 12 | MARCH 2010 | Risk management CHAIRSPERSON’SGENERAL CORNER “Any system susceptible to a Black Swan will eventually blow up.” –Nassim Taleb Figure 1 (on page 12) illustrates the difference between Type 1 and Type 2 distributions. On the left is Type 1 noise (white noise) which is Gaussian distributed. On the The scandal of prediction right is Type 2 noise (typical of electronic signal noise) Writing about forecasting in security analysis, politi- which is Power-law distributed. The striking difference cal science and economics: between the two is that Type 2 noise has one spike of extreme magnitude that dwarfs all other events, and that “I am surprised that so little introspection has been is not predictable. This spike is a Black Swan. Such Type done to check on the usefulness of these profes- 2 patterns are typical of complex adaptive systems. sions. There are a few—but not many—formal tests in three domains: security analysis, political science Thus, the problematic fourth quadrant refers to decision and economics. We will no doubt have more in a few making where payoffs are complex (i.e., not binary) and years. Or perhaps not—the author of such papers underlying probability distributions are fat-tailed and might become stigmatized by his colleagues. Out wild. In this area, according to Taleb, our forecasts fail: of close to a million papers published in politics, they cannot predict events that have massively adverse (or finance and economics, there have been only a small positive) consequences (the Black Swans). Because most number of checks on the predictive quality of such decisions in our world fall squarely in the fourth quadrant, knowledge. … Why don’t we talk about our record most actuarial work supports fourth quadrant decision in predicting? Why don’t we see how we (almost) making and is subject to the forecasting flaw. always miss the big events? I call this the scandal of prediction.” To support his thesis, Taleb cites numerous instances Nassim Taleb when we have been suckers, when dire consequences The Black Swan flowed from our inability to forecast in the fourth quad- rant, among which are the collapse of the Soviet Union, U.S. stock market collapses, and the current financial crisis. He also observes that in the areas of security analy- sis, political science and economics, no one seems to be WHY FORECASTS FAIL checking forecast accuracy (see the sidebar). Taleb gives three interrelated reasons why our fourth quadrant forecasts (and, thus, decisions based on these Although the consequences have not yet been as dra- forecasts) fail: matic as those cited by Taleb, many actuarial forecasts 1. Our minds have significant cognitive biases that cloud are notorious for their inaccuracy. For example, actual our ability to reason accurately. 1990 Medicare costs were 7.39 times higher than origi- 2. We do not understand that our world is increasingly nal projections.[7] More recently, CMS reports that complex and unpredictable.
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