TY - JOUR
T1 - The Role of Independence and Stationarity in Probabilistic Models of Binary Choice
AU - Regenwetter, Michel
AU - Davis-Stober, Clintin P.
N1 - Funding Information:
We thank Michael Birnbaum, Dan Cavagnaro, Jason Dana, Ying Guo, Marc Jekel, A.A.J. Marley, Anna Popova, and Chris Zwilling for their critical comments on earlier drafts. Several other scholars have contributed in many discussions at various conferences. Ying Guo helped with conversion to from LaTeX to Word. This work was supported by National Science Foundation grants SES 08-20009, SES 10-62045, SES 14-59699 (PI: M. Regenwetter), and SES 14-59866 (PI: C. Davis-Stober); a National Institutes of Health grant (K25AA024182, PI: C. Davis-Stober); and by a University of Illinois, Arnold O. Beckman, Research Award (PI: M. Regenwetter). Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of their colleagues, funding agencies, or universities.
Publisher Copyright:
Copyright © 2017 John Wiley & Sons, Ltd.
PY - 2018/1
Y1 - 2018/1
N2 - After more then 50 years of probabilistic choice modeling in economics, marketing, political science, psychology, and related disciplines, theoretical and computational advances give scholars access to a sophisticated array of modeling and inference resources. We review some important, but perhaps often overlooked, properties of major classes of probabilistic choice models. For within-respondent applications, we discuss which models require repeated choices by an individual to be independent and response probabilities to be stationary. We show how some model classes, but not others, are invariant over variable preferences, variable utilities, or variable choice probabilities. These models, but not others, accommodate pooling of responses or averaging of choice proportions within participant when underlying parameters vary across observations. These, but not others, permit pooling/averaging across respondents in the presence of individual differences. We also review the role of independence and stationarity in statistical inference, including for probabilistic choice models that, themselves, do not require those properties.
AB - After more then 50 years of probabilistic choice modeling in economics, marketing, political science, psychology, and related disciplines, theoretical and computational advances give scholars access to a sophisticated array of modeling and inference resources. We review some important, but perhaps often overlooked, properties of major classes of probabilistic choice models. For within-respondent applications, we discuss which models require repeated choices by an individual to be independent and response probabilities to be stationary. We show how some model classes, but not others, are invariant over variable preferences, variable utilities, or variable choice probabilities. These models, but not others, accommodate pooling of responses or averaging of choice proportions within participant when underlying parameters vary across observations. These, but not others, permit pooling/averaging across respondents in the presence of individual differences. We also review the role of independence and stationarity in statistical inference, including for probabilistic choice models that, themselves, do not require those properties.
KW - iid assumptions
KW - modeling heterogeneity
KW - probabilistic choice models
KW - statistical testing
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U2 - 10.1002/bdm.2037
DO - 10.1002/bdm.2037
M3 - Article
C2 - 29805199
AN - SCOPUS:85037983313
SN - 0894-3257
VL - 31
SP - 100
EP - 114
JO - Journal of Behavioral Decision Making
JF - Journal of Behavioral Decision Making
IS - 1
ER -