Multi-Sender Persuasion: A Computational Perspective
This addresses a foundational problem in computational economics and multi-agent systems, with incremental improvements in method design.
The paper tackles the multi-sender persuasion problem, proving that finding equilibria is computationally hard (PPAD-Hard and NP-Hard) and proposing a novel differentiable neural network with the extra-gradient algorithm to discover local equilibria that Pareto dominate full-revelation and existing methods.
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.