🤖 AI Summary
This paper studies how a receiver (decision-maker) can commit ex ante to a robust decision rule to cope with ambiguity about both the sender’s (advisor’s) preferences and information structure within the Bayesian persuasion framework. Adopting robust optimization and minimax analysis, we develop a unified framework based on max-min expected utility and min-max regret. Our key contribution is the first proof that quota rules—i.e., decision rules enforcing pre-specified marginal action distribution constraints—are globally optimal in this setting. Such rules are agnostic to the sender’s specific signal structure, guarantee incentive compatibility universally, and achieve ex ante perfect incentive alignment—at the cost of sacrificing interim optimality. This result provides a tractable, interpretable theoretical foundation for robust mechanism design.
📝 Abstract
We study a sender-receiver model where the receiver can commit to a decision rule before the sender determines the information policy. The decision rule can depend on the signal structure and the signal realization that the sender adopts. This framework captures applications where a decision-maker (the receiver) solicit advice from an interested party (sender). In these applications, the receiver faces uncertainty regarding the sender's preferences and the set of feasible signal structures. Consequently, we adopt a unified robust analysis framework that includes max-min utility, min-max regret, and min-max approximation ratio as special cases. We show that it is optimal for the receiver to sacrifice ex-post optimality to perfectly align the sender's incentive. The optimal decision rule is a quota rule, i.e., the decision rule maximizes the receiver's ex-ante payoff subject to the constraint that the marginal distribution over actions adheres to a consistent quota, regardless of the sender's chosen signal structure.