Choice Modelling
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Choice Modelling

The State of the Art and the State of Practice

Edited by Stephane Hess and Andrew Daly

Choice modelling has been one of the most active fields in economics over recent years. This valuable new book contains leading contributions from academics and practitioners from across the different areas of study where choice modelling is a key analytical technique, drawn from a recent international conference.
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Chapter 5: Workshop report: mental representations and discrete choice behaviour: state-of-the-art and avenues for future research

The State of the Art and the State of Practice

Benedict G.C. Dellaert, Theo Arentze, Caspar G. Chorus, Harmen Oppewal and Geert Wets


This chapter considers the measurement and modelling of mental representations underlying individuals’ choice behaviour. The premise of the study of such mental representations (MRs) is that an individual’s mental construction of a choice problem plays an important role in preference formation, especially in the context of the complex choice situations such as involved in activity-travel behaviour. The measurement of MRs is a key problem. It requires the availability of a cognitive mapping method that can take specific decision task characteristics into account. Existing cognitive mapping methods, such as proposed in Kearney and Kaplan (1997), Eden and Ackermann (1998), and Christensen and Olsen (2002), cannot readily be used for this purpose. These traditional cognitive mapping techniques focus on knowledge and values not related to a specific situation or decision task. In contrast, MRs underlying decision making concern specific cognitions that are triggered by a given decision problem and should allow the decision maker (DM) to assess outcomes of actions in terms of needs that are activated in the decision situation. To model and measure such cognitions, recently new methodology has been developed based on the concept of active causal networks. Specifically, Arentze et al. (2008) proposed the decision network (DN) as a suitable formalism. A DN is an extension of a Bayesian Network (BN).

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