Contrasts between utility maximisation and regret minimisation in the presence of opt out alternatives

Abstract

An increasing number of studies of choice behaviour are looking at random regret minimisation (RRM) as an alternative to the well established random utility maximisation (RUM) framework. Empirical evidence tends to show small differences in performance between the two approaches, with the implied preference between the models being dataset specific. In the present paper, we discuss how in the context of choice tasks involving an opt out alternative, the differences are potentially more clear cut. Specifically, we hypothesise that when opt out alternatives are framed as a rejection of all the available alternatives, this is likely to have a detrimental impact on the performance of RRM, while the performance of RUM suffers more when the opt out is framed as a respondent being indifferent between the alternatives on offer. We provide empirical support for these hypotheses through two case studies, using the two different types of opt out alternatives. Our findings suggest that analysts need to carefully evaluate their choice of model structure in the presence of opt out alternatives, while any a priori preference for a given model structure should be taken into account in survey framing.

The importance of population differences: Influence of individual characteristics on the Australian public’s preferences for emergency care

“…For each model, a constant was specified to be associated with the option to delay care (e.g. Cheng et al, 2012;Hess et al, 2014) . All attribute levels and the constant were included as random parameters, and the individual preference weights were assumed to follow a normal distribution.…”

Travel Choice Analysis under Metro Emergency Context: Utility? Regret? Or Both?

“…Comparisons between the RUM model and RRM model have been performed many times. According to the literature statistics [24][25][26], research studies on the RRM model increased rapidly from 2010, most of which used stated preference (SP) data to estimate the parameters, and compared these with the MNL model under various choice contexts. Empirical evidence showed that although differences in model fit and predictive performance between the RUM and RRM models may not be significantly distinguished, the policy implications may vary a lot between these two models.…”

If you choose not to decide, you still have made a choice

“…In this regard, the individuals were asked to choose between two unlabelled hypothetical alternatives, having also the choice of stating their indifference among both alternatives. All experiments considered the same attributes: fare (P), travel time (TT), access time (AT) and 6 As we are considering a binary choice situation in this specific case, it would have been possible to test the approach put forward by Hess et al (2014) by assuming regret minimization. Nevertheless, such comparison would be spurious as we assumed utility maximization when constructing the simulated dataset; the same applies (though to a lesser extent) to alternatives methods, such as the MPD-approach (Cantillo et al, 2010).…”

“…For illustrative purposes, this time we also included a model considering a null-alternative in the context of a regret minimization framework (Model 5). In this case, the additional alternative, can be framed as indifference (Hess et al, 2014). 7 The results are presented in Table 3.2.…”