In our laboratory experiment, subjects, in sequence, have to predict the value of a good. The second subject in the sequence makes his prediction twice: first (“first belief”), after he observes his predecessor’s prediction; second (“posterior belief”), after he observes his private signal. We find that the second subjects weigh their 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 the Likelihood Ratio Test Updating (LRTU) model, a generalization of the Maximum Likelihood Updating rule. It is at odds with another family of updating, the Full Bayesian Updating. In another experiment, we directly test the LRTU model 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
- 10.47004/wp.cem.2020.6020
- Publisher
- The IFS
Suggested citation
De Filippis, R et al. (2020). Non-Bayesian updating in a social learning experiment. London: The IFS. Available at: https://ifs.org.uk/publications/non-bayesian-updating-social-learning-experiment (accessed: 8 February 2025).
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