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On the Origin of Utility, Weighting, and Discounting Functions: How They Get Their Shapes and How to Change Their Shapes

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  • Neil Stewart

    (Department of Psychology, University of Warwick, Coventry CV4 7AL, United Kingdom)

  • Stian Reimers

    (Department of Psychology, City University London, London EC1V 0HB, United Kingdom)

  • Adam J. L. Harris

    (Department of Cognitive, Perceptual and Brain Sciences, University College London, London WC1H 0AP, United Kingdom)

Abstract

We present a theoretical account of the origin of the shapes of utility, probability weighting, and temporal discounting functions. In an experimental test of the theory, we systematically change the shape of revealed utility, weighting, and discounting functions by manipulating the distribution of monies, probabilities, and delays in the choices used to elicit them. The data demonstrate that there is no stable mapping between attribute values and their subjective equivalents. Expected and discounted utility theories, and also their descendants such as prospect theory and hyperbolic discounting theory, simply assert stable mappings to describe choice data and offer no account of the instability we find. We explain where the shape of the mapping comes from and, in describing the mechanism by which people choose, explain why the shape depends on the distribution of gains, losses, risks, and delays in the environment.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1853 . This paper was accepted by Yuval Rottenstreich, judgment and decision making .

Suggested Citation

  • Neil Stewart & Stian Reimers & Adam J. L. Harris, 2015. "On the Origin of Utility, Weighting, and Discounting Functions: How They Get Their Shapes and How to Change Their Shapes," Management Science, INFORMS, vol. 61(3), pages 687-705, March.
    • Handle: RePEc:inm:ormnsc:v:61:y:2015:i:3:p:687-705
    DOI: 10.1287/mnsc.2013.1853
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    8. Krzysztof Kontek, 2018. "Boundary effects in the Marschak-Machina triangle," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 13(6), pages 587-606, November.
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    14. Kassimatis, Konstantinos, 2021. "Mean-variance versus utility maximization revisited: The case of constant relative risk aversion," International Review of Financial Analysis, Elsevier, vol. 78(C).
    15. Guo, Julie & Tymula, Agnieszka, 2021. "Waterfall illusion in risky choice – exposure to outcome-irrelevant gambles affects subsequent valuation of risky gambles," European Economic Review, Elsevier, vol. 139(C).
    16. Caballero, William N. & Lunday, Brian J., 2019. "Influence modeling: Mathematical programming representations of persuasion under either risk or uncertainty," European Journal of Operational Research, Elsevier, vol. 278(1), pages 266-282.
    17. Vicki M. Bier & Simon French, 2020. "From the Editors: Decision Analysis Focus and Trends," Decision Analysis, INFORMS, vol. 17(1), pages 1-8, March.
    18. Glimcher, Paul W. & Tymula, Agnieszka A., 2023. "Expected subjective value theory (ESVT): A representation of decision under risk and certainty," Journal of Economic Behavior & Organization, Elsevier, vol. 207(C), pages 110-128.
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