Optimal Reporting of Predictions
研究了在多位专家向决策者报告预测分布时，如何通过选择报告策略来最大化自身后验权重，并给出了始终诚实报告为最优策略的效用函数条件。
Abstract Consider a problem in which you and a group of other experts must report your individual predictive distributions for an observable random variable X to some decision maker. Suppose that the report of each expert is assigned a prior weight by the decision maker and that these weights are then updated based on the observed value of X. In this situation you will try to maximize your updated, or posterior, weight by appropriately choosing the distribution that you report, rather than necessarily simply reporting your honest predictive distribution. We study optimal reporting strategies under various conditions regarding your knowledge and beliefs about X and the reports of the other experts, and under various utility functions for your posterior weight. We present the only utility functions for which it is always optimal to report your honest predictive distribution. Attention is restricted to problems in which X can take only a finite number of values.