We study platform markets in which the information about users’ preferences is dispersed. First, we show how the dispersion of information introduces idiosyncratic uncertainty about participation decisions and how the latter shapes the elasticity of the demands and the equilibrium prices. We then study the effects on profits, consumer surplus, and welfare of platform design, blogs, forums, conferences, advertising campaigns, post-launch disclosures, and other information management policies affecting the agents’ ability to predict participation decisions on the other side of the market.

OPTIMAL LEARNING BY EXPERIMENTATIONThis paper analyses the dynamic decision problem of an agent who is initially uncertain as to the true shape of his payoff function, but who obtains information aboutit over time by observing the outcome of his past decisions.In the long run, the action is a short run optimum given the beliefs, but may not be an optimum for the true payoff function.We derive conditions under which the limit action is optimal for the true payoff function and establish the robustness of the results.Finally we study the adjustment process in an example where such complete learning does not achieve in the long run.