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 and an action-signal about other agents' actions and then chooses his own action. I introduce the concept of information environments that are "weakly separating" and show that dispersed private information successfully aggregates in the long run if the environment is weakly separating, provided that the state-signals are unboundedly informative as in Smith and Sorensen (2000). The characterization unifies existing results in sequential social learning and helps us understand complex learning environments such as those in which agents observe summary statistics of other agents' actions.
This paper studies the impact of third-party information design on bilateral trade. A seller sets the price of a product and makes a take-it-or-leave-it offer to a buyer. A third-party information designer, who maximizes a weighted social welfare, observes the seller's pricing strategy and designs the information structure of a signal about the value of the product. The buyer observes the price, the information structure and the signal realization and decides whether to purchase the product. I characterize the set of equilibria and show that, due to the strategic interaction between the seller and the designer, third-party information design can cause a Pareto inferior outcome despite the designer's aim to maximize a weighted social welfare.
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.). A constant flow of users arrive in continuous 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 receives users’ feedback about the product quality according to a Poisson process. It is shown that if the platform learns about the product quality through 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 platform learns about the product quality through 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 comparison with several different benchmarks are discussed.
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.