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Flexible Utility Function Approximation via Cubic Bezier Splines

Author

Listed:
  • Sangil Lee

    (University of Pennsylvania
    University of Pennsylvania)

  • Chris M. Glaze

    (University of Pennsylvania)

  • Eric T. Bradlow

    (University of Pennsylvania)

  • Joseph W. Kable

    (University of Pennsylvania)

Abstract

In intertemporal and risky choice decisions, parametric utility models are widely used for predicting choice and measuring individuals’ impulsivity and risk aversion. However, parametric utility models cannot describe data deviating from their assumed functional form. We propose a novel method using cubic Bezier splines (CBS) to flexibly model smooth and monotonic utility functions that can be fit to any dataset. CBS shows higher descriptive and predictive accuracy over extant parametric models and can identify common yet novel patterns of behavior that are inconsistent with extant parametric models. Furthermore, CBS provides measures of impulsivity and risk aversion that do not depend on parametric model assumptions.

Suggested Citation

  • Sangil Lee & Chris M. Glaze & Eric T. Bradlow & Joseph W. Kable, 2020. "Flexible Utility Function Approximation via Cubic Bezier Splines," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 716-737, September.
    • Handle: RePEc:spr:psycho:v:85:y:2020:i:3:d:10.1007_s11336-020-09723-4
    DOI: 10.1007/s11336-020-09723-4
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    References listed on IDEAS

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    1. Clithero, John A., 2018. "Improving out-of-sample predictions using response times and a model of the decision process," Journal of Economic Behavior & Organization, Elsevier, vol. 148(C), pages 344-375.
    2. Anderson, Lisa R. & Mellor, Jennifer M., 2008. "Predicting health behaviors with an experimental measure of risk preference," Journal of Health Economics, Elsevier, vol. 27(5), pages 1260-1274, September.
    3. Drazen Prelec, 1998. "The Probability Weighting Function," Econometrica, Econometric Society, vol. 66(3), pages 497-528, May.
    4. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    5. Venkatraman, Vinod & Payne, John W. & Huettel, Scott A., 2014. "An overall probability of winning heuristic for complex risky decisions: Choice and eye fixation evidence," Organizational Behavior and Human Decision Processes, Elsevier, vol. 125(2), pages 73-87.
    6. Peter Wakker & Daniel Deneffe, 1996. "Eliciting von Neumann-Morgenstern Utilities When Probabilities Are Distorted or Unknown," Management Science, INFORMS, vol. 42(8), pages 1131-1150, August.
    7. David Laibson, 1997. "Golden Eggs and Hyperbolic Discounting," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(2), pages 443-478.
    8. Paul A. Samuelson, 1937. "A Note on Measurement of Utility," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 4(2), pages 155-161.
    9. Daniel R. Cavagnaro & Gabriel J. Aranovich & Samuel M. McClure & Mark A. Pitt & Jay I. Myung, 2016. "On the functional form of temporal discounting: An optimized adaptive test," Journal of Risk and Uncertainty, Springer, vol. 52(3), pages 233-254, June.
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