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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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