|
| | | | |
| | | |
| Stat |
Members: 3644
Articles: 2'499'343
Articles rated: 2609
16 April 2024 |
|
| | | | |
|
Article overview
| |
|
|
Quantifying Self-Organization with Optimal Predictors | | Cosma Rohilla Shalizi
; Kristina Lisa Shalizi
; Robert Haslinger
; | | Date: |
10 Sep 2004 | | Journal: | Physical Review Letters, vol. 93, no. 11 (10 September 2004), article 118701 DOI: 10.1103/PhysRevLett.93.118701 | | Subject: | Adaptation and Self-Organizing Systems; Cellular Automata and Lattice Gases; Statistics; Statistical Mechanics; Data Analysis, Statistics and Probability | nlin.AO cond-mat.stat-mech math.ST nlin.CG physics.data-an | | Abstract: | 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. | | Source: | arXiv, nlin.AO/0409024 | | Other source: | [GID 1108069] pmid15447385 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser claudebot
|
| |
|
|
|
|
News, job offers and information for researchers and scientists:
| |
REGISTER