Persuasion under ambiguity

This paper introduces a receiver who perceives ambiguity in a binary model of Bayesian persuasion. The sender has a well-defined prior, while the receiver considers an interval of priors and maximizes a convex combination of worst and best expected payoffs ( $$\alpha $$ α -maxmin preferences). We characterize the sender’s optimal signal and find that the receiver’s payoff differences across states given each action (sensitivities), play a fundamental role in the characterization and the comparative statics. If the sender’s preferred action is the least (most) sensitive one, then the sender’s equilibrium payoff, as well as the sender’s preferred degree of receiver ambiguity, is increasing (decreasing) in the receiver’s pessimism. We document a tendency for ambiguity-sensitive receivers to be more difficult to persuade.