Social Leanring with Course Inference
AbstractWe 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.
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Bibliographic InfoPaper provided by ESRC World Economy and Finance Research Programme, Birkbeck, University of London in its series WEF Working Papers with number 0050.
Date of creation: Jul 2009
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