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第55行： − 一些复杂性度量是算法性质的，并试图确定最小描述长度。对于这些度量，复杂性仅适用于描述，而不适用于系统或流程本身。其他衡量复杂性的方法考虑到一个系统的时间演变，并且往往建立在统计信息理论的理论基础之上。+ + +
→复杂性的度量
Some measures of complexity are algorithmic in nature and attempt to identify a minimal description length. For these measures complexity applies to a description, not the system or process per se. Other measures of complexity take into account the time evolution of a system and often build on the theoretical foundations of statistical information theory.
Some measures of complexity are algorithmic in nature and attempt to identify a minimal description length. For these measures complexity applies to a description, not the system or process per se. Other measures of complexity take into account the time evolution of a system and often build on the theoretical foundations of statistical information theory.
一些复杂性度量是算法性质的，并试图确定最小描述长度（描述一个算法所需要的最短长度）。对于这些度量，复杂性仅适用于描述，而不适用于系统或流程本身。其他衡量复杂性的方法考虑到一个系统的时间演变，并且往往建立在统计信息理论的理论基础之上。
Most extant complexity measures can be grouped into two main categories. Members of the first category (algorithmic information content and logical depth) all capture the randomness, information content or description length of a system or process, with random processes possessing the highest complexity since they most resist compression. The second category (including statistical complexity, physical complexity and neural complexity) conceptualizes complexity as distinct from randomness. Here, complex systems are those that possess a high amount of structure or information, often across multiple temporal and spatial scales. Within this category of measures, highly complex systems are positioned somewhere between systems that are highly ordered (regular) or highly disordered (random). <figref>Complexity_figure1.jpg</figref> shows a schematic diagram of the shape of such measures, however, it should be emphasized again that a generally accepted quantitative expression linking complexity and disorder does not currently exist.
Most extant complexity measures can be grouped into two main categories. Members of the first category (algorithmic information content and logical depth) all capture the randomness, information content or description length of a system or process, with random processes possessing the highest complexity since they most resist compression. The second category (including statistical complexity, physical complexity and neural complexity) conceptualizes complexity as distinct from randomness. Here, complex systems are those that possess a high amount of structure or information, often across multiple temporal and spatial scales. Within this category of measures, highly complex systems are positioned somewhere between systems that are highly ordered (regular) or highly disordered (random). <figref>Complexity_figure1.jpg</figref> shows a schematic diagram of the shape of such measures, however, it should be emphasized again that a generally accepted quantitative expression linking complexity and disorder does not currently exist.
大多数现存的复杂性度量可以分为两大类。第一类(算法信息内容和逻辑深度)的复杂性度量试图捕捉系统或过程的随机性、信息内容或描述长度。随机过程具有最高的复杂性，因为它们最难以压缩。第二类(包括统计复杂性、物理复杂性和神经复杂性)将复杂性概念化为有别于随机性。在这里，复杂系统是那些拥有大量结构或信息的系统，通常跨越多个时间和空间尺度。在这一类度量中，高度复杂的系统处于高度有序(规则)和高度无序(随机)的系统之间。<figref>Complexity_figure1.jpg</figref> 展示了这些测量方法的示意图。然而，应该再次强调的是，目前并不存在一个普遍接受的、将复杂性和无序性联系起来的定量表达式。
==复杂与随机==
==复杂与随机==