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

John Birge, Hongfan (Kevin) Chen (), N. Bora Keskin () and Amy Ward ()
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Hongfan (Kevin) Chen: CUHK Business School, Chinese University of Hong Kong, Hong Kong
N. Bora Keskin: Fuqua School of Business, Duke University, Durham, North Carolina 27708
Amy Ward: Booth School of Business, University of Chicago, Chicago, Illinois 60637

Operations Research, 2024, vol. 72, issue 6, 2391-2412

Abstract: 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.

Keywords: Market Analytics and Revenue Management; sharing economy; two-sided platforms; revenue management; pricing; demand learning; sequential estimation; exploration-exploitation; regret (search for similar items in EconPapers)
Date: 2024
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