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.
The ideas presented herein are an accumulation of research activity undertaken with a number of colleagues. I especially acknowledge the contributions made by William Greene, John Rose, David Layton, Sean Puckett, Ric Scarpa, Stephane Hess, and Joffre Swait. Discussions with Stewart Jones on belief functions were especially useful. This research is partially funded by the Australian Research Council Discovery Project Grant DP0770618.
Hensher, D.A. (2010), "Attribute Processing, Heuristics and Preference Construction in Choice Analysis", Hess, S. and Daly, A. (Ed.) Choice Modelling: The State-of-the-art and The State-of-practice, Emerald Group Publishing Limited, pp. 35-69. https://doi.org/10.1108/9781849507738-003Download as .RIS
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