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Non-Bayesian updating in a social learning experiment

Authors: Roberta De Filippis , Antonio Guarino , Philippe Jehiel and Toru Kitagawa
Date: 04 July 2018
Type: cemmap Working Paper, CWP39/18

Abstract

In our laboratory experiment, subjects, in sequence, have to predict the value of a good. We elicit the second subject’s belief twice: first (“first belief”), after he observes his predecessor’s action; second (“posterior belief”), after he observes his private signal. Our main result is that the second subjects weigh the private signal as a Bayesian agent would do when the signal confirms their first belief; they overweight the signal when it contradicts their first belief. This way of updating, incompatible with Bayesianism, can be explained by multiple priors on the predecessor’s rationality and a generalization of the Maximum Likelihood Updating rule. In another experiment, we directly test this theory and find support for it.

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