EAGER: SSDIM: Leveraging Point Processes and Mean Field Games Theory for Simulating Data on Interdependent Critical Infrastructures

这个探索性研究的早期概念补助金(AGER)项目解决了建模和推理问题，以提高对相互依赖的关键基础设施(ICIS)中的相互作用和相互依赖的理解。关键应用领域是金融服务、医疗保健系统、通信技术。这项工作将产生新的机器学习方法，以生成关于基础设施相互依存关系的数据。研究结果将在学术论坛上广泛传播，同时还将努力进行研究生水平的培训。该项目制作的数据和计算机软件将通过在线数据库向公众提供。该项目包括开发新的生成模型和算法，以模拟和合成广泛的相互依赖的CI数据，以进行全面研究。利用点过程模型和平均场博弈理论对相互依赖的关键基础设施(ICI)数据进行建模和仿真。特别是，多变量霍克斯过程被用来对各种领域中行为的相互作用和相互依赖进行建模。此外，MFG框架被用来捕获个体执行的隐式优化策略，以及驱动这些策略的成本函数。这项工作解决了ICIS的机械和人类方面，在点过程模型及其演变中捕获。这项工作为定量理解和严格分析ICIS提供了生成方法和算法的理论和计算方法。

This EArly-concept Grant for Exploratory Research (EAGER) project addresses modeling and inference problems in order to improve understanding of interactions and interdependencies within interdependent critical infrastructures (ICIs). The key application areas are financial services, healthcare systems, communication technologies. This work will result in novel machine learning methodologies to generate data on infrastructure interdependencies. Findings will be widely disseminated in scholarly fora, with accompanying efforts in graduate-level training. Data and computer software produced in this project will be made publicly available via online data repositories. This project includes the development of new generative models and algorithms to simulate and synthesize extensive interdependent CI data for comprehensive study. This research focuses on modeling and simulation of interdependent critical infrastructure (ICI) data by leveraging point process models and mean field games (MFG) theory. In particular, multivariate Hawkes processes are used to model interactions and interdependencies of behaviors in a variety of domains. Additionally, an MFG framework is employed to capture the implicit optimization strategies that individuals perform, along with the cost functions that drive those strategies. This work addresses both mechanistic and human aspects of the ICIs, captured in point process models and their evolution. This work advances the theory and computational methods for generative methods and algorithms for quantitative understanding and rigorous analysis of ICIs.