A Generalized Random Regret Minimization Model

12 Pages Posted: 23 Nov 2013

See all articles by Caspar Chorus

Caspar Chorus

Delft University of Technology - Department of Transport and Logistics

Date Written: November 22, 2013

Abstract

This paper presents, discusses and tests a generalized Random Regret Minimization (G-RRM) model. The G-RRM model is created by replacing a fixed constant in the attribute-specific regret functions of the RRM model, by a regret-weight variable. Depending on the value of the regret-weights, the G-RRM model generates predictions that equal those of, respectively, the canonical linear-in-parameters Random Utility Maximization (RUM) model, the conventional Random Regret Minimization (RRM) model, and hybrid RUM-RRM specifications. When the regret-weight variable is written as a binary logit function, the G-RRM model can be estimated on choice data using conventional software packages. As an empirical proof of concept, the G-RRM model is estimated on a stated route choice dataset, and its outcomes are compared with RUM and RRM counterparts.

Keywords: Random Utility Maximization, Random Regret Minimization, Choice model

JEL Classification: C25

Suggested Citation

Chorus, Caspar, A Generalized Random Regret Minimization Model (November 22, 2013). Available at SSRN: https://ssrn.com/abstract=2358415 or http://dx.doi.org/10.2139/ssrn.2358415

Caspar Chorus (Contact Author)

Delft University of Technology - Department of Transport and Logistics ( email )

Jaffalaan 5
NL-2628BX
Delft
Netherlands

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