在多尺度模型中基于轨迹的全球灵敏度分析.
Valentina Bazyleva1, Victoria M Garibay2, Debraj Roy3
1Faculty of Science, Informatics Institute, University of Amsterdam, Science Park 904, Amsterdam, 1098 XH, North Holland, The Netherlands. bazvalya@gmail.com.
Scientific reports
|June 17, 2024
概括
本研究提出了一个新的全球灵敏度分析 (GSA) 框架,用于基于代理的模型 (ABM). 它通过分析个体代理对整个人口的敏感性来增强对复杂系统的理解.
科学领域:
- 计算机建模和模拟.
- 复杂系统分析 复杂系统分析
- 不确定性量化不确定性的量化.
背景情况:
- 基于代理的模型 (ABM) 对于模拟复杂系统至关重要,但存在独特的分析挑战.
- 传统的全球灵敏度分析 (GSA) 方法经常与ABM固有的多层结构和时间动态作斗争.
- 需要先进的GSA框架,适合ABM的特定特征.
研究的目的:
- 引入一个新的GSA框架,专门为基于代理的模型 (ABM) 设计.
- 为了能够更全面地估计ABM中不同尺度的参数灵敏度.
- 在ABM开发和验证中,为详细的模型分析和知情决策提供强大的工具.
主要方法:
- 使用Grassmannian扩散图来减少ABM输出数据的维度.
- 使用稀疏多项式混沌扩展 (PCE) 来计算随机参数的灵敏度指数.
- 将框架应用于各种模型,包括系统动态,流行病学和经济ABM.
主要成果:
- 拟议的GSA框架有效地处理了ABM的多层结构和时间动态.
- 证明了从微观水平 (个体代理) 到宏观水平 (人口) 估计参数灵敏度的能力.
- 成功应用于各种ABM,展示了其在不同动态系统中的多功能性.
结论:
- 新的GSA框架增强了对ABM中复杂的时空过程的理解.
- 鼓励在复杂模型的不确定性量化中采用基于多元体的技术.
- 为ABM从业者提供先进的模型分析,验证和改进工具,改善常规实践.
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