Estimating Diversity among Forecaster Models

There is strong theoretical evidence that aggregation of human judgments should not simply average multiple forecasts together (the unweighted linear opinion pool, or ULinOP), but weight them in such a way as to insure representation of a maximally diverse set of models among the experts from whom they are elicited. Explicitly eliciting these models places a major burden on the experts. We report on a variety of approaches to estimating these models, or at least the diversity among them, with minimal explicit input from the experts.