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Second-best probability weighting

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  • Herold, Florian
  • Netzer, Nick

Abstract

Non-linear probability weighting is an integral part of descriptive theories of choice under risk such as prospect theory. But why do these objective errors in information processing exist? Should we try to help individuals overcome their mistake of overweighting small and underweighting large probabilities? In this paper, we argue that probability weighting can be seen as a compensation for preexisting biases in evaluating payoffs. In particular, inverse S-shaped probability weighting is a flipside of S-shaped payoff valuation. Probability distortions may thus have survived as a second-best solution to a fitness maximization problem, and it can be counter-productive to correct them while keeping the value function unchanged.

Suggested Citation

  • Herold, Florian & Netzer, Nick, 2023. "Second-best probability weighting," Games and Economic Behavior, Elsevier, vol. 138(C), pages 112-125.
    • Handle: RePEc:eee:gamebe:v:138:y:2023:i:c:p:112-125
    DOI: 10.1016/j.geb.2022.12.005
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    More about this item

    Keywords

    Probability weighting; Prospect theory; Evolution of preferences;
    All these keywords.

    JEL classification:

    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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