从基于模型的筛选到数据驱动的替代品：探索随机基于代理的模型的多阶段工作流程
From Model-Based Screening to Data-Driven Surrogates: A Multi-Stage Workflow for Exploring Stochastic Agent-Based Models
AI总结：
本文提出一个多阶段工作流程，通过结合系统实验设计与机器学习替代模型，解决基于代理的模型系统探索中的维度诅咒和内在随机性问题，重点在于发现不稳定区域的非线性交互效应。
摘要：
基于代理的模型（ABMs）的系统探索受到维度诅咒和内在随机性的挑战。本文提出一个多阶段流程，整合系统实验设计与机器学习替代模型。通过捕食者-猎物案例研究，我们的方法分两步进行。首先，自动化基于模型的筛选识别主导变量，评估结果变化并分割参数空间。其次，训练机器学习模型以映射剩余的非线性交互效应。这种方法自动化发现系统输出高度依赖于许多变量之间非线性交互的不稳定区域。因此，本文为模型构建者提供了一个严谨、无需干预的框架，用于敏感性分析和政策测试，即使在处理高维随机模拟器时也是如此。
英文摘要：
Systematic exploration of Agent-Based Models (ABMs) is challenged by the curse of dimensionality and their inherent stochasticity. We present a multi-stage pipeline integrating the systematic design of experiments with machine learning surrogates. Using a predator-prey case study, our methodology proceeds in two steps. First, an automated model-based screening identifies dominant variables, assesses outcome variability, and segments the parameter space. Second, we train Machine Learning models to map the remaining nonlinear interaction effects. This approach automates the discovery of unstable regions where system outcomes are highly dependent on nonlinear interactions between many variables. Thus, this work provides modelers with a rigorous, hands-off framework for sensitivity analysis and policy testing, even when dealing with high-dimensional stochastic simulators.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
