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Behavioral biases and the representative agent

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  • Elyès Jouini
  • Clotilde Napp

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.
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Suggested Citation

  • Elyès Jouini & Clotilde Napp, 2012. "Behavioral biases and the representative agent," Theory and Decision, Springer, vol. 73(1), pages 97-123, July.
    • Handle: RePEc:kap:theord:v:73:y:2012:i:1:p:97-123
    DOI: 10.1007/s11238-011-9274-3
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    4. 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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    More about this item

    Keywords

    Behavioral biases; Probability weighting function; Representative agent; G11; D81; D84; D87; D03;
    All these keywords.

    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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