"If only I had taken the other road...": Regret, risk and reinforced learning in informed route-choice

This paper presents a study of the effect of regret on route choice behavior when both descriptional information and experiential feedback on choice outcomes are provided. The relevance of Regret Theory in travel behavior has been well demonstrated in non-repeated choice environments involving decisions on the basis of descriptional information. The relation between regret and reinforced learning through experiential feedbacks is less understood. Using data obtained from a simple route-choice experiment involving different levels of travel time variability, discrete-choice models accounting for regret aversion effects are estimated. The results suggest that regret aversion is more evident when descriptional information is provided ex-ante compared to a pure learning from experience condition. Yet, the source of regret is related more strongly to experiential feedbacks rather than to the descriptional information itself. Payoff variability is negatively associated with regret. Regret aversion is more observable in choice situations that reveal risk-seeking, and less in the case of risk-aversion. These results are important for predicting the possible behavioral impacts of emerging information and communication technologies and intelligent transportation systems on travelers' behavior. © 2012 Springer Science+Business Media, LLC

Revisiting consistency with random utility maximisation: theory and implications for practical work

While the paradigm of utility maximisation has formed the basis of the majority of applications in discrete choice modelling for over 40 years, its core assumptions have been questioned by work in both behavioural economics and mathematical psychology as well as more recently by developments in the RUM-oriented choice modelling community. This paper reviews the basic properties with a view to explaining the historical pre-eminence of utility maximisation and addresses the question of what departures from the paradigm may be necessary or wise in order to accommodate richer behavioural patterns. We find that many, though not all, of the behavioural traits discussed in the literature can be approximated sufficiently closely by a random utility framework, allowing analysts to retain the many advantages that such an approach possesses

Econometric Modelling: Extensions

AbstractThis chapter is devoted to advanced issues of econometric modelling. The topics covered are, among others, models in willingness to pay space, the meaning of scale heterogeneity in discrete choice models and the application of various information processing rules such as random regret minimisation or attribute non-attendance. Other topics are anchoring and learning effects when respondents move through a sequence of choice tasks as well as different information processing strategies such as lexicographic preferences or choices based on elimination-by-aspects

Econometric Modelling: Basics

AbstractThis chapter addresses basic topics related to choice data analysis. It starts by describing the coding of attribute levels and choosing the functional form of the attributes in the utility function. Next, it focuses on econometric models with special attention devoted to the random parameter mixed logit model. In this context, the chapter compares different coefficient distributions to be used, addresses specifics of the cost attribute coefficient and it pays attention to potential correlations between random coefficients. Finally, topics related to the estimation procedure such as assuring its convergence or random draws are discussed

A Random Regret Minimization-based Discrete Choice Model

This Chapter presents the RRM-model. First, the Random Regretfunction is presented and explained (Sect. 2.1). Subsequently, this function is compared with the classical linear-additive Random Utility-function (Sect. 2.2). Finally, it is shown how the Random Regret-function translates into MNL-type choice probabilities for a particular distribution of the random error terms (Sect. 2.3).Infrastructures, Systems and ServicesTechnology, Policy and Managemen

Travel Choice Inertia: The Joint Role of Risk Aversion and Learning

This paper shows how travellers that are faced with a series of risky choices become behaviourally inert due to a combination of risk aversion and learning. Our theoretical analyses complement other studies that conceive inertia as resulting from the wish to save cognitive resources. We first present a model of risky travel mode choice. We show that if travellers dislike risk, and part of the quality of travel alternatives is only revealed upon usage, inertia emerges due to a learning-based lock-in effect. We extend our analyses to capture forward-looking behaviour and the provision of travel information