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Behavioral biases and representative agent

Author

Listed:
  • Elyès Jouini

    (CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

  • Clotilde Napp

    (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique, DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

Abstract

In this paper, we show that behavioral features can be obtained at a group level when the individuals of the group are heterogeneous enough. Starting from a standard model of Pareto optimal allocations, with expected utility maximizers but allowing for heterogeneity among individual beliefs, we show that the representative agent has an inverse S-shaped probability distortion function. As an application of this result, we show that an agent with a probability weighting function as in Cumulative Prospect Theory may be represented as a collection of agents with noisy beliefs.

Suggested Citation

  • Elyès Jouini & Clotilde Napp, 2012. "Behavioral biases and representative agent," Post-Print halshs-00550229, HAL.
    • Handle: RePEc:hal:journl:halshs-00550229
    DOI: 10.1007/s11238-011-9274-3
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00550229v1
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    Cited by:

    1. repec:dau:papers:123456789/13098 is not listed on IDEAS
    2. Hamza Bahaji, 2018. "Are employee stock option exercise decisions better explained through the prospect theory?," Annals of Operations Research, Springer, vol. 262(2), pages 335-359, March.
    3. Philip Millroth & HÃ¥kan Nilsson & Peter Juslin, 2019. "The decision paradoxes motivating Prospect Theory: The prevalence of the paradoxes increases with numerical ability," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 14(4), pages 513-533, July.

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    Keywords

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    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • D87 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Neuroeconomics
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles

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