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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 - Comue de Toulouse - Communauté d'universités et établissements 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 - Université 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 - Université 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

We conduct an experiment on individual choice under risk in which we study belief updating when an agent receives a signal that restricts the number of possible states of the world. Subjects observe a sample drawn from an urn and form initial beliefs about the urn's composition. We then elicit how beliefs are modified after subjects receive a signal that restricts the set of the possible urns from which the observed sample could have been drawn. We find that this type of signal increases the frequency of correct assessments and that prediction accuracy is higher for lower levels of risk. We also show that prediction accuracy is higher after invalidating signals (i.e. signals that contradict the initial belief). This pattern is explained by the lower level of risk associated with invalidating signals. Finally, we find evidence for a lack of persistence of choices under high risk.

Suggested Citation

  • François Poinas & Julie Rosaz & Béatrice Roussillon, 2012. "Updating beliefs with imperfect signals: experimental evidence," Post-Print halshs-00576669, HAL.
    • Handle: RePEc:hal:journl:halshs-00576669
    DOI: 10.1007/s11166-012-9143-7
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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.

    More about this item

    Keywords

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    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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