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Wisdom of stakeholder crowds in complex social–ecological systems

Nature Sustainability volume 3, pages 191–199 (2020)Cite this article

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Abstract

Sustainable management of natural resources requires adequate scientific knowledge about complex relationships between human and natural systems. Such understanding is difficult to achieve in many contexts due to data scarcity and knowledge limitations. We explore the potential of harnessing the collective intelligence of resource stakeholders to overcome this challenge. Using a fisheries example, we show that by aggregating the system knowledge held by stakeholders through graphical mental models, a crowd of diverse resource users produces a system model of social–ecological relationships that is comparable to the best scientific understanding. We show that the averaged model from a crowd of diverse resource users outperforms those of more homogeneous groups. Importantly, however, we find that the averaged model from a larger sample of individuals can perform worse than one constructed from a smaller sample. However, when averaging mental models within stakeholder-specific subgroups and subsequently aggregating across subgroup models, the effect is reversed. Our work identifies an inexpensive, yet robust way to develop scientific understanding of complex social–ecological systems by leveraging the collective wisdom of non-scientist stakeholders.

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Fig. 1: Centrality profiles of different groups (in colour) and the expert reference model (in black/grey).
Fig. 2: Agreement on strong causal patterns in the FCM of stakeholder-specific groups, the crowd and the experts.
Fig. 3: Eigenvalue similarity index.
Fig. 4: The dynamic distance between the experts model and the stakeholder-derived models based on 10,000 randomly generated scenarios (experiments).
Fig. 5: The sampling and averaging effect on performance error in crowds built by drawing and aggregating mental models using two aggregation methods.

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Data availability

All data supporting the findings of this study including data for obtaining the FCM of individuals are available and can be downloaded as Excel spreadsheets (Supplementary Datasets 1,2,3). Raw data files are available from the corresponding author on reasonable request.

Code availability

Codes for mental model aggregation and FCM analyses are publically available and can be obtained on GitHub at https://github.com/payamaminpour/PyFCM/wiki.

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Acknowledgements

We thank A. McFall and J. Hilsberg for their assistance in data collection. Funding was granted to R.A. by the German Ministry for Education and Research (grant nos. 01UU0907, 033W046A and 01LC1826E), the European Union through the European Maritime and Fisheries Fund and the State of Mecklenburg-Vorpommern (grant nos. MV-I.18-LM-004, B 730117000069). Funding was granted to S.A.G. from the Socio-Environmental Synthesis Center from National Science Foundation (grant no. DBI-1052875) and to A.J.J. from National Academies of Sciences, Engineering and Medicine (grant no. 200007350).

Author information

Authors and Affiliations

  1. Department of Community Sustainability, Michigan State University, East Lansing, MI, USA

    Payam Aminpour & Steven A. Gray

  2. Department of Biology and Ecology of Fishes, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany

    Steven A. Gray & Robert Arlinghaus

  3. Collective Intelligence Unit, Copenhagen Business School, Copenhagen, Denmark

    Steven A. Gray

  4. Department of Engineering and Technology Management, Portland State University, Portland, OR, USA

    Antonie J. Jetter

  5. The School of Information Studies, Syracuse Univeristy, Syracuse, NY, USA

    Joshua E. Introne

  6. Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA

    Alison Singer

  7. Division of Integrative Fisheries Management, Albrecht-Daniel-Thaer-Institute of Agricultural and Horticultural Sciences, Humboldt-Universität zu Berlin, Berlin, Germany

    Robert Arlinghaus

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Contributions

P.A., S.A.G., A.J.J. and J.E.I. conceived the study given data collected by R.A. All authors were involved in theoretical development. P.A., S.A.G. and A.J.J. analysed the data. All authors were involved in interpretation and writing.

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Correspondence to Payam Aminpour.

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Supplementary information

Supplementary Information

Supplementary methods, discussion, references, Figs. 1–3 and Tables 1–3.

Supplementary Dataset 1

Excel file of stakeholders’ adjacency matrices of fuzzy cognitive maps.

Supplementary Dataset 2

Excel file of experts’ adjacency matrices of fuzzy cognitive maps.

Supplementary Dataset 3

Excel file of participants’ identification codes and positions.

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Aminpour, P., Gray, S.A., Jetter, A.J. et al. Wisdom of stakeholder crowds in complex social–ecological systems. Nat Sustain 3, 191–199 (2020). https://doi.org/10.1038/s41893-019-0467-z

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