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Quantifying Self-Organization with Optimal Predictors

Cosma Rohilla Shalizi·Kristina Lisa Shalizi·Robert Haslinger
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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.

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Cosma Rohilla Shalizi, Kristina Lisa Shalizi, Robert Haslinger. 2004-09-10. Quantifying Self-Organization with Optimal Predictors. https://doi.org/10.1103/physrevlett.93.118701

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