Quantifying self-organization with optimal predictors
Quantifying self-organization with optimal predictors
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DOI：
10.1103/physrevlett.93.118701
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发表时间：
2004-09-10
影响因子：
8.6
通讯作者：
Haslinger, R
中科院分区：
文献类型：
--
作者：
Shalizi, CR;Shalizi, KL;Haslinger, R
尽管对自组织系统有广泛的兴趣，但很少有定量的、实验上适用的自组织标准。现有的标准对于重要的情况都给出了反直觉的结果。在这封信中，我们提出了一个新的标准，即内部产生的统计复杂性的增加，即对系统动力学进行最佳预测所需的信息量。我们利用互信息和最小充分统计的概率思想，精确地定义了空间扩展动力系统的这种复杂性。这导致了预测这类系统的一般方法和估计统计复杂性的简单算法。将该算法应用于一类可激介质模型（循环元胞自动机）的结果有力地支持了我们的建议。
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.