title: Criticality in conserved dynamical systems: experimental observation vs. exact properties.
abstract: Conserved dynamical systems are generally considered to be critical. We study a class of critical routing models, equivalent to random maps, which can be solved rigorously in the thermodynamic limit. The information flow is conserved for these routing models and governed by cyclic attractors. We consider two classes of information flow, Markovian routing without memory and vertex routing involving a one-step routing memory. Investigating the respective cycle length distributions for complete graphs, we find log corrections to power-law scaling for the mean cycle length, as a function of the number of vertices, and a sub-polynomial growth for the overall number of cycles. When observing experimentally a real-world dynamical system one normally samples stochastically its phase space. The number and the length of the attractors are then weighted by the size of their respective basins of attraction. This situation is equivalent, for theory studies, to "on the fly" generation of the dynamical transition probabilities. For the case of vertex routing models, we find in this case power law scaling for the weighted average length of attractors, for both conserved routing models. These results show that the critical dynamical systems are generically not scale-invariant but may show power-law scaling when sampled stochastically. It is hence important to distinguish between intrinsic properties of a critical dynamical system and its behavior that one would observe when randomly probing its phase space.
authors.affiliation: Institute for Theoretical Physics, Johann Wolfgang Goethe University, Frankfurt am Main, Germany.
authors.forename: Dimitrije
authors.initials: D
authors.lastname: Marković
authors.forename: Claudius
authors.initials: C
authors.lastname: Gros
authors.forename: André
authors.initials: A
authors.lastname: Schuelein
mesh_terms: D003198:Computer Simulation; D007254:Information Science / Q000379:methods*; D008390:Markov Chains; D017711:Nonlinear Dynamics; D009716:Numerical Analysis, Computer-Assisted; D011336:Probability; D013269:Stochastic Processes; D013598:Systems Theory; D013997:Time Factors