Preparing for the Worst but Hoping for the Best: Robust (Bayesian) Persuasion
研究发送者在担心自身信念可能错误时，如何设计信息策略以在最坏情况下仍能获得最大收益，并比较了稳健策略与标准贝叶斯说服的差异。
We propose a robust solution concept for Bayesian persuasion that accounts for the Sender's concern that her Bayesian belief about the environment—which we call the conjecture —may be false. Specifically, the Sender is uncertain about the exogenous sources of information the Receivers may learn from, and about strategy selection. She first identifies all information policies that yield the largest payoff in the “worst‐case scenario,” that is, when Nature provides information and coordinates the Receivers' play to minimize the Sender's payoff. Then she uses the conjecture to pick the optimal policy among the worst‐case optimal ones. We characterize properties of robust solutions, identify conditions under which robustness requires separation of certain states, and qualify in what sense robustness calls for more information disclosure than standard Bayesian persuasion. Finally, we discuss how some of the results in the Bayesian persuasion literature change once robustness is accounted for, and develop a few new applications.