Coupled human-environment systems are increasingly being understood as complex adaptive systems (CAS), in which micro-level interactions between components lead to emergent behavior. Agent-based models (ABMs) hold great promise for environmental policy design by capturing such complex behavior, enabling a sophisticated understanding of potential interventions. One limitation, however, is that ABMs can be computationally costly to simulate, which hinders their use for policy optimization. To address this, we propose a new statistical framework that exploits machine learning techniques to accelerate policy optimization with costly ABMs. We first develop a statistical approach for sensitivity testing of the optimal policy, then leverage a reinforcement learning method for efficient policy optimization. We test this framework on the classic ``Sugarscape'' model, an ABM for resource harvesting. We show that our approach can quickly identify optimal and interpretable policies that improve upon baseline techniques, with insightful sensitivity and dynamic analyses that connect back to economic theory.

翻译：耦合的人类-环境系统日益被理解为复杂适应系统（CAS），其中微观层面的组件相互作用导致了涌现行为。基于智能体的模型（ABMs）通过捕捉此类复杂行为，为环境政策设计提供了广阔前景，使人们能够深入理解潜在的干预措施。然而，一个局限性在于ABMs的模拟计算成本可能较高，这阻碍了其在政策优化中的应用。为解决这一问题，我们提出了一种新的统计框架，利用机器学习技术来加速高成本ABMs的政策优化过程。我们首先开发了一种用于最优政策敏感性测试的统计方法，随后采用强化学习方法进行高效的政策优化。我们在经典的“Sugarscape”模型（一种资源收获的ABM）上测试了该框架。结果表明，我们的方法能够快速识别出优于基线技术的最优且可解释的政策，并通过与经济学理论相联系的敏感性分析和动态分析提供了深刻见解。