Hierarchical Bayesian modeling of intertemporal choice

Melisa E. Chávez ； Elena Villalobos ； José L. Baroja ； Arturo Bouzas

《Judgment and Decision Making》 12卷1期 (2017/01) Pp. 19-28

摘要

There is a growing interest in studying individual differences in choices that involve trading off reward amount and delay to delivery because such choices have been linked to involvement in risky behaviors, such as substance abuse. The most ubiquitous proposal in psychology is to model these choices assuming delayed rewards lose value following a hyperbolic function, which has one free parameter, named discounting rate. Consequently, a fundamental issue is the estimation of this parameter. The traditional approach estimates each individual’s discounting rate separately, which discards individual differences during modeling and ignores the statistical structure of the population. The present work adopted a different approximation to parameter estimation: each individual’s discounting rate is estimated considering the information provided by all subjects, using state-of-the-art Bayesian inference techniques. Our goal was to evaluate whether individual discounting rates come from one or more subpopulations, using Mazur’s (1987) hyperbolic function. Twelve hundred eighty-four subjects answered the Intertemporal Choice Task developed by Kirby, Petry and Bickel (1999). The modeling techniques employed permitted the identification of subjects who produced random, careless responses, and who were discarded from further analysis. Results showed that one-mixture hierarchical distribution that uses the information provided by all subjects suffices to model individual differences in delay discounting, suggesting psychological variability resides along a continuum rather than in discrete clusters. This different approach to parameter estimation has the potential to contribute to the understanding and prediction of decision making in various real-world situations where immediacy is constantly in conflict with magnitude.

關鍵字

intertemporal choice ； hierarchical modeling ； delay discounting ； Bayesian inference ； parameter estimation