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 long-run 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 sellerfrom further learning. Additionally, we show that myopic pricing is a best response to such a platformstrategy, 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 .
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 establish a Bayesian Nash equilibrium by showing that the seller's best response to the platform's optimal policy is to price myopically at every history. 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 .
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