Working Papers - details - WEF0050
Social Learning with Course Inference
Antonio Guarino & Phillipe Jehiel
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Abstract
We study social learning by boundedly rational agents. Agents take a decision in sequence,
after observing their predecessors and a private signal. They are unable to understand their
predecessors’ decisions in their finest details: they only understand the relation between the
aggregate distribution of actions and the state of nature. We show that, in a continuous action
space, compared to the rational case, agents put more weight on early signals. Despite this
behavioral bias, beliefs converge to the truth. In a discrete action space, instead, convergence
to the truth does not occur even if agents receive signals of unbounded precisions.