Flexible Utility Function Approximation via Cubic Bezier Splines

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.

White matter-based brain network topological properties associated with individual impulsivity

“…An individual’s DD (quantified as a discount rate k ) can be measured using a history of choices between small-but-immediate and large-but-delayed rewards in an intertemporal choice task (also called DD task) 6 . In other words, DD values can be quantified by applying specific approaches to behavioral data obtained during the task 7 . For example, DD is quantified by estimating the k parameter according to a specific theoretical model (e.g., exponential model 8 or hyperbolic 9 model used in this study).…”