An intermediary serves as a platform through which a producer with a private cost sells a product to a unit mass of consumers. The intermediary has information about the match values between consumers and the product, which can be used to inform both the consumers and the producer. This paper studies the revenue-maximizing mechanisms for the intermediary under two critical business models: the retail model and the marketplace model. We show that the market outcomes are equivalent across the two business models. Furthermore, it is optimal for the intermediary to either (i) provide upper-censored partial information to consumers and no information to the producer, or (ii) fully inform consumers about their values and partially disclose that information to the producer and induce quasi-perfect price discrimination. This result suggests that product recommendation and price discrimination are outcome-equivalent mechanisms for an intermediary.
This paper examines the welfare implications of third-party informational intermediation. A seller sets the price of a product that is sold through an informational intermediary. The intermediary can disclose information about the product to consumers and earn a fixed percentage of sales revenue in each period. The intermediary's market base grows at a rate that increases with past consumer surplus. We characterize the stationary equilibria and the set of subgame perfect equilibrium payoffs. When market feedback (i.e., the extent to which past consumer surplus affects future market bases) increases, welfare may decrease in the Pareto sense.
Dynamic Pricing with Recommendation and Consumer Feedback. (Online Appendix) with Shuoguang Yang
A long-lived seller sells a new product of unknown value by offering prices and recommendations to short-lived consumers in continuous time. The seller receives feedback about the product at a rate that increases with the instantaneous sales volume. The optimal selling mechanism features episodes of price discounts, during which the seller discontinuously lowers the price and selectively sends out unwarranted recommendations to consumers. The optimal price path differs for niche and mass products and may involve delayed discounts, below-cost pricing, and dynamic price back-tracking.
This paper studies sequential social learning, in which agents learn about an underlying state from others' actions. In contrast to the classic models that have a network observational structure, agents arrive in cohorts and observe action-signals regarding previous cohorts' actions. I identify a simple, necessary, and sufficient condition for asymptotic learning, called separability, which is a joint property of action-signals and agents' private information about the state. A necessary condition for separability is "unbounded beliefs" which require agents' private information to generate strong evidence of the true state, even if only with a small probability. With unbounded beliefs, separability is satisfied if action-signals have double thresholds so that at a minimum they reveal whether agents above a threshold number in each cohort choose actions below a choice-threshold. Without double thresholds, learning can be confounded so that agents' actions are forever nontrivially split among the available choices.