Generative network automata

Hiroki Sayama, Department of Bioengineering, Binghamton University

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. Here we propose a novel modeling framework, "Generative Network Automata (GNA)", that can uniformly describe both state transitions and autonomous topology transformations of complex dynamical systems. The evolution of GNA is defined as a generative process realized by repetitive local subnetwork rewritings. We will present the formal definition of GNA, its generality to represent other dynamical systems models, and some preliminary results of an exhaustive sweep of possible dynamics found in elementary binary GNA with restricted updating rules.