The “wisdom of the crowds” has become a hot topic in the last decade with the rapid adaptation of the Internet. At the core of the phenomena is that users do not only consume information but also produce it. This dual role of the users leads to a fundamental design question, how to incentivize the users to explore (produce new information) rather than exploit (use existing information).

We provide a first step in understanding this new aspect of the classical tradeoff between exploration and exploitation in the face of agents’ incentives. Our abstraction studies a novel model in which agents arrive sequentially one after the other and each in turn chooses one action from a fixed set of actions to maximize his expected rewards given the information he possesses at the time of arrival. (More concretely, each agent is a two-arm bandit, maximizing his own utility given the information he has observed.)

The information that becomes available affects the incentives of an agent to explore and generate new information. We characterize the optimal disclosure policy of a planner whose goal is to maximizes social welfare. The planner's optimal policy is characterized and shown to be intuitive and very simple to implement. As the number of agents increases the social welfare converges to the optimal welfare of the unconstrained mechanism and the regret is bounded by a constant.

[Based on a joint work with Ilan Kremer and Motty Perry.]