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In our laboratory experiment, subjects, in sequence, have to predict the value of a good. We elicit the second subjects belief twice: first (first belief), after he observes his predecessors 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 predecessors rationality and a generalization of the Maximum Likelihood Updating rule. In another experiment, we directly test this theory and find support for it.
Authors
Research Associate University College London and Brown University
Toru is a Research Associate of the IFS, a Professor of Economics at UCL and an Associate Professor in the Department of Economics at Brown University
Antonio Guarino
Roberta De Filippis
Philippe Jehiel
Working Paper details
- DOI
- 10.1920/wp.cem.2018.3918
- Publisher
- The IFS
Suggested citation
De Filippis, R et al. (2018). Non-Bayesian updating in a social learning experiment. London: The IFS. Available at: https://ifs.org.uk/publications/non-bayesian-updating-social-learning-experiment-0 (accessed: 26 April 2024).
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