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Accurate characterization of delay discounting: A multiple model approach using approximate bayesian model selection and a unified discounting measure
Author(s) -
Franck Christopher T.,
Koffarnus Mikhail N.,
House Leanna L.,
Bickel Warren K.
Publication year - 2015
Publication title -
journal of the experimental analysis of behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.75
H-Index - 61
eISSN - 1938-3711
pISSN - 0022-5002
DOI - 10.1002/jeab.128
Subject(s) - discounting , model selection , computer science , measure (data warehouse) , valuation (finance) , econometrics , selection (genetic algorithm) , bayesian probability , bayesian inference , delay discounting , set (abstract data type) , machine learning , artificial intelligence , mathematics , data mining , economics , finance , programming language
The study of delay discounting, or valuation of future rewards as a function of delay, has contributed to understanding the behavioral economics of addiction. Accurate characterization of discounting can be furthered by statistical model selection given that many functions have been proposed to measure future valuation of rewards. The present study provides a convenient Bayesian model selection algorithm that selects the most probable discounting model among a set of candidate models chosen by the researcher. The approach assigns the most probable model for each individual subject. Importantly, effective delay 50 (ED50) functions as a suitable unifying measure that is computable for and comparable between a number of popular functions, including both one‐ and two‐parameter models. The combined model selection/ED50 approach is illustrated using empirical discounting data collected from a sample of 111 undergraduate students with models proposed by Laibson (1997); Mazur (1987); Myerson & Green (1995); Rachlin (2006); and Samuelson (1937). Computer simulation suggests that the proposed Bayesian model selection approach outperforms the single model approach when data truly arise from multiple models. When a single model underlies all participant data, the simulation suggests that the proposed approach fares no worse than the single model approach.