Bayesian Persuasion Is a Bargaining Game

Yue Lin, Shuhui Zhu, William A Cunningham, Wenhao Li, Pascal Poupart, Hongyuan Zha, Baoxiang Wang

Keywords: Bayesian persuasion, bargaining game, anti-exploitation, large language models

TL;DR: Bayesian persuasion is reducible to a bargaining game. Evidence of retaliation is provided by some LLMs experiments.

Abstract: Bayesian persuasion studies how a sender with an informational advantage can persuade a receiver with a different motive to take actions that benefit the sender. This problem is previously formulated from an equilibrium perspective, where the sender is to choose a Bayes correlated equilibrium and the receiver is willing to respect the signaling scheme based on posterior beliefs. However, evidence in real-world scenarios and studies in farsighted receivers suggest otherwise: senders tend to be much more honest than the equilibrium. In this work, we show that Bayesian persuasion is reducible to a bargaining game. This reduction suggests that the receiver in Bayesian persuasion can be aware of the game structure and can develop an anti-exploitation strategy. This equalizes the power of commitment of the two parties and prevents the sender from taking the maximum possible payoff. Through experiments on large language models, we demonstrate the receiver's retaliatory strategies and the sender's compromise to that. More findings on the impact of the context and alignments further suggest that bargaining behavior emerges in persuasion tasks. The insights given by our results have potential implications on various scenarios to reduce exploitation, improve equality, and improve social welfare.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 10752