Based on mental model theory, we expect individuals to construct a mental representation of the system they interact with which tends to be a strong reduction of reality and is tailored to the specific situation and task at hand. Such reductions may be particularly significant in complex decision situations involved in local spatial choice behavior. In this article, we develop a method to model and measure mental representations of decision problems involving individual spatio-temporal choice behavior in different situations. The so-called CNET method consists of an interview protocol to elicit the structures at the individual level as a causal network. We test the proposed method in a case study involving 180 respondents and an experimental shopping-trip planning task. The results indicate that the method is an adequate way of eliciting mental representations. We show how the networks revealed can be used to model and simulate reasoning and decision-making processes. © 2008 Sage Publications.

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Keywords Bayesian analysis, Bayesian belief and decision networks, Cognitive mapping, Interview protocol, Mental models, Spatial choice behavior, cognition, decision analysis, decision making, modeling
Persistent URL,
Series ERIM Article Series (EAS)
Journal Environment and Behavior
Arentze, T.A, Dellaert, B.G.C, & Timmermans, H.J.P. (2008). Modeling and measuring individuals' mental representations of complex spatio-temporal decision problems. Environment and Behavior, 40(6), 843–869. doi:10.1177/0013916507309994