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Cited by 56Cognitive Science and MappingComplex Systems and Decision MakingEducational Technology and Assessment

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

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Olazabal, Marta, Neumann, Marc B., Foudi, Sébastien, Chiabai, Aline·2018·Systems Research and Behavioral Science
DOI 10.1002/sres.2519
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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

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  • FCM takes a semi-quantitative approach to describing complex systems by aggregating qualitative and quantitative data collected from mapping participants and combining the data in a way that permits an understanding of the system as a whole.

    “Overall, FCM takes what Olazabal et al. describe as a “semi-quantitative” approach to describing complex systems by aggregating qualitative and quantitative data collected from mapping participants and combining the data in a way that permits an understanding of the system as a whole [17].”

    From Supporting underrepresented students in health sciences: a fuzzy cognitive mapping approach to program evaluation (2024) · cites this paper as [17]

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Cited by

Sorted by citations

Authors
Olazabal, M., Neumann, M. B., Foudi, S., & Chiabai, A.
Affiliations
  • Basque Centre for Climate Change (ES)
  • Ikerbasque (ES)
Corresponding author
Olazabal, Marta
ORCID
Venue
Systems Research and Behavioral Science
Publisher
Wiley
Published
2018-07-17
Volume
35
Issue
6
Pages
791-810
ISSN
  • 1092-7026 (ISSN-L)
  • 1099-1743
DOI
10.1002/sres.2519
OpenAlex
W2884115963
Cited by
56
References
3
Citations by year
2026: 5 · 2025: 5 · 2024: 11 · 2023: 9 · 2022: 6 · 2021: 13 · 2020: 7
FWCI
2.82
Type
journal article
Language
en
Open access
Licence
Locations
  • Systems Research and Behavioral Science (publishedVersion, cc-by-nc)
  • Communities in ADDI (University of the Basque Country) (submittedVersion, cc-by-nc-sa)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc-sa)
  • Hispana (submittedVersion, cc-by-nc-sa)
Funders
European Commission (308337)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Cognitive Science and Mapping
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Complex Systems and Decision Making
  • Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
Keywords
fuzzy cognitive mapping, transparency, reproducibility, cognitive maps, best practices, data aggregation, analyst bias, urban adaptation, stakeholder interviews, traceability

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