高复杂性、多系统问题的参与式建模:平衡定性理解和定量问题的挑战与建议

IF 1.7 3区 管理学 Q3 MANAGEMENT
Arielle R. Deutsch, Leah Frerichs, Madeleine Perry, Mohammad S. Jalali
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引用次数: 0

摘要

社区利益相关者的参与对于定性模型的开发过程具有极大的价值。然而,建模人员在参与式建模项目中经常会遇到挑战,这些项目侧重于高复杂性、多个问题、系统和粒度之间的协同互动。利益相关者的不同观点和开发此类模型所需的大量信息可能会导致定性模型难以转化为定量模拟或用于知情决策的清晰见解。在开发高复杂性的参与式模型方面,几乎没有推荐的最佳实践。我们将一个正在进行的项目作为案例研究,重点介绍利用系统动力学工具解决高复杂性、多系统问题的三个实际挑战。这些挑战包括平衡和尊重利益相关者的参与、定义变量聚合的边界和水平,以及定性/定量模型整合的时间和流程。我们针对这些挑战提出了五项建议,为进一步研究开发可转化的定性多系统模型的方法奠定了基础,以便为系统变革行动提供信息。© 2024 系统动力学学会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Participatory modeling for high complexity, multi‐system issues: challenges and recommendations for balancing qualitative understanding and quantitative questions
Community stakeholder participation can be incredibly valuable for the qualitative model development process. However, modelers often encounter challenges for participatory modeling projects focusing on high‐complexity, synergistic interactions between multiple issues, systems, and granularity. The diverse stakeholder perspectives and volumes of information necessary for developing such models can yield qualitative models that are difficult to translate into quantitative simulation or clear insight for informed decision‐making. There are few recommended best practices for developing high‐complexity, participatory models. We use an ongoing project as a case study to highlight three practical challenges for tackling high‐complexity, multi‐system issues with system dynamics tools. These challenges include balanced and respectful stakeholder engagement, defining boundaries and levels of variable aggregation, and timing and processes for qualitative/quantitative model integration. Our five recommendations to address these challenges serve as a foundation for further research on methods for developing translatable qualitative multi‐system models for informing actions for systemic change. © 2024 System Dynamics Society.
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来源期刊
CiteScore
6.60
自引率
8.30%
发文量
22
期刊介绍: The System Dynamics Review exists to communicate to a wide audience advances in the application of the perspectives and methods of system dynamics to societal, technical, managerial, and environmental problems. The Review publishes: advances in mathematical modelling and computer simulation of dynamic feedback systems; advances in methods of policy analysis based on information feedback and circular causality; generic structures (dynamic feedback systems that support particular widely applicable behavioural insights); system dynamics contributions to theory building in the social and natural sciences; policy studies and debate emphasizing the role of feedback and circular causality in problem behaviour.
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