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Flexible Utility Function Approximation via Cubic Bezier Splines.

Sangil Lee1,2, Chris M Glaze3, Eric T Bradlow4

  • 1Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA. sangillee3rd@gmail.com.

Psychometrika
|September 26, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces cubic Bezier splines (CBS) for modeling utility functions in decision-making. CBS offers superior accuracy in predicting choices and measuring impulsivity and risk aversion compared to traditional parametric models.

Keywords:
flexible modelinggeneralized utility functionsheterogeneityintertemporal choicerisky choice

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Area of Science:

  • Decision science
  • Behavioral economics
  • Psychophysics

Background:

  • Parametric utility models are standard for analyzing intertemporal and risky choices.
  • These models struggle with data that deviates from assumed functional forms.
  • Accurate measurement of impulsivity and risk aversion is crucial.

Purpose of the Study:

  • To introduce a novel, flexible method for modeling utility functions.
  • To improve the descriptive and predictive accuracy of choice modeling.
  • To provide model-agnostic measures of impulsivity and risk aversion.

Main Methods:

  • Utilizing cubic Bezier splines (CBS) to create smooth, monotonic utility functions.
  • Fitting CBS to diverse choice datasets.
  • Comparing CBS performance against established parametric models.

Main Results:

  • Cubic Bezier splines demonstrated higher descriptive and predictive accuracy.
  • CBS successfully modeled data inconsistent with parametric assumptions.
  • Novel behavioral patterns were identified using the CBS approach.

Conclusions:

  • Cubic Bezier splines offer a flexible and accurate alternative to parametric utility models.
  • CBS enhances the understanding of individual decision-making, impulsivity, and risk aversion.
  • This method allows for the analysis of complex choice behaviors without restrictive assumptions.