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). A constant flow of users arrive in sequential order. 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. 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 to be good for an extended period of time right after the product is released. Welfare comparison with several different benchmarks are discussed