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Workshop III Mental representations and discrete choice behaviour State of the art and avenues for future research Background Chair: Theo Arentze Presenters: Benedict Dellaert, Geert Wets Discussants: Caspar Chorus, Harmen Oppewal • To discuss new perspectives emerging from • recent new developments in the area of models and data collection to integrate mental representations in discrete choice models − Dedicated (online) tools provide scaleability − Apporaches to integrated model development Agenda • Paper presentations − An interactive computer-based interface to support discovery of individuals’ mental representations and preferences in decision problems - Geert Wets Discussant: Caspar Chorus − Online measurement of mental representations of complex decision problems - Benedict Dellaert Discussant: Harmen Oppewal • Discussion of several key themes • Wrap-up and conclusions Traditionally Cognition Choice Data collection New Attitudes / concepts / values not related to a specific choice task Mental representation of a specific choice problem Trade-off between attributes is invariant Situation-dependent motivations / benefits Qualitative in a separate stage (e.g., focus groups) Integrated with choice tasks Dedicated (online) tools provide scaleability Model developments Bayesian network model Joint MR and choice model Reveals what is underneath individuals choice behavior Discussion • Why this approach – what are the gains? • Improved prediction of choice behavior? − One-layer multi-attribute models may fit equally well? − Changes in behavior in (radically) new situtations • Better understanding of the behavior − Extends the idea of individual-level modeling − Particularly relevant when interpretations of consequences are a-priori unclear − − − − MRs of risk MRs of strategic behavior in decision questions MRs of political program choices Influence of group discussion / assumed role − Offers new meaningful ways for segmentation Discussion cont’d • Supporting policy making − ‘More buttons to push’ − Communication of policies – effective framing − Innovation in policy making Discussion cont’d • What is it that we measure? – caveats • Explanatory value of elicited cognitions? − More than just rationalisations / justifications / errorprone reconstructions? • Influence of measurement − Framing effects and order effects • Combine with additional data − Non-verbal approaches (e.g., response time) − Information search behaviour (or eye tracking) Conclusions • Operational models and online data collections tools are now available to integrate mental representations and discrete choice models • Has value particularly in terms of better understanding, policy making and communication • Opened up new perspectives for application areas and further development