Equifinality—a situation in which multiple plausible explanations exist for a single outcome—presents a challenge for socio-environmental systems modeling. When equifinality is ignored in model calibration, subsequent policy analyses may mis-estimate the range of potential policy effects. In this paper, we present and demonstrate an approach—called DMC-RPA—for generating a set of diverse model calibrations (DMC) to enable more robust policy analysis (RPA). The optimization-based approach maximizes diversity in the model parameters and/or structural configurations to efficiently represent any equifinality in the model set. We demonstrate the approach for an agent-based model that is used to compare resilience-enhancing strategies in a smallholder farming system. Results over the set of diverse model calibrations demonstrate consistent policy effects, enabling stronger conclusions than a single model analysis. Going forward, this approach can be applied in the development of socio-environmental systems models to facilitate more robust policy analysis and inference.

均等性（对于单个结果存在多种合理解释的情况）对社会环境系统建模提出了挑战。当模型校准中忽略等价性时，后续的策略分析可能会错误估计潜在的策略影响范围。在本文中，我们介绍并演示了一种称为DMC-RPA的方法，该方法用于生成一组不同的模型校准（DMC）以实现更强大的策略分析（RPA）。基于优化的方法最大程度地提高了模型参数和/或结构配置的多样性，以有效地表示模型集中的任何均等性。我们演示了一种基于代理的模型的方法，该模型用于比较小农农业系统中的弹性增强策略。一系列不同模型校准的结果显示出一致的政策效果，与单个模型分析相比，可以得出更强的结论。展望未来，这种方法可以应用于社会环境系统模型的开发中，以促进更可靠的政策分析和推断。