Social Learning with Selective Sampling

This paper studies how robust social learning is when sampling is selective, i.e., some types of actions are more likely to be sampled by successors. We show that Bayesian agents can achieve asymptotic learning despite non-expanding observations, because the endogenous observation network itself carries information and agents have ways to undo the selection bias.