Information Disclosure and Promotion Policy Design for Platforms

Yonatan Gur (), Gregory Macnamara (), Ilan Morgenstern () and Daniela Saban ()
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Yonatan Gur: Operations, Information and Technology, Stanford Graduate School of Business, Stanford, California 94305
Gregory Macnamara: Core Data Science, Meta Platforms, Inc., Menlo Park, California 94025
Ilan Morgenstern: Operations, Information and Technology, Stanford Graduate School of Business, Stanford, California 94305
Daniela Saban: Operations, Information and Technology, Stanford Graduate School of Business, Stanford, California 94305

Management Science, 2023, vol. 69, issue 10, 5883-5903

Abstract: We consider a platform facilitating trade between sellers and buyers with the objective of maximizing consumer surplus. Even though in many such marketplaces, prices are set by revenue-maximizing sellers, platforms can influence prices through (i) price-dependent promotion policies that can increase demand for a product by featuring it in a prominent position on the web page and (ii) the information revealed to sellers about the value of being promoted. Identifying effective joint information design and promotion policies is a challenging dynamic problem as sellers can sequentially learn the promotion value from sales observations and update prices accordingly. We introduce the notion of confounding promotion policies, which are designed to prevent a Bayesian seller from learning the promotion value (at the expense of the short-run loss of diverting some consumers from the best product offering). Leveraging these policies, we characterize the maximum long-run average consumer surplus that is achievable through joint information design and promotion policies when the seller sets prices myopically. We then construct a Bayesian Nash equilibrium, in which the seller’s best response to the platform’s optimal policy is to price myopically in every period. Moreover, the equilibrium we identify is platform optimal within the class of horizon-maximin equilibria, in which strategies are not predicated on precise knowledge of the horizon length and are designed to maximize payoff over the worst-case horizon. Our analysis allows one to identify practical long-run average optimal platform policies in a broad range of demand models.

Keywords: information design; Bayesian learning; revenue management; platforms; dynamic pricing (search for similar items in EconPapers)
Date: 2023
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