2023 AAAI AAAI 2023

Mediated Cheap Talk Design

Abstract

Abstract We study an information design problem with two informed senders and a receiver in which, in contrast to traditional Bayesian persuasion settings, senders do not have commitment power. In our setting, a trusted mediator/platform gathers data from the senders and recommends the receiver which action to play. We characterize the set of feasible action distributions that can be obtained in equilibrium, and provide an O(n log n) algorithm (where n is the number of states) that computes the optimal equilibrium for the senders. Additionally, we show that the optimal equilibrium for the receiver can be obtained by a simple revelation mechanism.

🧭 Keyword Pioneer — cheap talk
🐝 Cross-Pollinator — Artificial Intelligence, Deep Learning, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Reinforcement Learning