UBC Theses and Dissertations
Metaphors and mental models of risk : expert thinking about ecosystems Smith, William G. B.
Ecosystems are important to human survival and well-being (MEA, 2005). Human knowledge of ecosystems largely consists of the conceptual distinctions made by experts. Experts use different metaphors and mental models to define ecosystem risks and shape our thinking. Decision makers face a dilemma when experts use conflicting frames of reference to define the same problem. This research attempts to answer three questions. Do experts have different mental models of risk? Do their mental models of risk stem from different underlying worldviews? Do their worldviews arise from differences in their professional background or work experience? Repertory grid methodologies were used to elicit expert mental models. The analysis relied on Shaw and Gaines’s (1989) methodology for comparing experts’ conceptual systems. The data for this study was collected through a two-stage survey. The interview stage of the survey was used to identify widely shared conceptual distinctions experts use to talk about ecosystems. During the second stage, experts were asked to complete a web-based questionnaire, applying these shared distinctions to describe how several concepts or types of ecosystems differed from one another. The Interview stage of the survey revealed that ecological integrity was the dominant mental model of risk and the experts shared a common set of beliefs. These beliefs were most heavily influenced by the experts’ current position and previous work experience. The Internet stage of the survey revealed that the eco-concepts shared more similarities than differences. The eco-concepts as a whole belonged to one of two metaphoric categories that distinguished pristine from human-impacted systems. There was also a broad consensus among experts in the way they used the derived assessment criteria to describe the eco-concepts. The findings suggest that the survey subjects shared a common underlying mental model of ecosystem risk.
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