Quantifying Self-Organization with Optimal Predictors
摘要
Despite broad interest in self-organizing systems, there are few quantitative, experimentally-applicable criteria for self-organization. The existing criteria all give counter-intuitive results for important cases. In this Letter, we propose a new criterion, namely an internally-generated increase in the statistical complexity, the amount of information required for optimal prediction of the system's dynamics. We precisely define this complexity for spatially-extended dynamical systems, using the probabilistic ideas of mutual information and minimal sufficient statistics. This leads to a general method for predicting such systems, and a simple algorithm for estimating statistical complexity. The results of applying this algorithm to a class of models of excitable media (cyclic cellular automata) strongly support our proposal.
引用
@article{arxiv.nlin/0409024,
title = {Quantifying Self-Organization with Optimal Predictors},
author = {Cosma Rohilla Shalizi and Kristina Lisa Shalizi and Robert Haslinger},
journal= {arXiv preprint arXiv:nlin/0409024},
year = {2011}
}
备注
Four pages, two color figures