Revenue Maximization Mechanisms for an Uninformed Mediator with Communication Abilities

Revenue Maximization Mechanisms for an Uninformed Mediator with Communication Abilities

Zhikang Fan, Weiran Shen

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
Main Track. Pages 2693-2700. https://doi.org/10.24963/ijcai.2023/300

Consider a market where a seller owns an item for sale and a buyer wants to purchase it. Each player has private information, known as their type. It can be costly and difficult for the players to reach an agreement through direct communication. However, with a mediator as a trusted third party, both players can communicate privately with the mediator without worrying about leaking too much or too little information. The mediator can design and commit to a multi-round communication protocol for both players, in which they update their beliefs about the other player's type. The mediator cannot force the players to trade but can influence their behaviors by sending messages to them. We study the problem of designing revenue-maximizing mechanisms for the mediator. We show that the mediator can, without loss of generality, focus on a set of direct and incentive-compatible mechanisms. We then formulate this problem as a mathematical program and provide an optimal solution in closed form under a regularity condition. Our mechanism is simple and has a threshold structure. We also discuss some interesting properties of the optimal mechanism, such as situations where the mediator may lose money.
Keywords:
Game Theory and Economic Paradigms: GTEP: Mechanism design
Game Theory and Economic Paradigms: GTEP: Auctions and market-based systems
Multidisciplinary Topics and Applications: MDA: Economics