Multi-Sender Persuasion: A Computational Perspective
<|reference_start|>Multi-Sender Persuasion: A Computational Perspective: 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.<|reference_end|>
@article{hossain2024multi-sender,
    title={Multi-Sender Persuasion: A Computational Perspective},
    author={Safwan Hossain, Tonghan Wang, Tao Lin, Yiling Chen, David C. Parkes,
  Haifeng Xu},
    journal={arXiv preprint arXiv:2402.04971},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.04971},
    primaryClass={cs.AI cs.GT}
}