Optimal Design for Social Learning

We study the design of a recommender system for organizing social learning on a product. The optimal design trades off fully transparent social learning to improve incentives for early experimentation, by selectively over-recommending a product in the early phase of the product release. Under the optimal scheme, experimentation occurs faster than under full transparency but slower than under the first-best opti- mum, and the rate of experimentation increases over an initial phase and lasts until the posterior becomes sufficiently bad in which case the recommendation stops along with experimentation on the product. Fully transparent recommendation may become optimal if the (socially-benevolent) designer does not observe the agents’ costs or the agents choose the timing of receiving a recommendation.