Computational exploration of binary-state GNA dynamics
Department of Bioengineering, Binghamton University, SUNY
Last modified: June 26, 2007
A variety of modeling frameworks have been proposed and utilized in complex systems studies, including dynamical systems models that describe state transitions within a system of fixed topology, and complex networks models that describe topological transformations of a network with little attention paid to dynamical state changes. However, many real-world complex systems exhibit both of these two dynamics simultaneously. We recently proposed a novel modeling framework, "Generative Network Automata (GNA)", that can uniformly describe both state transitions and autonomous topology transformations of complex dynamical systems as a generative process realized by repetitive local subnetwork rewritings . In this talk, we will present our latest results of extensive computational experiments that exhaustively swept over possible rewriting rules of simple binary-state GNA (which are much less restricted than assumed in ) and revealed several distinct classes of the GNA dynamics. Possible applications of GNA for complex systems modeling will also be discussed.
 Hiroki Sayama, Generative network automata: A generalized framework for modeling complex dynamical systems with autonomously varying topologies, Proceedings of the First IEEE Symposium on Artificial Life (IEEE-CI-ALife '07), Honolulu, HI, 2007, IEEE, pp.214-221.