To Interfere or Not To Interfere: Information Revelation and Price-Setting Incentives in a Multiagent Learning Environment

Abstract

Demand uncertainty and seller competition are substantial challenges for online platforms. In “To Interfere or Not To Interfere: Information Revelation and Price-Setting Incentives in a Multiagent Learning Environment,” Birge, Chen, Keskin, and Ward analyze whether and how an online platform should offer demand information or price incentives to the sellers participating on the platform. The authors show that, when facing uncertain demand, the platform could be better off by doing nothing—that is, not providing any information or incentives to the sellers. They also develop a strategic reveal-and-incentivize policy for the platform to choose when to start sharing information and offering rewards to coordinate the sellers’ pricing. They prove that the strategic reveal-and-incentivize policy achieves near-optimal profit performance for the platform.