"Cartography of Complex Systems"

Many complex systems need protection. Frustratingly, there is often no clear objective way to measure a system's health. The most common alternative— of waiting for parts of a system or outputs from it to degrade— can be too reactionary. This talk explores an easier alternative: comparing systems to each other. Similar to how machine learning methods can classify and predict without a mechanistic model, a set of inter-system distances can be used to create a map describing a system's behavior. These kinds of maps can then be used to predict risk and infer processes at play. I will explore an empirical example of this approach to understanding complex systems using water quality assessments of the Chesapeake Bay, and then a theoretical example of how these maps can be used to classify arbitrary network data according to their generating mechanisms.

"Cartography of Complex Systems"

Many complex systems need protection. Frustratingly, there is often no clear objective way to measure a system's health. The most common alternative— of waiting for parts of a system or outputs from it to degrade— can be too reactionary. This talk explores an easier alternative: comparing systems to each other. Similar to how machine learning methods can classify and predict without a mechanistic model, a set of inter-system distances can be used to create a map describing a system's behavior. These kinds of maps can then be used to predict risk and infer processes at play. I will explore an empirical example of this approach to understanding complex systems using water quality assessments of the Chesapeake Bay, and then a theoretical example of how these maps can be used to classify arbitrary network data according to their generating mechanisms.