Information Disclosure and Promotion Policy Design for Platforms

95 Pages Posted: 28 Oct 2019 Last revised: 3 Feb 2022

See all articles by Yonatan Gur

Yonatan Gur

Stanford Graduate School of Business

Gregory Macnamara

Stanford Graduate School of Business

Ilan Morgenstern

Stanford Graduate School of Business

Daniela Saban

Stanford Graduate School of Business

Date Written: October 12, 2019

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 webpage 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 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 at 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, pricing, platforms, online marketplaces

Suggested Citation

Gur, Yonatan and Macnamara, Gregory and Morgenstern, Ilan and Saban, Daniela, Information Disclosure and Promotion Policy Design for Platforms (October 12, 2019). Available at SSRN: https://ssrn.com/abstract=3468674 or http://dx.doi.org/10.2139/ssrn.3468674

Yonatan Gur (Contact Author)

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

Gregory Macnamara

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

Ilan Morgenstern

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, 94305
United States

Daniela Saban

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

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