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Updating beliefs with imperfect signals: experimental evidence

Author

Listed:
  • François Poinas

    (GREMAQ - Groupe de recherche en économie mathématique et quantitative - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique)

  • Julie Rosaz

    (GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - ENS de Lyon - École normale supérieure de Lyon - UL2 - Université Lumière - Lyon 2 - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique)

  • Béatrice Roussillon

    (GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - ENS de Lyon - École normale supérieure de Lyon - UL2 - Université Lumière - Lyon 2 - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique)

Abstract

This article analyses belief updating when agents receive a signal that restricts the number of possible states of the world. We create an experiment on individual choice under uncertainty. In this experiment, the subject observes an urn, containing yellow and blue balls, whose composition is partially revealed. The subject has to assess the composition of the urn and form an initial belief. Then, he receives a signal that restricts the set of the possible urns from which the initial observed sample is drawn. Once again, he has to estimate the composition of the urn. Our results show that, on the whole, this type of signal increases the frequency of correct assessment. However, differences appear between validating and invalidating signals (i.e. signals that either confirm or disprove the initial belief). The later significantly increase the probability to make a correct assessment whereas validating signals reduce the frequency of correct estimations. We find evidences of lack of persistence in choice under uncertainty. The literature shows that people may persist with their choice even when they are wrong. We show that they may also change even if they are right.
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Suggested Citation

  • François Poinas & Julie Rosaz & Béatrice Roussillon, 2010. "Updating beliefs with imperfect signals: experimental evidence," Post-Print halshs-00674285, HAL.
    • Handle: RePEc:hal:journl:halshs-00674285
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    References listed on IDEAS

    as
    1. David M. Grether, 1980. "Bayes Rule as a Descriptive Model: The Representativeness Heuristic," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 95(3), pages 537-557.
    2. Friedman, Daniel, 1998. "Monty Hall's Three Doors: Construction and Deconstruction of a Choice Anomaly," American Economic Review, American Economic Association, vol. 88(4), pages 933-946, September.
    3. Gary Charness & Dan Levin, 2005. "When Optimal Choices Feel Wrong: A Laboratory Study of Bayesian Updating, Complexity, and Affect," American Economic Review, American Economic Association, vol. 95(4), pages 1300-1309, September.
    4. Ouwersloot, Hans & Nijkamp, Peter & Rietveld, Piet, 1998. "Errors in probability updating behaviour : Measurement and impact analysis," Journal of Economic Psychology, Elsevier, vol. 19(5), pages 535-563, October.
    5. Greiner, Ben, 2004. "An Online Recruitment System for Economic Experiments," MPRA Paper 13513, University Library of Munich, Germany.
    6. Hoffman, Ross M. & Kagel, John H. & Levin, Dan, 2011. "Simultaneous versus sequential information processing," Economics Letters, Elsevier, vol. 112(1), pages 16-18, July.
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    Cited by:

    1. Marek Jenöffy, 2023. "Can the Seesaw Model Depict the Certainty Effect?," Working Papers hal-04136569, HAL.
    2. Johanna Choumert-Nkolo & Anaïs Lamour & Pascale Phélinas, 2021. "The Economics of Volcanoes," Economics of Disasters and Climate Change, Springer, vol. 5(2), pages 277-299, July.
    3. Carlos Alós-Ferrer & Alexander Jaudas & Alexander Ritschel, 2021. "Effortful Bayesian updating: A pupil-dilation study," Journal of Risk and Uncertainty, Springer, vol. 63(1), pages 81-102, August.
    4. Aurélien Baillon & Han Bleichrodt & Umut Keskin & Olivier l’Haridon & Chen Li, 2018. "The Effect of Learning on Ambiguity Attitudes," Management Science, INFORMS, vol. 64(5), pages 2181-2198, May.
    5. Aurélien Baillon & Han Bleichrodt & Umut Keskin & Olivier L'Haridon & Author-Name: Chen Li, 2013. "Learning under ambiguity: An experiment using initial public offerings on a stock market," Economics Working Paper Archive (University of Rennes & University of Caen) 201331, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS.

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    More about this item

    Keywords

    Beliefs; Imperfect Information; Experiment;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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