A mixed random utility - Random regret model linking the choice of decision rule to latent character traits

Stephane Hess*, Amanda Stathopoulos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

An increasing number of studies are concerned with the use of alternatives to random utility maximisation as a decision rule in choice models, with a particular emphasis on regret minimisation over the last few years. The initial focus was on revealing which paradigm fits best for a given dataset, while later studies have looked at variation in decision rules across respondents within a dataset. However, only limited effort has gone towards understanding the potential drivers of decision rules, i.e. what makes it more or less likely that the choices of a given respondent can be explained by a particular paradigm. The present paper puts forward the notion that unobserved character traits can be a key source of this type of heterogeneity and proposes to characterise these traits through a latent variable within a hybrid framework. In an empirical application on stated choice data, we make use of a mixed random utility-random regret structure, where the allocation to a given class is driven in part by a latent variable which at the same time explains respondents' stated satisfaction with their real world commute journey. Results reveal a linkage between the likely decision rule and the stated satisfaction with the real world commute conditions. Notably, the most regret-prone respondents in our sample are more likely to have aligned their real-life commute performance more closely with their aspirational values.

Original languageEnglish (US)
Pages (from-to)27-38
Number of pages12
JournalJournal of Choice Modelling
Volume9
Issue number1
DOIs
StatePublished - Dec 1 2013

Keywords

  • Decision rules
  • Hybrid models
  • Latent class
  • Random regret
  • Random utility

ASJC Scopus subject areas

  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

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