Related papers: Multi-Sender Persuasion: A Computational Perspective

Multi-Sender Persuasion: A Computational Perspective

URL: http://arxiv.org/abs/2402.04971v4
Date: Thu, 20 Jun 2024 03:02:39 GMT
Title: Multi-Sender Persuasion: A Computational Perspective
Authors: Safwan Hossain, Tonghan Wang, Tao Lin, Yiling Chen, David C. Parkes, Haifeng Xu,
Abstract summary: We consider the multi-sender persuasion problem. It is ubiquitous in computational economics, multi-agent learning, and machine learning. We propose a novel differentiable neural network to approximate this game's non-linear and discontinuous utilities.
Score: 41.88812114165843
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We consider the multi-sender persuasion problem: multiple players with informational advantage signal to convince a single self-interested actor to take certain actions. This problem generalizes the seminal Bayesian Persuasion framework and is ubiquitous in computational economics, multi-agent learning, and multi-objective machine learning. The core solution concept here is the Nash equilibrium of senders' signaling policies. Theoretically, we prove that finding an equilibrium in general is PPAD-Hard; in fact, even computing a sender's best response is NP-Hard. Given these intrinsic difficulties, we turn to finding local Nash equilibria. We propose a novel differentiable neural network to approximate this game's non-linear and discontinuous utilities. Complementing this with the extra-gradient algorithm, we discover local equilibria that Pareto dominates full-revelation equilibria and those found by existing neural networks. Broadly, our theoretical and empirical contributions are of interest to a large class of economic problems.

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