Social-ecological systems research aims to understand the nature of social-ecological phenomena, to find ways to foster or manage conditions under which desired phenomena occur or to reduce the negative consequences of undesirable phenomena. Such challenges are often addressed using dynamical systems models (DSM) or agent-based models (ABM). Here we develop an iterative procedure for combining DSM and ABM to leverage their strengths and gain insights that surpass insights obtained by each approach separately. The procedure uses results of an ABM as inputs for a DSM development. In the following steps, results of the DSM analyses guide future analysis of the ABM and vice versa. This dialogue, more than having a tight connection between the models, enables pushing the research frontier, expanding the set of research questions and insights. We illustrate our method with the example of poverty traps and innovation in agricultural systems, but our conclusions are general and can be applied to other DSM-ABM combinations.

翻译：社会生态系统研究旨在理解社会生态现象的本质，寻找促进或管理期望现象发生条件的方法，或减少不良现象的负面影响。此类挑战通常通过动力系统模型或基于主体模型进行研究。本文提出一种结合DSM与ABM的迭代流程，以整合二者优势并获得超越单一方法的认知。该流程将ABM的输出结果作为DSM构建的输入参数，后续步骤中DSM的分析结果又指导ABM的进一步分析，反之亦然。这种超越模型间紧密连接的对话机制，能够推动研究前沿的拓展，丰富研究问题与认知维度。我们以农业系统中的贫困陷阱与创新为例演示该方法，但所得结论具有普适性，可推广至其他DSM-ABM组合场景。