We introduce a model of strategic experimentation on social networks where forwardlooking agents learn from their own and neighbors’ successes. In equilibrium, private discovery is followed by social diffusion. Social learning crowds out own experimentation, so total information decreases with...

We derive conditions on the learning environment - which encompasses both Bayesian and non-Bayesian processes - ensuring that an efficient allocation of resources is achievable in a dynamic allocation environment where impatient, privately informed agents arrive over time, and where the designer...

We study how subjects in an experiment use different forms of public information about their opponents' past behavior. In the absence of public information, subjects appear to use rather detailed statistics summarizing their private experiences. If they have additional public information, they...