A Generalized Random Regret Minimization Model

Chorus, Caspar (2013): A Generalized Random Regret Minimization Model.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Random Utility Maximization; Random Regret Minimization; Choice model; Unified approach; Generalized Random Regret Minimization
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling; M - Business Administration and Business Economics ; Marketing ; Accounting ; Personnel Economics > M3 - Marketing and Advertising > M30 - General; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R4 - Transportation Economics > R41 - Transportation: Demand, Supply, and Congestion ; Travel Time ; Safety and Accidents ; Transportation Noise

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