Regularized Aggregation of One-Off Probability Predictions

Abstract

How much can rational people really disagree? If we can understand the limits of such disagreement, can we remove noise by labeling excess disagreement as irrational and then construct a group belief based on everyone's rational beliefs? Based on this idea, “Regularized Aggregation of One-Off Probability Predictions” by Satopää proposes a Bayesian aggregator that requires no user intervention and can be computed efficiently even for a large number of one-off probability predictions. To illustrate, the aggregator is evaluated on predictions collected during a four-year forecasting tournament sponsored by the U.S. intelligence community. The aggregator improves the squared error (a.k.a., the Brier score) of simple averaging by around 20% and other commonly used aggregators by 10%−25%. This advantage stems almost exclusively from improved calibration. An R package called braggR implements the method and is available on CRAN.