We study social learning in a social network setting where agents receive independent noisy signals about the truth. Agents naïvely update beliefs by repeatedly taking weighted averages of neighbors' opinions. The weights are fixed in the sense of representing average frequency and intensity of...

We designed four observational learning experiments to identify the key channels that, along with Bayes-rational inferences, drive herd behavior. In Experiment 1, unobserved, whose actions remain private, learn from the public actions made in turn by subjects endowed with private signals of...

We report two information cascade game experiments that directly test the impact of altruism on observational learning. Participants interact in two parallel sequences, the observed and the unobserved sequence. Only the actions of the observed entail informational benefits to subsequent...

This paper proposes a behavioral model of social learning that unies various forms of inferential reasoning in one hierarchy of types. Iterated best responses that are based on uninformative level-0 play lead to the following of the private information (level-1), to the following of the majority...