用机器学习代理模型自动发现复杂系统中关键变量与不稳定区域。
From Model-Based Screening to Data-Driven Surrogates: A Multi-Stage Workflow for Exploring Stochastic Agent-Based Models
- 分两阶段：先筛选关键变量，再建模非线性交互关系。
- 在高维随机模型中识别出对结果影响大的不稳定参数区域。
- 适合需要自动化敏感性分析的复杂系统研究者使用。
对基于智能体的模型（ABMs）进行系统探索面临维度灾难和内在随机性的挑战。本文提出一个整合实验设计与机器学习代理模型的多阶段流程。以捕食者-猎物案例为例，方法分为两步：首先通过自动化模型驱动的筛选，识别主导变量、评估结果变异并划分参数空间；其次训练机器学习模型以捕捉剩余的非线性交互效应。该方法可自动发现系统结果高度依赖多个变量非线性耦合的不稳定区域。因此，本工作为建模者提供了一套严谨、无需人工干预的敏感性分析与政策测试框架，适用于高维随机仿真器。
原文摘要 · Abstract (English)
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.
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