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Abstract
Diverse groups exhibit enhanced capabilities in tackling complex problems compared to individuals. Also, involving diverse stakeholders has been shown to improve the understanding of complex social-ecological systems. Considering this, we investigated how pooling the knowledge of diverse stakeholder crowds can create new, emergent knowledge on cascading drought impacts. We define ‘emergent knowledge’ as information that only becomes visible when multiple perspectives are combined. Therefore, we used participatory modeling to capture the systemic effects of droughts on diverse socio-economic and environmental systems. We interviewed 25 stakeholders with different expertise to obtain individual causal loop diagrams (CLDs) representing how drought impacts propagate in a case study in Thuringia, Germany. These CLDs were aggregated to develop a collective CLD. We then compared the individual and collective CLDs using graph theory statistics. Our analysis revealed emergent system-level features, such as feedback loops, that only became apparent when combining individual perspectives. Also, variables like ‘biodiversity loss’, which had minimal influence within the individual CLDs, gained influence in the collective CLD. These findings demonstrate how pooling diverse stakeholder knowledge on cascading drought impacts unveils new insights that may be hidden when considering only individual perspectives. We anticipate these findings to enhance the integration of knowledge from diverse stakeholder crowds when studying complex drought impacts. Furthermore, these findings highlight the need for careful consideration in selecting domain expertise in participatory processes that study drought impact cascades, as the system dynamics can vary substantially.
DOI
https://doi.org/10.31223/X56M5C
Subjects
Geography
Keywords
participatory modeling, cascading drought impacts, Thuringia, network analysis
Dates
Published: 2024-04-04 15:30
Last Updated: 2024-04-04 22:30
License
CC BY Attribution 4.0 International
Additional Metadata
Data Availability (Reason not available):
The data for the individual models, the homogenization process, and the collective model, as well as the code for performing the outlined analyses, are publicly available: github.com/jansodoge/participatory_cci_impacts_data
Conflict of interest statement:
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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