As global environmental change continues to accelerate and intensify, science and society are turning to transdisciplinary approaches to facilitate transitions to sustainability. Modeling is increasingly used as a technological tool to improve our understanding of social-ecological systems (SES), encourage collaboration and learning, and facilitate decision-making. This study improves our understanding of how SES models are designed and applied to address the rising challenges of global environmental change, using mountains as a representative system. We analyzed 74 peer-reviewed papers describing dynamic models of mountain SES, evaluating them according to characteristics such as the model purpose, data and model type, level of stakeholder involvement, and spatial extent/resolution. Slightly more than half the models in our analysis were participatory, yet only 21.6% of papers demonstrated any direct outreach to decision makers. We found that SES models tend to under-represent social datasets, with ethnographic data rarely incorporated. Modeling efforts in conditions of higher stakeholder diversity tend to have higher rates of decision support compared to situations where stakeholder diversity is absent or not addressed. We discuss our results through the lens of appropriate technology, drawing on the concepts of boundary objects and scalar devices from Science and Technology Studies. We propose four guiding principles to facilitate the development of SES models as appropriate technology for transdisciplinary applications: (1) increase diversity of stakeholders in SES model design and application for improved collaboration; (2) balance power dynamics among stakeholders by incorporating diverse knowledge and data types; (3) promote flexibility in model design; and (4) bridge gaps in decision support, learning, and communication. Creating SES models that are appropriate technology for transdisciplinary applications will require advanced planning, increased funding for and attention to the role of diverse data and knowledge, and stronger partnerships across disciplinary divides. Highly contextualized participatory modeling that embraces diversity in both data and actors appears poised to make strong contributions to the world’s most pressing environmental challenges.

随着全球环境变化的继续加速和加剧，科学和社会正在转向跨学科方法以促进向可持续性的过渡。建模越来越多地用作一种技术工具，以增进我们对社会生态系统（SES）的理解，鼓励合作和学习并促进决策。这项研究提高了我们对以山脉为代表的系统如何设计和应用SES模型以应对全球环境变化日益严峻挑战的认识。我们分析了74篇经同行评审的描述山区SES动态模型的论文，并根据模型目的，数据和模型类型，利益相关者参与的程度以及空间范围/分辨率等特征对它们进行了评估。在我们的分析中，略有一半以上的模型具有参与性，但只有21.6％的论文显示了与决策者的直接联系。我们发现，SES模型倾向于代表社会数据集的不足，很少包含人种学数据。与缺少或未解决利益相关者多样性的情况相比，在利益相关者多样性更高的情况下进行建模工作往往会获得更高的决策支持率。我们利用科学技术研究的边界对象和标量设备的概念，通过适当技术的角度来讨论我们的结果。我们提出了四项指导原则，以促进SES模型的开发，作为适用于跨学科应用的适当技术：（1）在SES模型设计和应用中增加利益相关者的多样性，以改善协作；（2）通过整合各种知识和数据类型来平衡利益相关者之间的动力动态；（3）提高模型设计的灵活性；（4）弥补决策支持，学习和沟通方面的差距。创建适合跨学科应用的技术的SES模型将需要进行高级规划，增加对各种数据和知识的作用的资金投入和关注，以及跨学科鸿沟的更牢固的伙伴关系。高度情境化的参与式建模同时包含数据和参与者的多样性，似乎有望为世界上最紧迫的环境挑战做出巨大贡献。创建适合跨学科应用的技术的SES模型将需要进行高级规划，增加对各种数据和知识的作用的资金投入和关注，以及跨学科鸿沟的更牢固的伙伴关系。高度情境化的参与式建模同时包含数据和参与者的多样性，似乎有望为世界上最紧迫的环境挑战做出巨大贡献。创建适合跨学科应用的技术的SES模型将需要进行高级规划，增加对各种数据和知识的作用的资金投入和关注，以及跨学科鸿沟的更牢固的伙伴关系。高度情境化的参与式建模同时包含数据和参与者的多样性，似乎有望为世界上最紧迫的环境挑战做出巨大贡献。