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.
Full thesis : PDF 16.9MB