Cluster-Weighted Aggregation

We are interested in aggregating forecasts of multinomial problems elicited from multiple experts. A common approach is to assign a weight to each expert, then form a weighted sum over their forecasts. Theoretical studies suggest that an important factor in such weighting is the diversity among experts. However, diversity is intrinsically a pairwise measure over experts, and does not lend itself naturally to a single weight that can be applied to an expert’s forecast in a weighted average. We suggest a way to take advantage of such pairwise measures in aggregating forecasts.