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

Abstract

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...

A Generalized Random Regret Minimization model

“…Since the introduction of the Random Regret Minimization model (RRM) for discrete choice analysis (Chorus et al, 2008;Chorus, 2010), it has been acknowledged that the model provides a quite different perspective on choice modeling than does discrete choice analysis' workhorse, the linear-in-parameters Random Utility Maximization (from here on: RUM) model. Particularly, substantial differences have been highlighted in terms of the models' theoretical properties as well as in terms of their empirical outcomes such as choice probability forecasts and elasticities (e.g., Kaplan & Prato, 2012;Thiene et al, 2012;Boeri et al, 2013;Hensher et al, 2013;Beck et al, 2013;Boeri & Masiero, 2014).…”

Random regret minimization for consumer choice modeling: Assessment of empirical evidence

“…Since its recent introduction, the RRM-model has received an increasing amount of attention from choice modelers in fields as diverse as transportation, urban planning, environmental economics and health economics (e.g., Thiene et al, 2012;Kaplan & Prato, 2012;Beck et al, 2013;Guevara et al, 2013;Boeri et al, 2013;Hensher et al, 2013). The result of this increasing interest is a rapidly growing body of empirical and theoretical papers.…”

A Generalized Random Regret Minimization Model

“…Particularly, substantial differences have been highlighted in terms of the models' theoretical properties as well as in terms of their empirical outcomes such as choice probability forecasts and elasticities (e.g., Kaplan & Prato, 2012;Thiene et al, 2012;Boeri et al, 2013;Hensher et al, 2013;Beck et al, 2013;Boeri & Masiero, 2014).…”