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Participatory Modeling for High Complexity, Multi-System Issues: Challenges and Recommendations for Balancing

Arielle R Deutsch1,2, Leah Frerichs3, Madeline Perry3

  • 1Avera Research Institute, Avera Health, Sioux Falls, SD, USA.

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|January 20, 2025
PubMed
Summary
This summary is machine-generated.

Developing complex, participatory models requires careful stakeholder engagement, boundary definition, and qualitative/quantitative integration. Addressing these challenges is key for creating actionable insights for systemic change.

Keywords:
multiple system modelsqualitative quantitative integrationstakeholder engagement

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Area of Science:

  • Systems Science
  • Participatory Modeling
  • Computational Social Science

Background:

  • Community stakeholder participation enhances qualitative model development.
  • Participatory modeling faces challenges with high-complexity, multi-system interactions.
  • Translating qualitative models into quantitative simulations for decision-making is difficult.

Purpose of the Study:

  • To highlight practical challenges in developing high-complexity, participatory models.
  • To present recommendations for addressing these challenges in system dynamics projects.
  • To provide a foundation for research on translatable qualitative multi-system models.

Main Methods:

  • Case study of an ongoing participatory modeling project.
  • Focus on system dynamics tools for high-complexity, multi-system issues.
  • Identification of key challenges in stakeholder engagement and model integration.

Main Results:

  • Three primary challenges identified: stakeholder engagement, boundary definition, and qualitative/quantitative integration.
  • Recommendations provided to address these specific challenges.
  • Emphasis on the need for best practices in complex participatory modeling.

Conclusions:

  • Addressing engagement, boundaries, and integration is crucial for effective participatory modeling.
  • Further research is needed on methods for developing translatable qualitative multi-system models.
  • Improved models can inform actions for systemic change.