2024/12/03 by Lionel Levine, Hess, Rowan, Levine, Lionel
Computer Science · #Cognitive Science and Mapping #FOS: Mathematics #Probability (math.PR)
paper · pdf · doi:10.48550/arxiv.2412.02777
openalex publication_date 2024/12/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Given conflicting probability estimates for a set of events, how can we quantify how much they conflict? How can we find a single probability distribution that best encapsulates the given estimates? One approach is to minimize a loss function such as binary KL-divergence that quantifies the dissimilarity between the given estimates and the candidate probability distribution. Given a set of events, we characterize the facets of the polytope of coherent probability estimates about those events. We explore two applications of these ideas: eliciting the beliefs of large language models, and merging expert forecasts into a single coherent forecast.