论文标题
更新模棱两可信息的理论
A Theory of Updating Ambiguous Information
论文作者
论文摘要
我们引入了一个新的更新规则，即有条件的最大似然规则（CML），以更新模棱两可的信息。 CML公式用贝叶斯规则中的可能性术语代替了在状态下给定信号的最大可能性。我们表明，CML可以满足新的公理，更新后的灵敏度提高，而其他更新规则则不满足。使用CML，决策者的后验不受独立信号到达的顺序影响。 CML还适用于最新的实验发现，以更新准确性的信号，并可以对使用此类信号进行学习的简单预测。我们表明，每当代理商根据CML进行更新时，信息设计师几乎可以通过合适的歧义信息结构来实现她的最大回报。
We introduce a new updating rule, the conditional maximum likelihood rule (CML) for updating ambiguous information. The CML formula replaces the likelihood term in Bayes' rule with the maximal likelihood of the given signal conditional on the state. We show that CML satisfies a new axiom, increased sensitivity after updating, while other updating rules do not. With CML, a decision maker's posterior is unaffected by the order in which independent signals arrive. CML also accommodates recent experimental findings on updating signals of unknown accuracy and has simple predictions on learning with such signals. We show that an information designer can almost achieve her maximal payoff with a suitable ambiguous information structure whenever the agent updates according to CML.
