Combining Overlapping Information
研究决策者如何聚合来自多个专家的重叠信息以形成后验分布，通过正态和伯努利框架的例子揭示依赖关系对后验分布的影响，对实际信息聚合和专家共识有启示。
A decision maker trying to learn about an uncertain quantity may obtain divergent information from a number of sources (e.g., experts). In this paper we study the decision maker's problem of aggregating this information to form his posterior distribution when he believes that the experts' opinions are dependent due to shared information. Examples in both normal and Bernoulli frameworks lead to some surprising general insights regarding the impact of the dependence on the posterior distribution. The study has implications for practitioners who face real-world information-aggregation problems and for groups of experts who seek a consensus.