Assisted specification of discrete choice models

Nicola Ortelli*, Tim Hillel, Francisco C. Pereira, Matthieu de Lapparent, Michel Bierlaire

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Determining appropriate utility specifications for discrete choice models is time-consuming and prone to errors. With the availability of larger and larger datasets, as the number of possible specifications exponentially grows with the number of variables under consideration, the analysts need to spend increasing amounts of time on searching for good models through trial-and-error, while expert knowledge is required to ensure these models are sound. This paper proposes an algorithm that aims at assisting modelers in their search. Our approach translates the task into a multi-objective combinatorial optimization problem and makes use of a variant of the variable neighborhood search algorithm to generate sets of promising model specifications. We apply the algorithm both to semi-synthetic data and to real mode choice datasets as a proof of concept. The results demonstrate its ability to provide relevant insights in reasonable amounts of time so as to effectively assist the modeler in developing interpretable and powerful models.
Original languageEnglish
Article number100285
JournalJournal of Choice Modelling
Volume39
Number of pages18
ISSN1755-5345
DOIs
Publication statusPublished - 2021

Keywords

  • Discrete choice models
  • Utility specification
  • Muti-objective optimization
  • Combinatorial optimization

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