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 …

taking an (irreversible) action. We define \textit{asymptotic learning} as the fraction of agents taking the correct action … converging to one as a society grows large. Under truthful communication, we show that asymptotic learning occurs if (and under … ``information hubs'', which receive and distribute a large amount of information. Asymptotic learning therefore requires information …

We study perfect Bayesian equilibria of a sequential social learning model in which agents in a network learn about an … neighbors are mutually independent. We introduce a new metric of information diffusion in social learning, which is weaker than …