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Transparency and Reproducibility in Participatory Systems Modelling: the Case of Fuzzy Cognitive Mapping

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  • Marta Olazabal
  • Marc B. Neumann
  • Sébastien Foudi
  • Aline Chiabai

Abstract

By aggregating semi‐quantitative mind maps from multiple agents, fuzzy cognitive mapping (FCM) allows developing an integrated, cross‐sectoral understanding of complex systems. However, and especially for FCM based on individual interviews, the map‐building process presents potential pitfalls. These are mainly related to the different understandings of the interviewees about the FCM semantics as well as the biases of the analyst during the elicitation and treatment of data. This paper introduces a set of good practice measures to increase transparency and reproducibility of map‐building processes in order to improve credibility of results from FCM applications. The case study used to illustrate the proposed good practices assesses heatwave impacts and adaptation options in an urban environment. Agents from different urban sectors were interviewed to obtain individual cognitive maps. Using this set of data, we suggest good practices to collect, digitalize, interpret, pre‐process and aggregate the individual maps in a traceable and coherent way. © 2018 The Authors Systems Research and Behavioral Science published by International Federation for Systems Research and John Wiley & Sons Ltd

Suggested Citation

  • Marta Olazabal & Marc B. Neumann & Sébastien Foudi & Aline Chiabai, 2018. "Transparency and Reproducibility in Participatory Systems Modelling: the Case of Fuzzy Cognitive Mapping," Systems Research and Behavioral Science, Wiley Blackwell, vol. 35(6), pages 791-810, November.
    • Handle: RePEc:bla:srbeha:v:35:y:2018:i:6:p:791-810
    DOI: 10.1002/sres.2519
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    1. R. S. Clements & S. K. Birthisel & A. Daigneault & E. Gallandt & D. Johnson & T. Wentworth & M. T. Niles, 2021. "Climate change in the context of whole-farming systems: opportunities for improved outreach," Climatic Change, Springer, vol. 166(3), pages 1-20, June.
    2. López-Bernabé, Elena & Linares, Pedro & Galarraga, Ibon, 2022. "Energy-efficiency policies for decarbonising residential heating in Spain: A fuzzy cognitive mapping approach," Energy Policy, Elsevier, vol. 171(C).
    3. Vanermen, Iris & Muys, Bart & Verheyen, Kris & Vanwindekens, Frederic & Bouriaud, Laura & Kardol, Paul & Vranken, Liesbet, 2020. "What do scientists and managers know about soil biodiversity? Comparative knowledge mapping for sustainable forest management," Forest Policy and Economics, Elsevier, vol. 119(C).
    4. Goswami, Rupak & Roy, Kalyan & Dutta, Sudarshan & Ray, Krishnendu & Sarkar, Sukamal & Brahmachari, Koushik & Nanda, Manoj Kr. & Mainuddin, Mohammed & Banerjee, Hirak & Timsina, Jagadish & Majumdar, Ka, 2021. "Multi-faceted impact and outcome of COVID-19 on smallholder agricultural systems: Integrating qualitative research and fuzzy cognitive mapping to explore resilient strategies," Agricultural Systems, Elsevier, vol. 189(C).
    5. Marina Anokhina, 2020. "Parameters of the strategy for managing the economic growth of agricultural production in Russia," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 66(3), pages 140-148.
    6. Payam Aminpour & Heike Schwermer & Steven Gray, 2021. "Do social identity and cognitive diversity correlate in environmental stakeholders? A novel approach to measuring cognitive distance within and between groups," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-18, November.

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