This paper examines the welfare effects of informational intermediation. A (short-lived) seller sets the price of a product that is sold through a (long-lived) informational intermediary. The intermediary can disclose information about the product to consumers, earns a fixed percentage of the sales revenue in each period, and has concerns about its prominence---the market size it faces in the future, which in turn is increasing in past consumer surplus. We characterize the Markov perfect equilibria and the set of subgame perfect equilibrium payoffs of this game and show that when the market feedback (i.e., how much past consumer surplus affects future market sizes) increases, welfare may decrease in the Pareto sense.
A long-lived seller sells a product by setting prices and offering product recommendations to short-lived consumers arriving in continuous time. The seller receives consumer feedback about the product value, with an arrival rate increasing in the instantaneous sales volume. We characterize the optimal selling mechanism under general value distributions and feedback technologies and show that the seller may set the price below the production cost for an initial or interim period and may switch the price between low prices and high prices multiple times. The optimal mechanism generates a Pareto improvement compared to the optimal fixed-price mechanism which features an above-cost price and inefficient spamming.
Social Learning in General Information Environments - New Draft Coming
I study a sequential social learning model in a general information environment. Each agent learns about an underlying state of the world by observing a state-signal directly about the state, which is unboundedly informative as in Smith and Sorensen (2000), and a summary statistic about other agents' actions and then chooses his own action. I introduce the concept of "normal statistics" and show that dispersed private information successfully aggregates in the long run under all normal statistics, provided that past statistics are public information for all subsequent agents. In general, when past statistics are not always observed by subsequent agents, I introduce the concept of "weakly-separating statistics" which characterizes when information aggregation occurs.
Platform Design for Costly Learning
This paper studies the optimal design of a platform to incentivize its users to collectively acquire costly information about the quality of a product (or a service, an investment opportunity, etc.). Users arrive in discrete time. Each user observes information disclosed by the platform and may acquire a costly private signal about the product quality before making his purchase decision. The platform observes users' past purchase history and can disclose any information about past histories to subsequent users. It is shown that if the information environment features negative feedback, it is optimal for the platform to release no information early on to induce user exploration and publish a list of potentially good products at a later point in time, once and for all. On the other hand, if the information environment features positive feedback, it is optimal for the platform to continuously flag projects that are potentially good for an extended period of time right after the product is released. Welfare comparisons with different benchmarks are discussed.
Social Learning under Information Control
The efficiency of market economies and democratic political system depends on the accuracy of individuals’ beliefs. However, centralized information control has, from time to time, hindered efficient societal information aggregation. In this paper, we study to what extent information aggregation in social learning environments is affected by centralized information control of a principal with a state-independent preference. We consider a population of agents who arrive sequentially and obtain information about the state of the world both from their private signals and by observing information about other agents’ actions either exogenously or through the principal. Contrary to the naïve intuition, information aggregation can be very resilient, rather than fragile, provided that the agents have access to some minimal amount of “expanding” observations of others’ actions that are outside of the principal’s control. In general, the learning outcome depends on whether and how the beliefs generated by the private signals are bounded, as well as the type of exogenous observations that agents have.