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Algorithmic Persuasion Through Simulation

Author

Listed:
  • Keegan Harris
  • Nicole Immorlica
  • Brendan Lucier
  • Aleksandrs Slivkins

Abstract

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.

Suggested Citation

  • Keegan Harris & Nicole Immorlica & Brendan Lucier & Aleksandrs Slivkins, 2023. "Algorithmic Persuasion Through Simulation," Papers 2311.18138, arXiv.org, revised Apr 2024.
    • Handle: RePEc:arx:papers:2311.18138
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    References listed on IDEAS

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