Neutral Emergence and Coarse Graining Cellular Automata

Abstract

Emergent systems are often thought of as special, and are often linked to desirable properties like robustness, fault tolerance and adaptability. But, though not well understood, emergence is not a magical, unfathomable property.

We introduce neutral emergence as a new way to explore emergent phenomena, showing that being good enough, enough of the time may actually yield more robust solutions more quickly.

We then use cellular automata as a substrate to investigate emergence, and find they are capable of exhibiting emergent phenomena through coarse graining. Coarse graining shows us that emergence is a relative concept - while some models may be more useful, there is no correct emergent model - and that emergence is lossy, mapping the high level model to a subset of the low level behaviour.

We develop a method of quantifying the 'goodness' of a coarse graining (and the quality of the emergent model) and use this to find emergent models - and, later, the emergent models we want - automatically.