Making the Most of Mental Models : Advancing the Methodology for Mental Model Elicitation and Documentation with Expert Stakeholders

Eliciting stakeholders’ mental models is an important participatory modeling (PM) tool for building systems knowledge, a frequent challenge in natural resource management. Therefore, mental models constitute a valu-able source of information, making it imperative to document them in detail, while pr...

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Bibliographic Details
Published in:Environmental Modelling & Software
Main Authors: LaMere, Kelsey, Mäntyniemi, Samu, Vanhatalo, Jarno, Haapasaari, Päivi
Other Authors: Environmental and Ecological Statistics Group, Helsinki Institute of Sustainability Science (HELSUS), Creative adaptation to wicked socio-environmental disruptions (WISE STN), Ecosystems and Environment Research Programme, Organismal and Evolutionary Biology Research Programme, Department of Mathematics and Statistics, Research Centre for Ecological Change, Biostatistics Helsinki, Environmental Sciences, Marine risk governance group
Format: Article in Journal/Newspaper
Language:English
Published: ELSEVIER SCI IRELAND LTD 2020
Subjects:
Online Access:http://hdl.handle.net/10138/310650
Description
Summary:Eliciting stakeholders’ mental models is an important participatory modeling (PM) tool for building systems knowledge, a frequent challenge in natural resource management. Therefore, mental models constitute a valu-able source of information, making it imperative to document them in detail, while preserving the integrity of stakeholders’ beliefs. We propose a methodology, the Rich Elicitation Approach (REA), which combines direct and indirect elicitation techniques to meet these goals. We describe the approach in the context of the effects of climate change on Baltic salmon. The REA produced holistic depictions of mental models, with more variables and causal relationships per diagram than direct elicitation alone, thus providing a solid knowledge base on which to begin PM studies. The REA was well received by stakeholders and fulfilled the substantive, normative, instrumental, and educational functions of PM. However, motivating stakeholders to confirm the accuracy of their models during the verification stage of the REA was challenging. Peer reviewed