Censorship as optimal persuasion
研究了发送者效用仅依赖于期望状态时的贝叶斯说服问题，发现当边际效用拟凹时，将高于阈值状态合并的审查策略对所有先验分布最优，并分析了风险厌恶和效用左移对信息揭示的影响，最后应用于政府媒体审查问题。
We consider a Bayesian persuasion problem where a sender's utility depends only on the expected state. We show that upper censorship that pools the states above a cutoff and reveals the states below the cutoff is optimal for all prior distributions of the state if and only if the sender's marginal utility is quasi‐concave. Moreover, we show that it is optimal to reveal less information if the sender becomes more risk averse or the sender's utility shifts to the left. Finally, we apply our results to the problem of media censorship by a government.