Quantifying Self-Organization in Cyclic Cellular Automata

Simple item page
dc.creatorShalizi, Cosma Rohilla
dc.creatorShalizi, Kristina Lisa
dc.date2005-07-29
dc.date.accessioned2026-07-07T05:36:36Z
dc.date.available2026-07-07T05:36:36Z
dc.descriptionCyclic cellular automata (CCA) are models of excitable media. Started from random initial conditions, they produce several different kinds of spatial structure, depending on their control parameters. We introduce new tools from information theory that let us calculate the dynamical information content of spatial random processes. This complexity measure allows us to quantitatively determine the rate of self-organization of these cellular automata, and establish the relationship between parameter values and self-organization in CCA. The method is very general and can easily be applied to other cellular automata or even digitized experimental data.
dc.description10 pages, 6 figures. This was a preliminary report on the research whose final results appeared in nlin.AO/0409024. However, this report includes certain algorithmic details and discussion of related literature omitted from the paper for reasons of space
dc.identifierhttps://arxiv.org/abs/nlin/0507067
dc.identifierhttp://arxiv.org/abs/nlin/0507067
dc.identifierLutz Schimansky-Geier, Derek Abbott, Alexander Neiman and Christian Van den Broeck (eds.),_Noise in Complex Systems and Stochastic Dynamics_ (Bellingham, Washington: SPIE, 2003), pp. 108--117
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/81046
dc.subjectAdaptation and Self-Organizing Systems
dc.subjectCellular Automata and Lattice Gases
dc.titleQuantifying Self-Organization in Cyclic Cellular Automata
dc.typetext

Files

Collections

arXiv