Crowdfunding, Internet websites, and health care are only a few examples of markets in which agents make decisions not only on the basis of their own investigations and knowledge, but also on the basis of information from a "central planner" about other agents’ actions. While such reciprocal learning can be welfare-improving, it may reduce agents’ incentives to conduct their own investigations, and may lead to harmful cascades. We study the planner’s optimal policy regarding when to provide information and how much information to provide. We show that the optimum policy involves a delicate balance of hiding and revealing information.