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

80 Pages Posted: 11 Jun 2021

See all articles by John R. Birge

John R. Birge

University of Chicago - Booth School of Business

Hongfan Chen

Chinese University of Hong Kong - Business School

N. Bora Keskin

Duke University - Fuqua School of Business

Amy Ward

The University of Chicago Booth School of Business

Date Written: June 8, 2021

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 design policies that enable the platform to judiciously 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: sharing economy, two-sided platforms, pricing, revenue management, sequential estimation, exploration-exploitation, regret

Suggested Citation

Birge, John R. and Chen, Hongfan and Keskin, N. Bora and Ward, Amy, To Interfere or Not To Interfere: Information Revelation and Price-Setting Incentives in a Multiagent Learning Environment (June 8, 2021). Available at SSRN: https://ssrn.com/abstract=3864227 or http://dx.doi.org/10.2139/ssrn.3864227

John R. Birge

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

Hongfan Chen

Chinese University of Hong Kong - Business School ( email )

N. Bora Keskin (Contact Author)

Duke University - Fuqua School of Business ( email )

100 Fuqua Drive
Durham, NC 27708-0120
United States

HOME PAGE: http://faculty.fuqua.duke.edu/~nk145/

Amy Ward

The University of Chicago Booth School of Business ( email )

5807 S Woodlawn Ave
Chicago, IL 60637
United States

HOME PAGE: http://www.chicagobooth.edu/faculty/directory/w/amy-ward

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