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- W3144226360 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." @default.
- W3144226360 created "2021-04-13" @default.
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- W3144226360 date "2013-01-01" @default.
- W3144226360 modified "2023-09-23" @default.
- W3144226360 title "A Generalized Random Regret Minimization Model" @default.
- W3144226360 hasPublicationYear "2013" @default.
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