Forecasting Round-Up

I don’t usually post lists of links, but the flurry of great material on forecasting that hit my screen over the past few days is inspiring me to make an exception. Here in no particular order are several recent pieces that deserve wide reading:

• “The Weatherman Is Not a Moron.” Excerpted from a forthcoming book by the New York Times’ Nate Silver, this piece deftly uses meteorology to illustrate the difficulties of forecasting in complex systems and some of the ways working forecasters deal with them. For a fantastic intellectual history on the development of the ensemble forecasting approach Silver discusses, see this July 2005 journal article by John Lewis in the American Meteorological Society’s Monthly Weather Review.

• “Trending Upward.” Michael Horowitz and Phil Tetlock write for Foreign Policy about how the U.S. “intelligence community” can improve its long-term forecasting. The authors focus on the National Intelligence Council’s Global Trends series, which attempts the Herculean (or maybe Sisyphean) feat of trying to peer 15 years into the future, but the recommendations they offer apply to most forecasting exercises that rely on expert judgment. And, on the Duck of Minerva blog, John Western pushes back: “I think there is utility in long-range forecasting exercises, I’m just not sure I see any real benefits from improved accuracy on the margins. There may actually be some downsides.” [Disclosure: Since this summer, I have been a member of Tetlock and Horowitz’s team in the IARPA-funded forecasting competition they mention in the article.]

• “Theories, Models, and the Future of Science.” This post by Ashutosh Jogalekar on Scientific American‘s Curious Waveform blog argues that “modeling and simulation are starting to be considered as a respectable ‘third leg’ of science, in addition to theory and experiment.” Why? Because “many of science’s greatest current challenges may not be amenable to rigorous theorizing, and we may have to treat models of phenomena as independent, authoritative explanatory entities in their own right.” Like Trey Causey, who pointed me toward this piece on Twitter, I think the post draws a sharper distinction between modeling for simulation and explanation than it needs to, but it’s a usefully provocative read.

• “The Probabilities of Large Terrorist Events.” I recently finished Nassim Nicholas Taleb’s Black Swan and was looking around for worked examples applying that book’s idea of “fractal randomness” to topics I study. Voilà! On Friday, Wired‘s Social Dimensions blog spotlighted a recent paper by Aaron Clauset and Ryan Woodward that uses a mix of statistical techniques, including power-law models, to estimate the risk of this particular low-probability, high-impact political event. Their approach—model only the tail of the distribution and use an ensemble approach like the aforementioned meteorologists do—seems really clever to me, and I like how they are transparent about the uncertainty of the resulting estimates.

Posted by Jay Ulfelder on September 10, 2012

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