Classification of Complex Systems Based on Transients
This work provides a tool for classifying complex systems, potentially aiding in the development of artificial life models, but it appears incremental as it builds on existing classification frameworks.
The authors tackled the problem of identifying systems capable of producing complex behavior for artificial life modeling by developing a novel classification method based on transients in deterministic discrete dynamical systems. They applied it to elementary and 2D cellular automata, achieving results that correlate well with existing manual classifications.
In order to develop systems capable of modeling artificial life, we need to identify, which systems can produce complex behavior. We present a novel classification method applicable to any class of deterministic discrete space and time dynamical systems. The method distinguishes between different asymptotic behaviors of a system's average computation time before entering a loop. When applied to elementary cellular automata, we obtain classification results, which correlate very well with Wolfram's manual classification. Further, we use it to classify 2D cellular automata to show that our technique can easily be applied to more complex models of computation. We believe this classification method can help to develop systems, in which complex structures emerge.