A Generalized Random Regret Minimization model

Chorus, Caspar

doi:10.1016/j.trb.2014.06.009

Cited by 105 publications

(137 citation statements)

References 16 publications

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“…Travel mode fairness was elicited by requesting respondents to rate on a 5-point likert scale the quality of their travel time ranging from poor to excellent by transit in comparison with the time by car as the reference travel mode to university and leisure activities. Greater difference between an attribute (i.e., quality of travel time) of the chosen alternative (i.e., transit) versus the foregone alternative (i.e., car) is naturally associated with a higher level of expected regret (Chorus, 2010;Prato, 2014), which is a counterfactual thought that is strongly related to the perception of fairness (Nicklin et al, 2011).…”

Section: Survey Designmentioning

confidence: 99%

The relationship between young people׳s transit use and their perceptions of equity concepts in transit service provision

Kaplan

Silva²,

Ciommo

2014

Transport Policy

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Theory of planned behavior Structural equation models a b s t r a c tThis study investigates the effect of price and travel mode fairness and spatial equity in transit provision on the perceived transit service quality, willingness to pay, and habitual frequency of use. Based on the theory of planned behavior, we developed a web-based questionnaire for revealed preferences data collection. The survey was administered among young people in Copenhagen and Lisbon to explore the transit perceptions and use under different economic and transit provision conditions. The survey yielded 499 questionnaires, analyzed by means of structural equation models. Results show that higher perceived fairness relates positively to higher perceived quality of transit service and higher perceived ease of paying for transit use. Higher perceived spatial equity in service provision is associated with higher perceived service quality. Higher perceived service quality relates to higher perceived ease of payment, which links to higher frequency of transit use.

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Section: Survey Designmentioning

confidence: 99%

The relationship between young people׳s transit use and their perceptions of equity concepts in transit service provision

Kaplan

Silva²,

Ciommo

2014

Transport Policy

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“…Recently, Chorus (2010) introduced an approach based on Random Regret Minimization (RRM), as a complement to the RUM for the analysis of DCE data. The Random Regret…”

Section: These Studies Base Their Analysis On the Linear-in-parametermentioning

confidence: 99%

“…is the unobserved part of regret Extreme Value Type I-distributed. The observed part of the regret function as described by Chorus (2010), is:…”

Section: Modelling Utility and Regretmentioning

confidence: 99%

“…Acknowledging the fact that minimizing the random regret is mathematically equivalent to maximizing the negative of the random regret, the probability for individual n of choosing alternative i over any other alternative j in the choice set in this context is represented by a RR-MNL (Chorus, 2010): (5) Equation (5) It is worth emphasizing that, while the RU-MNL assumes that respondents use a fully compensatory behaviour, the RR-MNL implies semi-compensatory decision makers. A fully compensatory behaviour implies that individuals, when trading off attributes defining an alternative, follow a decision rule that allows a positive evaluation of an attribute to compensate for a negative evaluation of another attribute.…”

Section: Modelling Utility and Regretmentioning

confidence: 99%

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The role of regret minimisation in lifestyle choices affecting the risk of coronary heart disease

Boeri

Longo

Grisolía

et al. 2013

Journal of Health Economics

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Research Highlights: This paper introduces the discrete choice paradigm of Random Regret Minimization (RRM) to the field of health economics The combined use of RRM and Random Utility Maximization (RUM) models provide useful behavioural insights on choice Whilst the RUM is suitable for calculating welfare estimates, the RRM highlights how anticipated regret affects choices We find that the choices of overweight or obese respondents and smokers are more likely to conform to the RUM approach We also find that the choices of people in good health and with higher education are more likely to reflect the RRM approach AbstractPage 2 of 31 A c c e p t e d M a n u s c r i p t This paper introduces the discrete choice model-paradigm of Random Regret Minimization (RRM) to the field of health economics. The RRM is a regret-based model that explores a driver of choice different from the traditional utility-based Random Utility Maximization (RUM). The RRM approach is based on the idea that, when choosing, individuals aim to minimize their regret -regret being defined as what one experiences when a non-chosen alternative in a choice set performs better than a chosen one in relation to one or more attributes. Analysing data from a discrete choice experiment on diet, physical activity and risk of a fatal heart attack in the next ten years administered to a sample of the Northern Ireland population, we find that the combined use of RUM and RRM models offer additional information, providing useful behavioural insights for better informed policy appraisal.Keywords: Random Regret Minimization; Random Utility Maximization; dietary choices; physical activity; coronary heart disease risk; behavioural economics. Page 3 of 31A c c e p t e d M a n u s c r i p t IntroductionDiscrete choice experiments (DCE) are a survey-based technique used to investigate the trade-offs that people are prepared to make between different hypothetical goods or services, where respondents are shown alternative variants of the good or service described by a set of attributes and are asked to choose the most preferred one (Ryan and Hughes, 1997;Vick and Scott, 1998). Since the first applications of DCE in health economics to value patient experiences (Ryan and Hughes, 1997;Ryan, 1999), this technique has become widely used to investigate a wide range of policy questions (Ryan et al., 2008 Evidence from previous studies suggests that regret can be an important factor in both medical decision making (Smith, 1996;Djulbegovic et al. 1999 andSorum et al. 2004) and personal healthcare decisions (Brehaut et al., 2003 andZiarnowski et al., 2009), particularly when the outcome of the choice may be unfavourable. Smith (1996) explored the concept of regret in healthcare decision making from the patient's viewpoint, arguing that valuation techniques based on the utility theory, such as QALYs, will not necessarily reflect the true preferences of the individual. Ziarnowski et al. (2009) found that anticipated regret played an important role in the decision to vaccinate adol...

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“…The regret for any considered alternative j, denoted ( ) Reg j , is the sum of all binary regrets of choosing alternative j over the non-considered alternatives j J  . The random regret minimisation model, proposed in Chorus et al (2008) and subsequently refined by Chorus (2010), has been shown to be able to accommodate the compromise effect.…”

Section: Overview Of the Random Regret Mixed Logit Modelmentioning

confidence: 99%

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

Hensher

Greene

2016

J. Transp. Eng.

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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.

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A Generalized Random Regret Minimization model

Cited by 105 publications

References 16 publications

The relationship between young people׳s transit use and their perceptions of equity concepts in transit service provision

The relationship between young people׳s transit use and their perceptions of equity concepts in transit service provision

The role of regret minimisation in lifestyle choices affecting the risk of coronary heart disease

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

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