"Understanding if and how things escalate"
Guest Lecture Science Camp 2020
The Sars-CoV-2 pandemic has put mathematical modelling into the spotlight. Predictions about the number of new infections have led to political, social and economic decisions of unprecedented proportions. The pandemic has, however, also revealed the limitations and pitfalls with mathematical modelling of complex dynamical systems.
It may seem like a contradiction, but it works: We can represent a complex system by something simpler, a model, which then allows us to (i) understand the mechanisms by which the complex system functions, and (ii) make predictions about its behaviour. How can we simplify something, that we do not understand, in order to understand it?
Welcome to the world of modelling in science!
In the lecture "Understanding if and how things escalate", I am going to review mathematical models, including statistical models, machine learning models, graph models, dynamical systems models. The applications of these models range from molecular dynamic simulations, to cellular networks, epidemiology, and climate research. I want to focus on the principal ideas, the opportunities and limitations of systems approaches. I am going to explain the statement “All models are wrong, but some are useful.”