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Center for Nonlinear Studies |
Real-world bio-social habitats are often represented as co-evolving complex networks. Reasoning about such complex systems is complicated and scientifically challenging due to their size, co-evolutionary nature and multiple contagions spreading simultaneously. Examples include: human immune system, The 2019 COVID-19 pandemic, 2009 financial crisis, 2003 Northeast power blackout, global migration, information propagation over social media, societal impacts of natural and human initiated disasters and the effect of climate change. Advances in computing have fundamentally altered how such bio-social complex systems can be synthesized, analyzed and reasoned.
Graphical Dynamical systems (GDS) can be used to model and represent large co-evolving bio-social habitats. The talk will describe a computational theory of GDS with the aim of developing scalable and practical decision support systems coevolving bio-social habitats. The role of AI and high performance computing will be highlighted. I will draw on our work in urban transport planning, national security and public health epidemiology to guide the discussion.
Host: Nidhi Parikh (A-1)