Updating ambiguous beliefs 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 CWP13/17, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: We present a social learning experiment in which subjects predict the value of a good in sequence. We elicit each subject’s belief twice: first (“first belief”), after he observes his predecessors’ prediction; second, after he also observes a private signal. Our main result is that subjects update on their signal asymmetrically. They weigh the private signal as a Bayesian agent when it confirms their first belief and overweight it when it contradicts their first belief. This way of updating, incompatible with Bayesianism, can be explained by ambiguous beliefs (multiple priors on the predecessor’s rationality) and a generalization of the Maximum Likelihood Updating rule. Our experiment allows for a better understanding of the overweighting of private information documented in previous studies.

Keywords: Social; learning; experiment (search for similar items in EconPapers)
Date: 2017-03-24
New Economics Papers: this item is included in nep-exp
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Citations: View citations in EconPapers (8)

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Related works:
Working Paper: Updating ambiguous beliefs in a social learning experiment (2017)
Working Paper: Updating ambiguous beliefs in a social learning experiment (2016)
Working Paper: Updating ambiguous beliefs in a social learning experiment (2016)
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