A Generalized Random Regret Minimization Model

Caspar Chorus

MPRA Paper from University Library of Munich, Germany

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; Unified approach; Generalized Random Regret Minimization (search for similar items in EconPapers)
JEL-codes: C5 M30 R41 (search for similar items in EconPapers)
Date: 2013-11-21
New Economics Papers: this item is included in nep-dcm, nep-ecm, nep-tre and nep-upt
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Related works:
Journal Article: A Generalized Random Regret Minimization model (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:51637

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