Preparing for the Worst But Hoping for the Best: Robust (Bayesian) Persuasion

More on Piotr Dworczak

Abstract: 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 equilibrium selection. Thus, she first identifies all information policies that yield the largest payoff in the "worst-case scenario," i.e., 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.

Joint with Alessandro Pavan

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