Optimal Forecasting Groups
研究了如何组合预测者形成最优群体，推导出群体最优构成取决于预测准确性、类型内和类型间协方差以及群体规模，小群体应多数为最准确类型，大群体应多数为类型内协方差最小的类型。
This paper characterizes the optimal composition of a group for making a combined forecast. In the model, individual forecasters have types defined according to a statistical criterion we call type coherence. Members of the same type have identical expected accuracy, and forecasters within a type have higher covariance than forecasters of different types. We derive the optimal group composition as a function of predictive accuracy, between- and within-type covariance, and group size. Group size plays a critical role in determining the optimal group: in small groups the most accurate type should be in the majority, whereas in large groups the type with the least within-type covariance should dominate. This paper was accepted by Peter Wakker, decision analysis.