How can an informed sender persuade a receiver, having only limited
information about the receiver's beliefs? Motivated by research showing
generative AI can simulate economic agents, we initiate the study of
information design with an oracle. We assume the sender can learn more about
the receiver by querying this oracle, e.g., by simulating the receiver's
behavior. Aside from AI motivations such as general-purpose Large Language
Models (LLMs) and problem-specific machine learning models, alternate
motivations include customer surveys and querying a small pool of live users.
Specifically, we study Bayesian Persuasion where the sender has a
second-order prior over the receiver's beliefs. After a fixed number of queries
to an oracle to refine this prior, the sender commits to an information
structure. Upon receiving the message, the receiver takes a payoff-relevant
action maximizing her expected utility given her posterior beliefs. We design
polynomial-time querying algorithms that optimize the sender's expected utility
in this Bayesian Persuasion game. As a technical contribution, we show that
queries form partitions of the space of receiver beliefs that can be used to
quantify the sender's knowledge.

通过使用 oracle 查询来学习有关接收者的信仰，我们研究了信息设计与贝叶斯说服问题，设计了多项式时间查询算法来优化发送者的预期效用。