  We study a Bayesian persuasion game where a sender wants to persuade a
receiver to take a binary action, such as purchasing a product. The sender is
informed about the (binary) state of the world, such as whether the quality of
the product is high or low, but only has limited information about the
receiver's beliefs and utilities. Motivated by customer surveys, user studies,
and recent advances in AI, we allow the sender to learn more about the receiver
by querying an oracle that simulates the receiver's behavior. After a fixed
number of queries, the sender commits to a messaging policy and the receiver
takes the action that maximizes her expected utility given the message she
receives. We characterize the sender's optimal messaging policy given any
distribution over receiver types. We then design a polynomial-time querying
algorithm that optimizes the sender's expected utility in this game. We also
consider approximate oracles, more general query structures, and costly
queries.
