The question becomes, is it possible to set up a system for learning from history that’s not simply programmed to avoid the most recent mistake in a very simple, mechanistic fashion? Is it possible to set up a system for learning from history that actually learns in our sophisticated way that manages to bring down both false positive and false negatives to some degree? That’s a big question mark.
Nobody has really systematically addressed that question until IARPA, the Intelligence Advanced Research Projects Agency, sponsored this particular project, which is very, very ambitious in scale. It’s an attempt to address the question of whether you can push political forecasting closer to what philosophers might call an optimal forecasting frontier. That an optimal forecasting frontier is a frontier along which you just can’t get any better.
PHILIP E. TETLOCK is Annenberg University Professor at the University of Pennsylvania (School of Arts and Sciences and Wharton School). He is author of Expert Political Judgment: How Good Is It? How Can We Know? which describes a twenty-year study in which 284 experts in many fields, including government officials, professors, and journalists and ranging from Marxists to free-marketeers, were asked to make 28,000 predictions about the future. He found they were only slightly more accurate than chance, and worse than simple extrapolation algorithms. The book has received many awards, including the 2006 Woodrow Wilson Award from the American Political Science Association and the 2008 Grawemeyer Award for Ideas Improving World Order.