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Updating ambiguous beliefs in a social learning experiment

Author

Listed:
  • Roberta De Filippis
  • Antonio Guarino
  • Philippe Jehiel
  • Toru Kitagawa

Abstract

We present a novel experimental design to study social learning in the laboratory. Subjects have to predict the value of a good in a sequential order. We elicit each subject’s belief twice: first (“prior belief”), after he observes his predecessors’ action; second (“posterior belief”), after he observes a private signal on the value of the good. We are therefore able to disentangle social learning from learning from a private signal. Our main result is that subjects update on their private signal in an asymmetric way. They weigh the private signal as a Bayesian agent would do when the signal confirms their prior belief; they overweight the signal when it contradicts their prior belief. We show that 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.

Suggested Citation

  • Roberta De Filippis & Antonio Guarino & Philippe Jehiel & Toru Kitagawa, 2016. "Updating ambiguous beliefs in a social learning experiment," CeMMAP working papers 18/16, Institute for Fiscal Studies.
  • Handle: RePEc:azt:cemmap:18/16
    DOI: 10.1920/wp.cem.2016.1816
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    Cited by:

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    2. Cheng, Ing-Haw & Hsiaw, Alice, 2022. "Distrust in experts and the origins of disagreement," Journal of Economic Theory, Elsevier, vol. 200(C).
    3. Li, Wenhui & Wilde, Christian, 2020. "Belief formation and belief updating under ambiguity: Evidence from experiments," SAFE Working Paper Series 251, Leibniz Institute for Financial Research SAFE, revised 2020.
    4. Bogaçhan Çelen & Sen Geng & Huihui Li, 2018. "Belief Error and Non-Bayesian Social Learning: An Experimental Evidence," GRU Working Paper Series GRU_2018_022, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    5. Corgnet, Brice & Hernán-González, Roberto & Kujal, Praveen, 2020. "On booms that never bust: Ambiguity in experimental asset markets with bubbles," Journal of Economic Dynamics and Control, Elsevier, vol. 110(C).
    6. Konstantinos Georgalos, 2019. "An experimental test of the predictive power of dynamic ambiguity models," Journal of Risk and Uncertainty, Springer, vol. 59(1), pages 51-83, August.
    7. Levy, Gilat & Razin, Ronny, 2021. "A maximum likelihood approach to combining forecasts," LSE Research Online Documents on Economics 104116, London School of Economics and Political Science, LSE Library.
    8. Denis Shishkin & Pietro Ortoleva, 2021. "Ambiguous Information and Dilation: An Experiment," Working Papers 2020-53, Princeton University. Economics Department..
    9. Duffy, John & Hopkins, Ed & Kornienko, Tatiana & Ma, Mingye, 2019. "Information choice in a social learning experiment," Games and Economic Behavior, Elsevier, vol. 118(C), pages 295-315.

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