Superforecasting: The Art and Science of Prediction

by Philip E. Tetlock

An awareness of irreducible uncertainty is the core of probabilistic thinking, but it’s a tricky thing to measure. To do that, we took advantage of a distinction that philosophers have proposed between “epistemic” and “aleatory” uncertainty. Epistemic uncertainty is something you don’t know but is, at least in theory, knowable. If you wanted to predict the workings of a mystery machine, skilled engineers could, in theory, pry it open and figure it out. Mastering mechanisms is a prototypical clocklike forecasting challenge. Aleatory uncertainty is something you not only don’t know; it is unknowable. No matter how much you want to know whether it will rain in Philadelphia one year from now, no matter how many great meteorologists you consult, you can’t outguess the seasonal averages. You are dealing with an intractably cloud-like problem, with uncertainty that it is impossible, even in theory, to eliminate. Aleatory uncertainty ensures life will always have surprises, regardless of how carefully we plan. Superforecasters grasp this deep truth better than most. When they sense that a question is loaded with irreducible uncertainty—say, a currency-market question—they have learned to be cautious, keeping their initial estimates inside the shades-of-maybe zone between 35% and 65% and moving out tentatively. They know the “cloudier” the outlook, the harder it is to beat that dart-throwing chimpanzee.