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

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 …

This paper presents a non-Bayesian model of social learning in networks in an environment with a finite set of actions …

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 …

This paper provides a model of social learning where the order in which actions are taken is determined by an $m …

This paper provides a model of social learning where the order in which actions are taken is determined by an m …

DeGroot rule, then actions converge to the state (in probability), i.e., \emph{asymptotic learning}, in any informative …

This paper provides a formal characterization of the process of rational learning in social networks. Agents receive … rational learning leads to global consensus, local indifference and local disagreement are characterized. In the general … setting considered, rational learning can lead to pairs of neighbors selecting different actions once learning ends, while not …

outcome but influence and naive learning are not …