An entropy based measure for comparing distributions of complexity
An entropy based measure for comparing distributions of complexity
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DOI：
10.1016/j.physa.2016.02.007
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发表时间：
2016-07-01
影响因子：
3.3
通讯作者：
Castellani, B.
中科院分区：
文献类型：
--
作者：
Rajaram, R.;Castellani, B.
本文是一系列解决复杂系统内部和之间的复杂性的多样性的经验/统计分布的一部分。在这里，我们考虑测量系统复杂性多样性的问题，给定其复杂性类型i的有序范围及其发生概率p（i），理解i的值越大，复杂性程度越高。为了解决这个问题，我们引入了一个新的复杂性度量，称为基于案例的熵C-c -Shannon-Wiener熵度量H的修改。这种测度的效用在于，与当前的复杂性测度不同--它关注单个系统的宏观复杂性-- C-c可以用来经验性地识别和度量复杂性在多个自然和人造系统内和之间的分布，以及系统任何部分的复杂性相对于有序复杂性类型的总范围的多样性贡献。(C)2016爱思唯尔B. V.保留所有权利。
This paper is part of a series addressing the empirical/statistical distribution of the diversity of complexity within and amongst complex systems. Here, we consider the problem of measuring the diversity of complexity in a system, given its ordered range of complexity types i and their probability of occurrence p(i), with the understanding that larger values of i mean a higher degree of complexity. To address this problem, we introduce a new complexity measure called case-based entropy C-c - a modification of the Shannon-Wiener entropy measure H. The utility of this measure is that, unlike current complexity measures - which focus on the macroscopic complexity of a single system - C-c can be used to empirically identify and measure the distribution of the diversity of complexity within and across multiple natural and human-made systems, as well as the diversity contribution of complexity of any part of a system, relative to the total range of ordered complexity types. (C) 2016 Elsevier B.V. All rights reserved.