Social-ecological systems (SES) research aims to understand the nature of social-ecological phenomena, to find effective ways to foster or manage conditions under which desirable phenomena, such as sustainable resource use, occur or to change conditions or reduce the negative consequences of undesirable phenomena, such as poverty traps. Challenges such as these are often addressed using dynamical systems models (DSM) or agent-based models (ABM). Both modeling approaches have strengths and weaknesses. DSM are praised for their analytical tractability and efficient exploration of asymptotic dynamics and bifurcation, which are enabled by reduced number and heterogeneity of system components. ABM allows representing heterogeneity, agency, learning and interactions of diverse agents within SES, but this also comes at a price such as inefficiency to explore asymptotic dynamics or bifurcations. In this paper we combine DSM and ABM to leverage strengths of each modeling technique and gain deeper insights into dynamics of a system. We start with an ABM and research questions that the ABM was not able to answer. Using results of the ABM analysis as inputs for DSM, we create a DSM. Stability and bifurcation analysis of the DSM gives partial answers to the research questions and direct attention to where additional details are needed. This informs further ABM analysis, prevents burdening the ABM with less important details and reveals new insights about system dynamics. The iterative process and dialogue between the ABM and DSM leads to more complete answers to research questions and surpasses insights provided by each of the models separately. We illustrate the procedure with the example of the emergence of poverty traps in an agricultural system with endogenously driven innovation.

翻译：社会-生态系统（SES）研究旨在理解社会-生态现象的本质，寻找有效途径来培育或管理理想现象（如可持续资源利用）发生的条件，或改变条件以减少非理想现象（如贫困陷阱）的负面后果。此类挑战通常通过动力系统模型（DSM）或基于智能体模型（ABM）来应对。这两种建模方法各有优劣。DSM因其分析可处理性以及能够高效探索渐近动力学和分岔现象而备受赞誉，这得益于其系统组件数量较少且异质性较低。ABM则允许表征SES中多样化智能体的异质性、自主性、学习能力及相互作用，但代价是难以高效探索渐近动力学或分岔现象。本文通过结合DSM与ABM，充分发挥每种建模技术的优势，从而更深入地洞察系统动力学。我们以一个ABM及其未能回答的研究问题为起点，将ABM分析结果作为DSM的输入，构建了DSM。对DSM的稳定性与分岔分析为研究问题提供了部分答案，并指出了需要补充额外细节的方向。这进一步指导了ABM分析，避免ABM承载过多次要细节，并揭示了关于系统动力学的新见解。ABM与DSM之间的迭代过程与对话使研究问题得到更完整的解答，超越了两种模型各自单独提供的洞见。我们以内生驱动创新的农业系统中贫困陷阱的出现为例，阐述了这一流程。