Dynamic Learning in Strategic Pricing Games

36 Pages Posted: 12 May 2020

See all articles by Matt Stern

Matt Stern

Columbia University - Department of Industrial Engineering and Operations Research (IEOR)

John R. Birge

University of Chicago - Booth School of Business

Date Written: April 17, 2020

Abstract

In monopoly pricing situations, firms should optimally vary prices to learn demand. The variation must be sufficiently high to ensure complete learning. In competitive situations, however, varying prices provides information to competitors and may reduce the value of learning. Such situations may arise in the pricing of new products such as pharmaceuticals and digital goods. This paper shows that firms in competition can learn efficiently in certain equilibrium actions which involve adding noise to myopic estimation and best-response strategies. The paper then discusses how this may not be the case when actions reveal information quickly to competitors. The paper provides a setting where this effect can be strong enough to stop learning so that firms optimally reduce any variation in prices and choose not to learn demand. The result can be that the selling firms achieve a collaborative outcome instead of a competitive equilibrium. The result has implications for policies that restrict price changes or require disclosures.

Keywords: Revenue Management, Sequential Estimation, Dynamic Pricing, Learning, Competition

JEL Classification: C73,C61,C62

Suggested Citation

Stern, Matt and Birge, John R., Dynamic Learning in Strategic Pricing Games (April 17, 2020). Available at SSRN: https://ssrn.com/abstract=3579123 or http://dx.doi.org/10.2139/ssrn.3579123

Matt Stern

Columbia University - Department of Industrial Engineering and Operations Research (IEOR) ( email )

331 S.W. Mudd Building
500 West 120th Street
New York, NY 10027
United States

John R. Birge (Contact Author)

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

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