Observational learning is typically examined when agents have precise information about their position in the sequence … individuals and receive a private signal about the state of the world. We show that social learning is robust to position … uncertainty. Under any sampling rule satisfying a stationarity assumption, learning is complete if signal strength is unbounded …

We study social learning in a social network setting where agents receive independent noisy signals about the truth …

We analyze boundedly rational learning in social networks within binary action environments. We establish how learning … induces learning (i.e., convergence to the optimal action for every agent in every connected network) only in highly … asymmetric environments. In all other environments learning fails in networks with a diameter larger than four. Finally, we …

We study social learning in a social network setting where agents receive independent noisy signals about the truth …

, and showed that it gives a lower bound on the time it takes for a certain natural best-reply or learning process operating …

We study learning and influence in a setting where agents communicate according to an arbitrary social network and …

This paper studies the behavioral foundations of non-Bayesian models of learning over social networks and develops a … agents follow social learning rules that satisfy "imperfect recall", according to which they treat the current beliefs of … the corresponding learning rules (including the canonical model of DeGroot). We then obtain general long-run learning …

determine the speed of social learning. To this end, we study a variant of the seminal model of DeGroot (1974), according to … learning has a simple analytical characterization in terms of the relative entropy of agents’ signal structures and their … non-trivial implications for the rate of learning. In particular, we show that when the informativeness of different …

This paper introduces a model of boundedly rational observational learning, which is rationally founded and applicable …