Random Regret Minimization and Random Utility Maximization in the Presence of Preference Heterogeneity: An Empirical Contrast

Abstract

Random regret minimisation (RRM) interpretations of discrete choices are growing in popularity as a complementary modelling paradigm to random utility maximisation (RUM).While behaviourally very appealing in the sense of accommodating the regret of not choosing the 'best' alternative, studies to date suggest that the differences in willingness to pay estimates, choice elasticities and choice probabilities compared to RUM are small. However, the evidence is largely based on a simple multinomial logit form of the RRM model. In this paper we revisit this behavioural contrast and move beyond the multinomial logit model to incorporate random parameters, revealing the presence of preference heterogeneity. The important contribution of this paper is to see if the extension of RRM-MNL to RRM-mixed logit in passenger mode choice widens the behavioural differences between RUM and RRM. The current paper has identified a statistically richer improvement in fit of mixed logit compared to multinomial logit under RRM (and RUM) but found small differences overall between the empirical outputs of RUM and RRM, with no basis of an improved model fit between these two non-nested model forms. The inclusion of both model forms should continue to inform the 3 likely range of behavioural outputs as we investigate a broader range of process heuristics designed to capture real world behavioural response.

Consensus Measure with Multi-stage Fluctuation Utility Based on China’s Urban Demolition Negotiation

“…In order to describe the preference degree in the economic field, Bernoulli (1954) firstly used the concept of utility. Yet utility theory was truly put forward by Von Neumann and Morgenstern (1947), which laid a solid foundation to later research (Feenstra 2003;Hensher et al 2016;Charpentier et al 2016; Gul et al 2014; Kolbin et al 2016), and gradually evolved in the hot spot of the decision-making area. However, after fuzzy set theory was proposed (Zadeh 1965), the concept of utility function was gradually transformed into the application of membership function (Pandey et al 2015;Yadav et al 2015;Liu 2014;Chang 2008).…”

Error distributions assumptions in random regret choice models: towards error Frechit specifications

“…Consequently, most applications involve Equation (5) in combination with Equation 2; a few studies explored taste variation (e.g. Boeri and Masiero 2014;Hensher, Greene, and Ho 2016). The behavioral validity of independently and identically distributed unobserved regrets has not found much attention in transportation research.…”

On the robustness of efficient experimental designs towards the underlying decision rule

“…The main reason why we use the RRM model for our analyses is because RRM models are among the more widely used non-RUM models (see e.g. Hensher et al, 2016;Boeri and Longo, 2017;Sharma et al, 2017;van Cranenburgh and Chorus (in press) for recent applications). Moreover, the specific RRM model we usethe P-RRM model (Van Cranenburgh et al, 2015a)is equally parsimonious as the canonical linear-additive RUM model, andas we will show belowhas very convenient mathematical properties for constructing efficient designs.…”