Random Regret-based Discrete Choice Modeling

This tutorial presents a hands-on introduction to a new discrete choice modeling approach based on the behavioral notion of regret-minimization. This so-called Random Regret Minimization-approach (RRM) forms a counterpart of the Random Utility Maximization-approach (RUM) to discrete choice modeling, which has for decades dominated the field of choice modeling and adjacent fields such as transportation, marketing and environmental economics. Being as parsimonious as conventional RUM-models and compatible with popular software packages, the RRM-approach provides an alternative and appealing account of choice behavior. Rather than providing highly technical discussions as usually encountered in scholarly journals, this tutorial aims to allow readers to explore the RRM-approach and its potential and limitations hands-on and based on a detailed discussion of examples. This tutorial is written for students, scholars and practitioners who have a basic background in choice modeling in general and RUM-modeling in particular. It has been taken care of that all concepts and results should be clear to readers that do not have an advanced knowledge of econometrics.

Introduces a regret minimization-based approach to discrete choice modeling The approach provides a promising alternative to utility maximization-based choice models Detailed discussion of the regret-based model's properties and empirical applicability Working knowledge of choice modeling is presupposed, advanced econometric skills not needed Includes supplementary material: sn.pub/extras

Inhalt
?Introduction.- A Random Regret Minimization-based discrete choice model.- Empirical application of Random Regret Minimization-models.- Applicability of Random Regret Minimization-models.- Selection of recent developments in RRM-modeling.