Modeling Probability Forecasts via Information Diversity
成果类型：
Article
署名作者：
Satopaa, Ville A.; Pemantle, Robin; Ungar, Lyle H.
署名单位：
INSEAD Business School; University of Pennsylvania; University of Pennsylvania; University of Pennsylvania
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1080/01621459.2015.1100621
发表日期：
2016
页码：
1623-1633
关键词：
摘要：
Randomness in scientific estimation is generally assumed to arise from unmeasured or uncontrolled factors. 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. Our model thus gives a more principled understanding of the historically ad hoc practice of extremizing average forecasts. Supplementary material for this article is available online.