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Participatory Development and Analysis of a Fuzzy Cognitive Map of the Establishment of a Bio-Based Economy in the Humber Region

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  • Alexandra S Penn
  • Christopher J K Knight
  • David J B Lloyd
  • Daniele Avitabile
  • Kasper Kok
  • Frank Schiller
  • Amy Woodward
  • Angela Druckman
  • Lauren Basson

Abstract

Fuzzy Cognitive Mapping (FCM) is a widely used participatory modelling methodology in which stakeholders collaboratively develop a ‘cognitive map’ (a weighted, directed graph), representing the perceived causal structure of their system. This can be directly transformed by a workshop facilitator into simple mathematical models to be interrogated by participants by the end of the session. Such simple models provide thinking tools which can be used for discussion and exploration of complex issues, as well as sense checking the implications of suggested causal links. They increase stakeholder motivation and understanding of whole systems approaches, but cannot be separated from an intersubjective participatory context. Standard FCM methodologies make simplifying assumptions, which may strongly influence results, presenting particular challenges and opportunities. We report on a participatory process, involving local companies and organisations, focussing on the development of a bio-based economy in the Humber region. The initial cognitive map generated consisted of factors considered key for the development of the regional bio-based economy and their directional, weighted, causal interconnections. A verification and scenario generation procedure, to check the structure of the map and suggest modifications, was carried out with a second session. Participants agreed on updates to the original map and described two alternate potential causal structures. In a novel analysis all map structures were tested using two standard methodologies usually used independently: linear and sigmoidal FCMs, demonstrating some significantly different results alongside some broad similarities. We suggest a development of FCM methodology involving a sensitivity analysis with different mappings and discuss the use of this technique in the context of our case study. Using the results and analysis of our process, we discuss the limitations and benefits of the FCM methodology in this case and in general. We conclude by proposing an extended FCM methodology, including multiple functional mappings within one participant-constructed graph.

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  • Alexandra S Penn & Christopher J K Knight & David J B Lloyd & Daniele Avitabile & Kasper Kok & Frank Schiller & Amy Woodward & Angela Druckman & Lauren Basson, 2013. "Participatory Development and Analysis of a Fuzzy Cognitive Map of the Establishment of a Bio-Based Economy in the Humber Region," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-14, November.
  • Handle: RePEc:plo:pone00:0078319
    DOI: 10.1371/journal.pone.0078319
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    References listed on IDEAS

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    2. Themistoklis Koutsellis & Georgios Xexakis & Konstantinos Koasidis & Alexandros Nikas & Haris Doukas, 2022. "Parameter analysis for sigmoid and hyperbolic transfer functions of fuzzy cognitive maps," Operational Research, Springer, vol. 22(5), pages 5733-5763, November.
    3. Knight, Christopher J.K. & Penn, Alexandra S. & Hoyle, Rebecca B., 2014. "Comparing the effects of mutualism and competition on industrial districts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 541-557.
    4. Anna Adamus-Matuszyńska & Jerzy Michnik & Grzegorz Polok, 2019. "A Systemic Approach to City Image Building. The Case of Katowice City," Sustainability, MDPI, vol. 11(16), pages 1-20, August.
    5. Hélène Cervo & Stéphane Ogé & Amtul Samie Maqbool & Francisco Mendez Alva & Lindsay Lessard & Alexandre Bredimas & Jean-Henry Ferrasse & Greet Van Eetvelde, 2019. "A Case Study of Industrial Symbiosis in the Humber Region Using the EPOS Methodology," Sustainability, MDPI, vol. 11(24), pages 1-32, December.
    6. Konstantinos Papageorgiou & Pramod K Singh & Elpiniki I Papageorgiou & Harpalsinh Chudasama & Dionysios Bochtis & George Stamoulis, 2020. "Participatory modelling for poverty alleviation using fuzzy cognitive maps and OWA learning aggregation," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-28, June.
    7. 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).
    8. Dariusz Banaś & Jerzy Michnik, 2019. "Evaluation of the Impact of Strategic Offers on the Financial and Strategic Health of the Company—A Soft System Dynamics Approach," Mathematics, MDPI, vol. 7(2), pages 1-20, February.
    9. Abram, Joseph J. & Dyke, James G., 2018. "Structural Loop Analysis of Complex Ecological Systems," Ecological Economics, Elsevier, vol. 154(C), pages 333-342.
    10. Ziv, Guy & Watson, Elizabeth & Young, Dylan & Howard, David C. & Larcom, Shaun T. & Tanentzap, Andrew J., 2018. "The potential impact of Brexit on the energy, water and food nexus in the UK: A fuzzy cognitive mapping approach," Applied Energy, Elsevier, vol. 210(C), pages 487-498.

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