Modeling Probability Forecasts via Information Diversity

However, when combining subjective probability estimates, heterogeneity stemming from people’s cognitive or information diversity is often more important than measurement noise. This article presents a novel framework that uses partially overlapping information sources.

A specific model is proposed within that framework and applied to the task of aggregating the probabilities given by a group of forecasters who predict whether an event will occur or not. Our model describes the distribution of information across forecasters in terms of easily interpretable parameters and shows how the optimal amount of extremizing of the average probability forecast (shifting it closer to its nearest extreme) varies as a function of the forecasters’ information overlap.