Bounded Rational Choice Behaviour: Applications in Transport

This chapter reviews models of decision-making and choice under conditions of certainty. It allows readers to position the contribution of the other chapters in this book in the historical development of the topic area.

Bounded rationality is defined in terms of a strategy to simplify the decision-making process. Based on this definition, different models are reviewed. These models have assumed that individuals simplify the decision-making process by considering a subset of attributes, and/or a subset of choice alternatives and/or by disregarding small differences between attribute differences.

A body of empirical evidence has accumulated showing that under some circumstances the principle of bounded rationality better explains observed choices than the principle of utility maximization. Differences in predictive performance with utility-maximizing models are however small.

The chapter provides a detailed account of the different models, based on the principle of bounded rationality, that have been suggested over the years in travel behaviour analysis. The potential relevance of these models is articulated, model specifications are discussed and a selection of empirical evidence is presented. Aspects of an agenda of future research are identified.

This chapter proposes a new mixture model which allows for heterogeneity in sensitivities and decision rules across decision makers and attributes.

A new mixture model is put forward in which the different latent classes make use of different decision rules, where the use of generalised random regret minimisation kernel allows for within class heterogeneity in the decision rules applied across attributes.

Our theoretical developments are supported by the findings of an empirical application using data from a typical stated choice survey.

Existing work has looked at heterogeneity in decision rules and sensitivities across respondents. Other work has focused on the possibility that different decision rules apply to different attributes. This chapter puts forward a model that combines these two directions of research and does so in a way that lets the optimal specification be driven by the data rather than being imposed by the analyst.

The chapter outlines the principles underlying relative utility models, discusses the results of empirical applications and critically assesses the usefulness of this specification against commonly used random utility models and other context dependence models. It also discusses how relative utility can be viewed as a generalisation of context dependency.

In contrast to the conventional concept of random utility, relative utility assumes that decision-makers derive utility from their choices relative to some threshold(s) or reference points. Relative utility models thus systematically specify the utility against such thresholds or reference points.

Examples in the chapter show that relative utility model perform well in comparison to conventional utility-maximising models in some circumstances.

Examples of relative utility models are rare in transportation research. The chapter shows that several recent models can be viewed as special cases of relative utility models.

There is extensive evidence that decision-makers, faced with increasing information load, may simplify their choice by reducing the amount of information to process. One simplification, commonly referred to as attribute non-attendance (ANA), is a reduction of the number of attributes of the choice alternatives. Several previous studies have identified relationships between varying information load and ANA using self-reported measures of ANA. This chapter revisits this link, motivated by recognition in the literature that such self-reported measures are vulnerable to reporting error.

This chapter employs a recently developed modelling approach that has been shown to effectively infer ANA, the random parameters attribute non-attendance (RPANA) model. The empirical setting systematically varies the information load across respondents, on a number of dimensions.

Confirming earlier findings, ANA is accentuated by an increase in the number of attribute levels, and a decrease in the number of alternatives. Additionally, specific attributes are more likely to not be attended to as the total number of attributes increases. Willingness to pay (WTP) under inferred ANA differs notably from when ANA is self-reported. Additionally accounting for varying information load, when inferring ANA, has little impact on the WTP distribution of those that do attend. However, due to varying rates of non-attendance, the overall WTP distribution varies to a large extent.

This is the first examination of the impact of varying information load on inferred ANA that is identified with the RPANA model. The value lies in the confirmation of earlier findings despite the evolution of methodologies in the interim.

Increasing evidence suggests that choice behaviour in real world may be guided by principles of bounded rationality as opposed to typically assumed fully rational behaviour, based on the principle of utility-maximization. Under such circumstances, conventional rational choice models cannot capture the decision processes. The purpose of the chapter is to propose a modeling framework that can capture both decision outcome and decision process.

The modeling framework incorporates a discrete cognitive representation structure and implies several decision heuristics, such as conjunctive, disjunctive and lexicographic rules. This allows modeling unobserved decision heterogeneity involved in a single decision, for example, in the form of a latent-class specification, taking into account mental effort, risk perception and expected outcome as explanatory factors.

Two models based on this framework are applied to decision problems underlying pedestrian shopping behaviour and compared with conventional multinomial logit models. The results show that the proposed models may not be superior to logit models in terms of model selection criteria due to the extra complexity in selecting heuristics, but suggest more interesting insights to the underlying decision mechanisms.

Understanding decision processes additional to outcomes is a promising research direction. A more developed model should take into account more contextual and socio-demographic factors in the heuristic selection part. The assumptions of information processing must be subject to empirical tests to validate the model.

The proposed modeling framework bridges the long-existing contradicting approaches in the field of decision modeling, namely the rational approach and the bounded rational approach, by proving that non-compensatory decision heuristics can be inferred from compensatory model formulations with discretized information representations and decision criteria assumed. It also incorporates a heuristic choice part into the decision processes in the form of latent-class specifications and shows the viability of the new modeling framework.

This chapter focuses on individuals’ mental representations of complex decision problems in transportation. An overview of approaches and techniques in this recent area of research is given as well as an illustration. The illustration concerns an application of CNET (causal network elicitation technique) to measure mental representations in a shopping activity scheduling task. The presence of an online shopping alternative is varied to investigate the influence of an online alternative on how individuals represent the choice problem.

Mental-model and means-ends-chain theories are discussed. These theories state that individuals when faced with a decision problem construct a mental representation of the choice alternatives by activating relevant parts of their broader causal knowledge that allow them to evaluate consequences regarding their existing needs. Furthermore, these theories emphasise that situational and person dependence of this process can explain observed variability in preferences of travellers.

The results indicate that considerable variation exists between individuals in terms of both the complexity, and the attributes and benefits that are activated in the mental representation of the choice problem. Presence of an online alternative has an influence on the benefits that individuals consider important. The impact is however small.

The chapter provides an overview of recent developments in the study of mental representations underlying choice behaviour. Traditionally, this has been the exclusive domain of qualitative research methods. The techniques reviewed enable larger samples and a formal representation of mental representations. Thus, the approach can help to better understand preference heterogeneity and incorporate this in (transport) choice models.

Several activity-based transportation models are now becoming operational and are entering the stage of application for the modelling of travel demand. In our application, we will use decision rules to support the decision-making of the model instead of principles of utility maximization, which means our work can be interpreted as an application of the concept of bounded rationality in the transportation domain. In this chapter we explored a novel idea of combining decision trees and Bayesian networks to improve decision-making in order to maintain the potential advantages of both techniques. The results of this study suggest that integrated Bayesian networks and decision trees can be used for modelling the different choice facets of a travel demand model with better predictive power than CHAID decision trees. Another conclusion is that there are initial indications that the new way of integrating decision trees and Bayesian networks has produced a decision tree that is structurally more stable.

This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models and demonstration in an agent-based microsimulation.

A descriptive theory on multidimensional travel behaviour is conceptualised. It theorizes multidimensional knowledge updating, search start/stopping criteria and search/decision heuristics. These components are formulated or empirically modelled and integrated in a unified and coherent approach.

The theory is supported by empirical observations and the derived quantitative models are tested by an agent-based simulation on a demonstration network.

Based on artificially intelligent agents, learning and search theory and bounded rationality, this chapter makes an effort to embed a sound theoretical foundation for the computational process approach and agent-based micro-simulations. A pertinent new theory is proposed with experimental observations and estimations to demonstrate agents with systematic deviations from the rationality paradigm. Procedural and multidimensional decision-making are modelled. The numerical experiment highlights the capabilities of the proposed theory in estimating rich behavioural dynamics.

This chapter discusses the formulation of an agent-based model to simulate day-to-day dynamics in activity-travel patterns, based on short and long-term adaptations to exogenous and exogenous changes.

The model is based on theoretical considerations of bounded rationality. Agents are able to explore the area, adapt their aspirations and develop habitual behaviour. If they experience dissatisfaction, stress emerges and this may lead to short or long-term adaptations of an agent’s activity-travel patterns. Both cognitive and affective responses are taken into account, when agents evaluate available options. Moreover, memory-activation and forgetting processes play a significant role in the development of habitual behaviour.

Results of numerical simulations show the effect of memory-activation and emotion-related parameters on habit formation, on the decision-making process and on overall model behaviour. Effects of specific aspects of bounded rationality on the evolution of dynamics in the activity-travel patterns of an individual are illustrated. Effects seem realistic, behaviourally rich and, therefore, more sensitive to a larger spectrum of policies.

The model is unique in its kind. It is one of the first attempts to formulate a dynamic model of activity-travel behaviour, based on principle of bounded rationality, which includes both cognitive and affective mechanism of adaptation.

This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models, and demonstration in an agent-based microsimulation.

A descriptive theory on multidimensional travel behaviour is conceptualised. It theorizes multidimensional knowledge updating, search start/stopping criteria, and search/decision heuristics. These components are formulated or empirically modelled and integrated in a unified and coherent approach.

The theory is supported by empirical observations and the derived quantitative models are tested by an agent-based simulation on a demonstration network.

Based on artificially intelligent agents, learning and search theory, and bounded rationality, this chapter makes an effort to embed a sound theoretical foundation for the computational process approach and agent-based microsimulations. A pertinent new theory is proposed with experimental observations and estimations to demonstrate agents with systematic deviations from the rationality paradigm. Procedural and multidimensional decision-making are modelled. The numerical experiment highlights the capabilities of the proposed theory in estimating rich behavioural dynamics.

This chapter explores Prospect Theory — a descriptive model of modelling individual choice making under risk and uncertainty, and its applications to a range of travel behaviour contexts.

The chapter provides background on Prospect Theory, its basic assumptions and formulations, and summarises some of its theoretical developments, applications and evidence in the field of transport research.

A body of empirical evidence has accumulated showing that the principle of maximisation of expected utility provides limited explanation of travel choices under risk and uncertainty. Prospect Theory can be seen as an alternative and promising framework for travel choice modelling (although not without theoretical and practical controversy). These findings are supported by empirical observations reported in the literature reviewed in this chapter.

The chapter provides a detailed account of the design and results of accumulated research in travel behaviour research that is based on Prospect Theory’s observations, insights and formulations. The potential of Prospect Theory for particular decision-making in travel behaviour research is articulated, main findings are presented and discussed, and limitations are identified, leading to further research needs.