Non-Bayesian updating in a social learning experiment

Roberta De Filippis, Antonio Guarino, Philippe Jehiel () and Toru Kitagawa ()
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Roberta De Filippis: Institute for Fiscal Studies
Toru Kitagawa: Institute for Fiscal Studies and University College London

No CWP39/18, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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.

Date: 2018-07-04
New Economics Papers: this item is included in nep-exp
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Non-Bayesian updating in a social learning experiment (2022)
Working Paper: Non-Bayesian updating in a social learning experiment (2022)
Working Paper: Non-Bayesian updating in a social learning experiment (2022)
Working Paper: Non-Bayesian updating in a social learning experiment (2020)
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