Delft University of Technology
August 09, 2011, 11:00, Room GC B3 424 (click here for the map)
This talk presents an overview of recent progress related to the recently introduced discrete choice-paradigm of Random Regret Minimization (RRM). The RRM-approach to discrete choice-modeling provides an alternative to the conventional, Random Utility Maximization (RUM)-based approach which has dominated the field since its inception. In contrast with RUM-theory, RRM-theory postulates that when choosing, decision-makers are concerned with avoiding the situation where one or more non-chosen alternatives perform better than a chosen one in terms of one or more attributes. From this central behavioral premise, semi-compensatory decision-making and choice set-composition effects like the compromise effect emerge as RRM-model features. Being as parsimonious as RUMís linear-additive multinomial logit model, RRM features logit-choice-probabilities and is easily estimable using conventional discrete choice-software packages. This paper ties together the main insights and results from a number of recent studies that have explored RRMís model properties and empirically tested RRM-based models vis-à-vis competing model forms. As such, the talk provides an assessment of RRMís potential and its limitations as a discrete choice model.