Dynamics of Self-Adapting Networks (Corona module)

Despite the partial effectiveness of many epidemic models, the COVID-19 pandemic has revealed a very striking gap in the knowledge, how to control risk in nonlinear network dynamical systems: adaptation of models to new scenarios is too slow, hence making them explanatory but not predictive. There is clear evidence that monitoring an epidemic network alone does not cover all the ensuing networked economic, social, political, or even medical risks. The project aims to develop new mathematical theory based on stochastic dynamics, how networks can self-adapt towards new configuration/phase space layers and new dynamical rules.