Multi-Sender Persuasion – A Computational Perspective
CoRR(2024)
摘要
We consider multiple senders with informational advantage signaling to
convince a single self-interested actor towards certain actions. Generalizing
the seminal Bayesian Persuasion framework, such settings are ubiquitous in
computational economics, multi-agent learning, and machine learning with
multiple objectives. 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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