This paper presents an overview of model properties and empirical evidence related to the recently introduced discrete choice paradigm of random regret minimization (RRM). The RRM approach to discrete choice modelling provides an alternative to the conventional, linear-additive random utility maximization (RUM)-based approach which has dominated the field since its inception. Section of Transport and Logistics RRM models postulate 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 behavioural premise, semi-compensatory decision-making and choice set composition effects like the compromise effect emerge as RRM model features. Being as parsimonious as RUM's 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 RRM's model properties and empirically tested RRM-based models Delft University of Technology, based on a range of revealed and stated choice data sets. As such, the paper allows for an early assessment of RRM's potential and its limitations as a model of discrete (travel) choice behaviour.
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