AI RESEARCH
Prior-Agnostic Robust Forecast Aggregation
arXiv CS.LG
•
ArXi:2604.24517v1 Announce Type: new Robust forecast aggregation combines the predictions of multiple information sources to perform well in the worst case across all possible information structures. Previous work largely focuses on settings with a known binary state space, where the state is either 0 or 1. We study prior-agnostic robust forecast aggregation in which the aggregator observes only experts' reports, yet is ignorant of both the underlying joint information structure and the full prior, including the underlying state space.