Modeling and Measuring Individuals' Mental Representations of Complex Spatio-Temporal Decision Problems

Abstract

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.