A method for finding aggregated representations of linear dynamical systems
Artikel i vetenskaplig tidskrift, 2010

A central problem in the study of complex systems is to identify hierarchical and intertwined dynamics. A hierarchical level is defined as an aggregation of the system's variables such that the aggregation induces its own closed dynamics. In this paper, we present an algorithm that finds aggregations of linear dynamical systems, e. g. including Markov chains and diffusion processes on weighted and directed networks. The algorithm utilizes that a valid aggregation with n states correspond to a set of n eigenvectors of the dynamics matrix such that these respect the same permutation symmetry with n orbits. We exemplify the applicability of the algorithm by employing it to identify coarse grained representations of cellular automata.

complexity

prediction

aggregated Markov chains

lumpability

cellular-automata

network clustering

weak lumpability

markov-chains

aggregation of variables

state space reduction

variables

reduction

hierarchical networks

selection

cellular automata

Hierarchical dynamics

Författare

Olof Görnerup

Chalmers, Energi och miljö, Fysisk resursteori

Martin Nilsson Jacobi

Chalmers, Energi och miljö, Fysisk resursteori

Advances in Complex Systems

0219-5259 (ISSN)

Vol. 13 199-215

Ämneskategorier

Fysik

DOI

10.1142/S0219525910002542