Hybrid Choice Models: Progress and Challenges

Moshe Ben-Akiva, Daniel Mcfadden, Kenneth Train, Joan Walker, Chandra Bhat, Michel Bierlaire, Denis Bolduc, Axel Boersch-Supan, David Brownstone, David S. Bunch, Andrew Daly, Andre De Palma, Dinesh Gopinath, Anders Karlstrom, Marcela A. Munizaga

Research output: Contribution to journalArticlepeer-review

478 Scopus citations

Abstract

We discuss the development of predictive choice models that go beyond the random utility model in its narrowest formulation. Such approaches incorporate several elements of cognitive process that have been identified as important to the choice process, including strong dependence on history and context, perception formation, and latent constraints. A flexible and practical hybrid choice model is presented that integrates many types of discrete choice modeling methods, draws on different types of data, and allows for flexible disturbances and explicit modeling of latent psychological explanatory variables, heterogeneity, and latent segmentation. Both progress and challenges related to the development of the hybrid choice model are presented.

Original languageEnglish
Pages (from-to)163-175
Number of pages13
JournalMarketing Letters
Volume13
Issue number3
DOIs
StatePublished - 2002
Externally publishedYes

Keywords

  • Choice modeling
  • Estimation
  • Latent variables
  • Logit kernel
  • Mixed logit
  • Simulation

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