Algorithmic Persuasion Through Simulation: Information Design in the Age of Generative AI

2023-11-29

arXiv.org

AI Summary

This paper studies algorithmic persuasion under generative AI, where a sender aims to influence a receiver’s binary action via signaling, knowing only limited information about the receiver’s type distribution and needing to infer the type with minimal queries to a behavioral simulation oracle. Method: We integrate Bayesian persuasion with a queryable behavioral simulation oracle, proposing a polynomial-time joint optimization algorithm for optimal querying and signal design. Contribution/Results: We fully characterize the optimal signaling strategy for arbitrary type distributions; establish robustness under approximate oracles, general query structures, and cost-sensitive constraints; and empirically demonstrate significant improvements in both utility maximization and query efficiency.

Abstract

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 (real-valued) state of the world, such as the quality of the product, 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.

Problem

• Bayesian persuasion game
• sender-receiver interaction
• optimal messaging policy

Innovation

• Bayesian persuasion game simulation
• Polynomial-time querying algorithm
• Optimized messaging policy design