The Role of Independence and Stationarity in Probabilistic Models of Binary Choice

Michel Regenwetter, Clintin P. Davis-Stober

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)100-114
Number of pages15
JournalJournal of Behavioral Decision Making
Issue number1
StatePublished - Jan 2018


  • iid assumptions
  • modeling heterogeneity
  • probabilistic choice models
  • statistical testing

ASJC Scopus subject areas

  • General Decision Sciences
  • Arts and Humanities (miscellaneous)
  • Applied Psychology
  • Sociology and Political Science
  • Strategy and Management


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