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

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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.