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

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.

We consider a platform in which multiple sellers offer their products for sale over a time horizon of T periods. Each seller sets its own price. The platform collects a fraction of the sales revenue and provides price-setting incentives to the sellers to maximize its own revenue. The demand for each seller’s product is a function of all sellers’ prices and some customer features. Initially, neither the platform nor the sellers know the demand function, but they can learn about it through sales observations: each seller observes its own sales, whereas the platform observes all sellers’ sales as well as the customer feature information. We measure the platform’s performance by comparing its expected revenue with the full-information optimal revenue, and we design policies that enable the platform to manage information revelation and price-setting incentives. Perhaps surprisingly, a simple “do-nothing” policy does not always exhibit poor revenue performance and can perform exceptionally well under certain conditions. With a more conservative policy that reveals information to make price-setting incentives more effective, the platform can always protect itself from large revenue losses caused by demand model uncertainty. We develop a strategic reveal-and-incentivize policy that combines the benefits of the aforementioned policies and thereby achieves asymptotically optimal revenue performance as T grows large.

Funding: This work was supported by Duke University Fuqua School of Business, University of Chicago Booth School of Business, and CUHK Business School.

Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2023.0363.