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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.

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.

It has long been recognised that humans draw from a large pool of processing aids to help manage the everyday challenges of life. It is not uncommon to observe individuals adopting simplifying strategies when faced with ever increasing amounts of information to process, and especially for decisions where the chosen outcome will have a very marginal impact on their well-being. The transactions costs associated with processing all new information often exceed the benefits from such a comprehensive review. The accumulating life experiences of individuals are also often brought to bear as reference points to assist in selectively evaluating information placed in front of them. These features of human processing and cognition are not new to the broad literature on judgment and decision-making, where heuristics are offered up as deliberative analytic procedures intentionally designed to simplify choice. What is surprising is the limited recognition of heuristics that individuals use to process the attributes in stated choice experiments. In this paper we present a case for a utility-based framework within which some appealing processing strategies are embedded (without the aid of supplementary self-stated intentions), as well as models conditioned on self-stated intentions represented as single items of process advice, and illustrate the implications on willingness to pay for travel time savings of embedding each heuristic in the choice process. Given the controversy surrounding the reliability of self-stated intentions, we introduce a framework in which mixtures of process advice embedded within a belief function might be used in future empirical studies to condition choice, as a way of increasingly judging the strength of the evidence.

Patients and health professionals often make decisions which involve a choice between discrete alternatives. This chapter reviews the econometric methods which have been developed for modelling discrete choices and their application in the health economics literature. We start by reviewing the multinomial and mixed logit models and then consider issues such as scale heterogeneity, estimation in willingness to pay space and attribute non-attendance.

In this chapter, we draw on social science theoretical and empirical literatures to discuss the factors that influence buying and using an electric vehicle (EV), as well as how adopting an EV can impact on other travel choices or broader sustainability behaviours. We provide an overview of theories of technology adoption, which expose the interplay of individual, technological, and societal factors that dictate how rapidly a technology will spread throughout society. From the empirical literature, we show that far from being a purely economic or pragmatic decision, choosing an EV is also deeply grounded in social, moral and personality factors, such as self-presentation, norms and values, and appetite for risking the novel. Furthermore, since running an EV is not the same as running an internal combustion engine vehicle (ICEV), we explore how adopters adjust their behaviour to the technology, and also how EV ownership may trigger or undermine broader shifts in lifestyle required to achieve climate change and other sustainability goals. We therefore provide a critical reflection on the drivers, barriers, and behavioural implications of choosing an EV.

Purpose — The main objective of this survey is to collect data for the development of six models in a freight modeling framework. The framework aims to simulate the interactions between shippers and carriers in a freight market.

Methodology/approach — A web-based survey was designed using stated preference methods and experimental auctions, to collect information about shipper and carrier behavior when facing hypothetical situations. Hypothetical situations were constructed using information collected during the survey.

Findings — The modeling results are available for one model, the carrier selection model. In this model, data were collected using stated preference (SP) methods. Nine SP designs were developed using D-designs and an approach to minimize the nonattendance problem. A multinomial probit model was used. No bias was found due to the position of alternatives on the screen, signs of the parameters are as expected, and level of service attributes are relevant in the carrier selection process.

Research limitations/implications — The final response rate was small (about 9%) which is not uncommon in surveys with freight managers. This response rate might result in nonresponse bias of the estimates, which is the subject of future research.

Practical implications — Since freight transport is the output of a freight market, the application of the freight modeling framework presented in this chapter has potential to improve forecasts of freight flows.

Originality/value of chapter — To the best of our knowledge, the survey presented in this chapter consists of an innovative data collection procedure for the development of an original freight modeling framework.