Random Regret Minimization Research Articles

Park-and-ride lot choice model using random utility maximization and random regret minimization

This research aims to understand the park-and-ride (PNR) lot choice behaviour of users i.e., why PNR user choose one PNR lot versus another. Multinomial logit models are developed, the first based on the random utility maximization (RUM) concept where users are assumed to choose alternatives that have maximum utility, and the second based on the random regret minimization (RRM) concept where users are assumed to make decisions such that they minimize the regret in comparison to other foregone alternatives. A PNR trip is completed in two networks, the auto network and the transit network. The travel time of users for both the auto network and the transit network are used to create variables in the model. For the auto network, travel time is obtained using information from the strategic transport network using EMME/4 software, whereas travel time for the transit network is calculated using Google’s general transit feed specification data using a backward time-dependent shortest path algorithm. The involvement of two different networks in a PNR trip causes a trade-off relation within the PNR lot choice mechanism, and it is anticipated that an RRM model that captures this compromise effect may outperform typical RUM models. We use two forms of RRM models; the classical RRM and µRRM. Our results not only confirm a decade-old understanding that the RRM model may be an alternative concept to model transport choices, but also strengthen this understanding by exploring differences between two models in terms of model fit and out-of-sample predictive abilities. Further, our work is one of the few that estimates an RRM model on revealed preference data.

Regulatory Focus and Decision Rules: Are Promotion-Focused Consumers Utility Maximizers and Prevention-Focused Consumers Regret Minimizers?

This study aims to investigate the potential consumer-specific driver for decision rules between utility maximization or regret minimization in discrete choice modeling to gain a better understanding of consumers’ decision process, while introducing the random regret minimization (RRM) model in marketing to examine consumer choices. Drawing upon regulatory focus theory, we posit that promotion-focused consumers, who are inclined to maximize their opportunities, are more likely to be utility maximizers while prevention-focused consumers, who are inclined to minimize negative outcomes, are more likely to be regret minimizers. Using a finite mixture discrete choice model of utility-maximizing and regret-minimizing behavioral classes, we confirm our hypotheses with an online choice-based conjoint experiment data on US consumers. Our empirical findings further suggest that, while the RRM alone can perform as well as the conventional multinomial logit, it is important to incorporate structural heterogeneity to gain additional insights on consumer preference and the market structure.

On the similarities between random regret minimization and mother logit: The case of recursive route choice models

This paper focuses on the comparison of the random regret minimization (RRM) and mother logit models for analyzing the choice between alternatives having deterministic attributes. The mother logit model allows utilities of a given alternative to depend on attributes of other alternatives. It was designed to relax the independence from irrelevant alternatives (IIA) property while keeping the random terms independently and identically distributed extreme value distributed (McFadden et al., 1978).We adapt and extend the RRM model proposed by Chorus (2014) to the case of recursive logit (RL) route choice models (Fosgerau et al., 2013). We argue that these RRM models can be cast as mother logit models and we define such models that are equivalent to the RRM ones considered in this paper. The results show that one of the RRM models and its mother logit equivalent has the best out-of-sample fit indicating that utility functions based on attribute differences best explains the choices in our application.

Analysis of The Time Value for Public Transport Passenger by Using Random Regret Minimization

This study aims to analyze the value of time a public transport passengers in Medan which is the amount of money issued by a person who was willing to save one unit of time traveling. An investment in transport projects should consider the high or low travel speeds for road users, whenever he or she is the driver, passengers or goods that were transported. Savings or reduction in the value of time is one of the advantages in transportation projects. Random Regret Minimization is a new method that can be used to calculate the value of time. The value of the travel time of passenger public transportation Medan with Random Regret Minimization method is Rp.6687.52/hour/person. Regret Minimization showed significantly different results with the Mode Choice Approach method and the Income Approach. This shows the tendency of public transport passengers in Medan to avoid the regret that will arise when the monorail will operate later.

Context-driven regret-based model of travel behavior under uncertainty: a latent class approach

Understanding variability in observed travel behavior has been one of the major research topics in travel behavior analysis and travel demand modeling. Differences in outcomes of travel decisions can be attributed to observed and non-observed differences between travelers and different situations and contexts in which decisions are made. The majority of studies in transportation research have estimated the effects of observed socio-demographic profiles on choice probabilities under certainty. Unobserved heterogeneity in utility functions has been typically examined using mixed logit or latent class models. The focus of the current paper concerns the effect of context and personality traits on decision-making under uncertainty, a combination of factors that has received not much attention in transportation research to date. Using route choice in an activity context as an example, we estimate a latent class random regret-minimization model, which takes into account the travel time and therefore arrival time uncertainty that people face when making route choice decisions. In addition, it incorporates the effects of personality traits, socio-demographic profiles and contextual factors, which increase or decrease travelers’ feelings of regret. The model is estimated based on a stated choice experiment, which was administered through a Web-based survey. Results suggest the existence of three latent classes underlying differences in regret-driven choice behavior.

Named Content Searching in Opportunistic Mobile Networks

In this letter, we study the adaptation of named data networking-based named content searching in opportunistic mobile networks (OMNs). Given the inherent uncertainty in OMNs, our goal is to replicate the content requests to a suitable set of nodes that can help improve users’ experience in terms of requests satisfaction and latency. To this end, we propose a scheme, content searching as regret minimization (CHARM), based on the technique of random regret minimization. In CHARM, content interest replication and non-replication choices are expressed in terms of several measurable attributes. Regrets associated with the two choices indicate whether or not an interest should be replicated. Moreover, to reduce overhead, CHARM uses dynamic time-to-live adaptation, where the lifetime of a message is proactively scaled down. The results of performance evaluation based on real-life traces and synthetic mobility model indicate that CHARM can improve the number of interests satisfied and latency, respectively, by up to 1.6 and 1.4 times when compared with a contemporary scheme.

Modeling the decoy effect with context-RUM Models: Diagrammatic analysis and empirical evidence from route choice SP and mode choice RP case studies

Evidence outside transportation has suggested that the introduction of a decoy to the choice-set could increase the share of other alternatives. This evidence breaks the regularity assumption, which is at the root of the classical Random Utility Maximization (RUM) model with utilities that ignore the choice context. This article assesses the suitability of various context-RUM choice models that could overcome this limitation. For this we use a diagrammatic analysis, as well as Stated Preference (SP) and Revealed Preference (RP) transportation choice evidence. We begin confirming that the reported decoy outcomes cannot be replicated with the classical RUM models and that such a goal could be achieved instead using a set of five context-RUM models. We then show, for the first time, that the Asymmetrically Dominated (AD) and Compromise (CP) decoy effects were present in an SP route choice setting. We also show that, for a subset of individuals, the relative strength of the different decoy types was coherent with a Data Generation Process (DGP) defined by the Random Regret Minimization (RRM) or by the Regret by Aspects (RBA) parsimonious models. Then, we use cross-validation analysis where we found that RRM and RBA were superior to a classical Logit for all decoy types. Nevertheless, the ad-hoc Emergent Value (EV) model was consistently superior to all models suggesting that, although the parsimonious models may in theory replicate all decoy types, they seem to still make an incomplete representation of the DGP behind the overall decoy effect. We finally consider an RP mode choice experiment with which we detect, for the first time, an AD decoy effect in this choice setting. We also use this experiment to illustrate how to handle the decoy phenomena in a real context with various alternatives and variables. The article concludes summarizing the main contributions of this research and suggesting future lines of investigation for it.

Evacuation travel behavior in regret minimization or utility maximization rules? Evidence from emergency context

The aim of this paper is to compare Random Regret Minimization (RRM) and Random Utility Maximization (RUM) paradigms in emergency evacuation decision application. Three models including utility model, regret model, and hybrid utility-regret model are formulated, respectively. A Stated Preference (SP) survey on evacuation route choice under emergency was conducted to obtain the evacuees' trade-off among average travel time, travel time uncertainty, perceived road damage probability, and perceived service level. The estimation reveals that regret-based model performs better than utility-based model. The differences between RRM and RUM in emergency context are substantial in model fit when compared with previous empirical results under non-emergency state. Specifically, the model also explains the influence of determining factors related to evacuees' socio-demographics on their route decisions. Further, considering the road damage probability as a predominant factor, Value of Damage (VOD) is proposed to reveal the unobserved regret aversion psychology. Direct elasticity and cross elasticity are calculated for the hybrid utility-regret model. Regret increases in the emergency scenario, supporting that the advantage of RRM paradigm to explain evacuation route choices. The study is expected to give a better understanding of evacuation route choice behavior for emergency actions.

Incorporating psycho-physical mapping into random regret choice models: model specifications and empirical performance assessments

Recently, regret-based choice models have been introduced in the travel behavior research community as an alternative to expected/random utility models. The fundamental proposition of regret theory is that individuals minimize the amount of regret they (are expected to) experience when choosing among choice alternatives. In this context, regret is defined as a function of attribute differences between the considered choice alternative and one or more foregone choice alternatives in an individual’s choice set. This definition of regret as a function of physical attributes of the choice alternatives implies that current regret-based choice models do not account for the perception of attributes, which is likely related to their magnitude. Therefore, in this paper, we propose and empirically test such an elaboration of the basic regret-minimization models. The current paper sets out to formulate random regret minimization models that incorporate a non-linear representation of the perception of attribute levels. Inspired by long-standing research on psycho-physical measurement, it is assumed that the perception of stimuli (attributes) is proportional to their magnitude. To allow for slight deviations from this representation, a more general non-linear psycho-physical representation of the relationship between attribute levels and their perception is also tested. The suggested models are tested using two data sets, one data set concerned the stated choice of shopping centre, the other concerned revealed preference of mode choice. The two newly formulated regret models are compared against each other and against their original random regret minimization base specifications. In addition to comparing the predictive performance of these model specifications, validation tests are conducted. In each case study, the newly suggested regret models, incorporating a non-linear representation of perception, achieve significant improvements in goodness-of-fit over the original regret formulations. The results of the K-fold validation tests provide further support to this finding.

On the robustness of random regret minimization modelling outcomes towards omitted attributes

As discrete choice models may be misspecified, it is crucial for choice modellers to have knowledge on the robustness of their modelling outcomes towards misspecification. This study investigates the robustness of Random Regret Minimization (RRM) modelling outcomes towards one sort of model misspecification: the omission of relevant attributes. We explore the effect of omitted attributes (orthogonal and correlated) in the context of labelled and unlabeled data. In the context of labelled data, we show that – just as in RUM models – in RRM models Alternative Specific Constants (ASCs) can be used to capture the average effect of omitted attributes. However, in contrast to RUM models, ASCs in RRM models are choice set composition specific. This implies that in order to achieve consistent parameter estimates when the choice set composition varies across choice observations, different sets of ASCs need to be estimated for each unique choice set composition. In the context of unlabeled data, we show – using Monte Carlo simulations – that RRM models are fairly robust towards the presence of an orthogonal omitted attribute, though not as robust as the linear-additive RUM model. Specifically, we find that: (1) Aggregate Demand Elasticities (ADEs) implied by RRM models are less robust towards the presence of an orthogonal omitted attributes than those implied by linear-additive RUM models, and (2) Average Sample Effects (ASEs) implied by RRM models are – in the presence of an omitted orthogonal attribute – more sensitive towards misspecification in terms of the underlying decision rule than those implied by its linear-additive RUM counterpart.

지역간 수단선택에 있어서 확률적 후회 최소화 모형의 적용 연구

통행자 수단선택 연구에는 확률 효용 최대화 원칙(RUM)에 입각한 다항로짓모형이 주로 사용되어졌다. 그러나 최근 들어 RUM 원칙을 대신할 대안적 방법론의 제시가 이루어지고 있으며, 이러한 대안적 방법의 하나로 제시된 것이 확률적 후회 이론모형(RRM)이다. 본 연구는 지역간 통행자의 수단선택 행태를 모사함에 있어 RRM 모형을 적용하고, RUM 원칙을 적용한 다항로짓모형의 구축결과와 비교를 통하여 RRM 모형의 적용가능성을 검토하였다. 분석결과, RUM 모형과 RRM 모형 모두 모수 추정결과는 직관과 부합하는 결과를 보였으며, 모형의 자료 적합도는 RUM 모형보다 RRM 모형이 조금 더 높게 도출되었다. 결론적으로 자료적합성 측면에서 RRM 모형이 RUM 모형보다 낫다고 판단할 수 있으나, 아직 일부 사례에만 검토되어졌기 때문에 추후 경로선택, 화물 운송수단 선택 등 더 많은 통행자의 선택상황에 대한 추가적인 검토가 필요하다고 하겠다. The multinomial logit model, based on random utility maximization (RUM) theory, has been the predominant model used in travel mode choice contexts. In this paper, the travel mode choice model based on random regret minimization (RRM) theory is proposed as an alternative to the RUM model, and the applicability of the RRM model is examined. The presented model is applied to the case of inter-city travel mode choice in Korea. The empirical results show that the RUM model and RRM model have parameters that are consistent with the intuition. The goodness of fit statistics in the RRM model improved compared with the results of the RUM model. Consequently, these results show the possibility of using the RRM model in the context of travel mode choice.

Sampling of Alternatives in Random Regret Minimization Models

Sampling of alternatives is often required in discrete choice models to reduce the computational burden and to avoid describing a large number of attributes. This approach has been used in many areas, including modeling of route choice, vehicle ownership, trip destination, residential location, and activity scheduling. The need for sampling of alternatives is accentuated for random regret minimization (RRM) models because, unlike random utility models, the regret function for each alternative depends on all of the alternatives in the choice-set. In this paper we develop and test a method to achieve consistency, asymptotic normality, and relative efficiency of the estimators while sampling alternatives in a class of models that includes RRM. The proposed method can be seen as an extension of the approach used to address sampling of alternatives in multivariate extreme value models. We illustrate the methodology using Monte Carlo experimentation and a case study with real data. Experiments show that the proposed method is practical, performs better than a truncated model, and results in finite-sample estimates that provide a good approximation of those obtained with a model considering all of the alternatives.

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

Random regret minimization (RRM) interpretations of discrete choices are growing in popularity as a complementary modeling paradigm to random utility maximization (RUM). While behaviorally 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 (MNL) form of the RRM model. This paper revisits this behavioral contrast and moves 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 behavioral 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 nonnested model forms. The inclusion of both model forms should continue to inform the likely range of behavioral outputs during investigation of a broader range of process heuristics designed to capture real world behavioral response.

Heterogeneous Valuation of Quality Dimensions of Railway Freight Service by Chinese Shippers: Choice-Based Conjoint Analysis

This paper operationalizes and tests approaches to identify market segments for rail freight services and measures the importance that customers attach to rail service attributes (i.e., transport cost, time, frequency, reliability, and safety). The approach is based on choice-based conjoint analysis in which heterogeneity is captured by means of latent-class analysis. The research is novel in several respects. First, it addresses the diverse valuation of service preferences by shippers who use rail transport. Second, besides estimates for rail users who contract for transport services, the analysis also arrives at new estimates for forwarders as immediate clients for rail services. Third, in addition to the conventional random utility maximization (RUM) model, the paper discusses trials with a random regret minimization (RRM) model and a hybrid RUM-RRM model. Finally, the research produces unique values for China, one of the largest rail transport markets in the world.

Incorporating Driver Behaviors in Network Design Problems: Challenges and Opportunities

The goal of a network design problem (NDP) is to make optimal decisions to achieve a certain objective such as minimizing total travel time or maximizing tolls collected in the network. A critical component to NDP is how travelers make their route choices. Researchers in transportation have adopted human decision theories to describe more accurate route choice behaviors. In this paper, we review the NDP with various route choice models: the random utility model (RUM), random regret-minimization (RRM) model, bounded rationality (BR), cumulative prospect theory (CPT), the fuzzy logic model (FLM) and dynamic learning models. Moreover, we identify challenges in applying behavioral route choice models to NDP and opportunities for future research.

PRM85 - Does differential framing of opt-out alternatives in discrete choice experiments (dces) matter? Comparison of random utility maximization (rum) and random regret minimization (rrm) models

PRM85 - Does differential framing of opt-out alternatives in discrete choice experiments (dces) matter? Comparison of random utility maximization (rum) and random regret minimization (rrm) models

New insights on random regret minimization models

This paper develops new methodological insights on Random Regret Minimization (RRM) models. It starts by showing that the classical RRM model is not scale-invariant, and that – as a result – the degree of regret minimization behavior imposed by the classical RRM model depends crucially on the sizes of the estimated taste parameters in combination with the distribution of attribute-values in the data. Motivated by this insight, this paper makes three methodological contributions: (1) it clarifies how the estimated taste parameters and the decision rule are related to one another; (2) it introduces the notion of “profundity of regret”, and presents a formal measure of this concept; and (3) it proposes two new family members of random regret minimization models: the μRRM model, and the Pure-RRM model. These new methodological insights are illustrated by re-analyzing 10 datasets which have been used to compare linear-additive RUM and classical RRM models in recently published papers. Our re-analyses reveal that the degree of regret minimizing behavior imposed by the classical RRM model is generally very limited. This insight explains the small differences in model fit that have previously been reported in the literature between the classical RRM model and the linear-additive RUM model. Furthermore, we find that on 4 out of 10 datasets the μRRM model improves model fit very substantially as compared to the RUM and the classical RRM model.

Indifference based value of time measures for Random Regret Minimisation models

The notion of Value of Time (VoT) is a cornerstone of discrete choice based economic appraisal in transportation. Its derivation and interpretation in the context of Random Utility Maximisation (RUM) models with linear-additive utility functions is straightforward and well known. The choice set-composition effects and semi-compensatory behaviour emphasised in the Random Regret Minimisation (RRM) model induces deviations from this basic VoT specification. This paper reviews and provides new insights into the RRM based VoT measure developed by Chorus (2012a). It defines the theoretical properties of the measure using the micro-economic notion of indifference, and provides insights into the limitations of the measure with respect to deriving individual and aggregate welfare measures. Additionally, the representative consumer approach is adopted to derive an alternative VoT measure, which is behaviourally more complete than the Chorus (2012a) measure. Although alleviating some of the restrictions, the measure has its own theoretical disadvantage. The main contribution of the paper can therefore be summarised as the generation of the necessary insights into the extent to which RRM-based VoT measures can be applied for the purpose of economic appraisal.

A Generalized Random Regret Minimization model

This paper presents, discusses and tests a Generalized Random Regret Minimization (G-RRM) model. The G-RRM model is created by recasting a fixed constant in the attribute-specific regret functions of the conventional RRM model, into an attribute-specific regret-weight. Given that regret-weights of different attributes can take on different values, the G-RRM model allows for additional flexibility when compared to the conventional RRM model, as it allows the researcher to capture choice behavior that equals that implied by, respectively, the canonical linear-in-parameters Random Utility Maximization (RUM) model, the conventional Random Regret Minimization (RRM) model, and hybrid RUM–RRM specifications. Furthermore, for particular values of the attribute-specific regret-weights, models are obtained where regret minimization (i.e., reference dependency and asymmetry of preferences) is present for the attribute, but in a less pronounced way than in a conventional RRM model. When regret-weights are written as binary logit functions, the G-RRM model can be estimated on choice data using conventional software packages. As an empirical proof of concept, the G-RRM model is estimated on a stated route choice dataset as well as on synthetic data, and its outcomes are compared with RUM, RRM, hybrid RUM–RRM and latent class counterparts.

Relative advantage maximisation as a model of context dependence for binary choice data

In recent years we have seen a burgeoning literature, within a discrete choice modelling setting, focussing on capturing increased behavioural realism in decision making through the use of alternative specifications of the utility function. One notable example is the increasing popularity of the random regret minimisation (RRM) model, in which respondents are assumed to choose the alternative that minimises the negative emotions associated with decision making.In this paper, we discuss a ‘relative advantage maximisation’ (RAM) model, which shares some similar characteristics to the RRM model in terms of how contextual effects in the choice set are accounted for. The key feature of the RAM model is the relative advantage component which frames the comparison of an attribute against its counterparts in all other alternatives in the choice set as either an advantage or a disadvantage. The relative advantage is so named because of the assumption that it is the advantage of an alternative relative to the sum of its advantage and disadvantage that matters in the utility function.Although the RRM model has been successfully applied to choice sets with three or more alternatives, with binary choice data, one significant limitation to its scope is that the RRM model collapses to the standard RUM model. This is not the case with the RAM model, and so the notion of context dependency is still meaningfully preserved under RAM. Using the MNL model, we find that the RAM model performs no worse than the RUM model in all six binary choice datasets analysed. In some cases, a small but significant improvement in model fit is observed. However, more advanced models such as the error components and random parameters logit present a more empirically mixed picture for the RAM model. Marginal willingness to pay (WTP) measures derived from the RAM model are also discussed. Differences may be negligible in the MNL case, but not necessarily so for the random parameter logit.