Algorithmic Persuasion Through Simulation
CoRR(2023)
摘要
We study a Bayesian persuasion problem 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 generative 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
Bayesian persuasion game. We also consider approximate oracles, more general
query structures, and costly queries.
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