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. This paper was accepted by David Simchi-Levi, revenue management and market analytics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4677 .

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We consider a platform facilitating trade between sellers and buyers with the objective of maximizing consumer surplus. In many such platforms prices are set by revenue-maximizing sellers, but the platform may influence prices through its promotion policy (e.g., increasing demand to a certain product by assigning to it a prominent position on the webpage), and the information it reveals about the additional demand associated with being promoted. Identifying effective joint information design and promotion policies for the platform 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 polices, which are designed to prevent a Bayesian seller from learning the promotion value (at the cost of diverting consumers away from the best product offering). Leveraging this notion, we characterize the maximum longrun average consumer surplus that is achievable by the platform when the seller is myopic. We then establish that long-run average optimality can be maintained by optimizing over a class of joint information design and promotion policies under which the platform provides the seller with a (random) information signal at the beginning of the horizon, and then uses the best confounding promotion policy, which prevents the seller from further learning. Additionally, we show that myopic pricing is a best response to such a platform strategy, thereby establishing an approximate Bayesian Nash equilibrium between the platform and the seller. Our analysis allows one to identify practical long-run average optimal platform policies in a broad range of demand models and evaluate the impact of the search environment and the design of promotions on consumer surplus.

When Consumers May Benefit from Firms Tracking and Exploiting Their Data In today’s economy, firms routinely collect, track, and leverage consumer data to make decisions. Although the effects of these practices on consumers are complex and context-dependent, one may expect that that consumers would be disadvantaged if their data are used for a purpose that does not create value for them, such as personalized pricing. In “Data Tracking Under Competition,” Bimpikis, Morgenstern, and Saban develop a game-theoretic model to explore how technologies that allow firms to use consumer data for price discrimination affect market outcomes. Perhaps counter to intuition, they find that data-tracking practices may actually increase consumer surplus, even if consumers do not develop privacy concerns associated with disclosing their data and act myopically. However, this only occurs when multiple firms compete for consumers’ purchases. The study highlights the crucial role of competition in determining the benefits that consumers may derive from the data they generate and underscores the importance of considering competition in debates about the economic consequences of data-tracking practices.

ate School of Business We explore the welfare implications of data tracking technologies that enable firms to collect consumer data and potentially use it for price discrimination. The model we develop centers around two features: first, competition between firms and, second, consumers' level of sophistication. Our baseline environment features a firm that can collect information about the consumers it transacts with in a duopoly market, which it can then use in a second monopoly market. We characterize and compare the equilibrium outcomes in three settings of interest: (i) an economy with myopic consumers, who, when making purchase decisions, do not internalize the fact that firms have the ability to track their behavior and use this information in future transactions, (ii) an economy with forward-looking consumers, who take into account the implications of data tracking when determining their actions, and (iii) an economy where no data tracking technologies are used either due to technological or regulatory constraints.We find that the absence of data tracking may lead to a decrease in consumer surplus, even when consumers are myopic. Importantly, this result relies critically on competition: consumer surplus may be higher when data tracking technologies are used in the marketplace only when multiple firms offer substitutable products to consumers. Our results contribute to the debate of whether to regulate firms' use of data tracking technologies by illustrating that their effect on consumers depends not only on their level of sophistication, i.e., the extent to which they internalize how their data may be used, but also on the degree of competition in the market. Finally, in contrast to earlier work, we show that firms may have no incentive to self-regulate their use of consumer data even when consumers fully internalize and anticipate how their data may be used.The full paper is available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3808228 CCS Concepts: • Security and privacy → Economics of security and privacy; • Applied computing → Electronic commerce.