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<blockquote>Defining structure and detecting the emergence of complexity in nature are inherently subjective, though essential, scientific activities. Despite the difficulties, these problems can be analysed in terms of how model-building observers infer from measurements the computational capabilities embedded in non-linear processes. An observer’s notion of what is ordered, what is random, and what is complex in its environment depends directly on its computational resources: the amount of raw measurement data, of memory, and of time available for estimation and inference. The discovery of structure in an environment depends more critically and subtly, though, on how those resources are organized. The descriptive power of the observer’s chosen (or implicit) computational model class, for example, can be an overwhelming determinant in finding regularity in data.<ref>

<blockquote>Defining structure and detecting the emergence of complexity in nature are inherently subjective, though essential, scientific activities. Despite the difficulties, these problems can be analysed in terms of how model-building observers infer from measurements the computational capabilities embedded in non-linear processes. An observer’s notion of what is ordered, what is random, and what is complex in its environment depends directly on its computational resources: the amount of raw measurement data, of memory, and of time available for estimation and inference. The discovery of structure in an environment depends more critically and subtly, though, on how those resources are organized. The descriptive power of the observer’s chosen (or implicit) computational model class, for example, can be an overwhelming determinant in finding regularity in data.

<blockquote>Defining structure and detecting the emergence of complexity in nature are inherently subjective, though essential, scientific activities. Despite the difficulties, these problems can be analysed in terms of how model-building observers infer from measurements the computational capabilities embedded in non-linear processes. An observer’s notion of what is ordered, what is random, and what is complex in its environment depends directly on its computational resources: the amount of raw measurement data, of memory, and of time available for estimation and inference. The discovery of structure in an environment depends more critically and subtly, though, on how those resources are organized. The descriptive power of the observer’s chosen (or implicit) computational model class, for example, can be an overwhelming determinant in finding regularity in data.<ref>

Defining structure and detecting the emergence of complexity in nature are inherently subjective, though essential, scientific activities. Despite the difficulties, these problems can be analysed in terms of how model-building observers infer from measurements the computational capabilities embedded in non-linear processes. An observer’s notion of what is ordered, what is random, and what is complex in its environment depends directly on its computational resources: the amount of raw measurement data, of memory, and of time available for estimation and inference. The discovery of structure in an environment depends more critically and subtly, though, on how those resources are organized. The descriptive power of the observer’s chosen (or implicit) computational model class, for example, can be an overwhelming determinant in finding regularity in data.

尽管是必要的科学活动，定义结构和探测自然界复杂性的涌现本质上是主观的。尽管存在这些困难，这些问题可以从建模观察者如何从测量中推断出在非线性过程中蕴含的计算的角度进行分析。观察者对于什么是有序的，什么是随机的，什么是复杂的环境的概念直接取决于它的计算资源: 原始测量数据的数量，存储空间，以及可用于计算的时间。更关键和微妙的一点是，环境中结构的发现取决于这些计算资源是如何被使用的。例如，观察者选择的(或隐含的)计算模型的描述能力，是能否在数据中找到规律性的一个极端重要的决定因素。 <ref>

尽管是必要的科学活动，定义结构和探测自然界复杂性的涌现本质上是主观的。尽管存在这些困难，这些问题可以从建模观察者如何从测量中推断出在非线性过程中蕴含的计算的角度进行分析。观察者对于什么是有序的，什么是随机的，什么是复杂的环境的概念直接取决于它的计算资源: 原始测量数据的数量，存储空间，以及可用于计算的时间。更关键和微妙的一点是，环境中结构的发现取决于这些计算资源是如何被使用的。例如，观察者选择的(或隐含的)计算模型的描述能力，是能否在数据中找到规律性的一个极端重要的决定因素。 <ref>

The low [[entropy]] of an ordered system can be viewed as an example of subjective emergence: the observer sees an ordered system by ignoring the underlying microstructure (i.e. movement of molecules or elementary particles) and concludes that the system has a low entropy.<ref>

The low [[entropy]] of an ordered system can be viewed as an example of subjective emergence: the observer sees an ordered system by ignoring the underlying microstructure (i.e. movement of molecules or elementary particles) and concludes that the system has a low entropy.

The low entropy of an ordered system can be viewed as an example of subjective emergence: the observer sees an ordered system by ignoring the underlying microstructure (i.e. movement of molecules or elementary particles) and concludes that the system has a low entropy.<ref>

The low entropy of an ordered system can be viewed as an example of subjective emergence: the observer sees an ordered system by ignoring the underlying microstructure (i.e. movement of molecules or elementary particles) and concludes that the system has a low entropy.

有序系统的低熵值可以看作是主观涌现的一个例子: 观察者通过忽略基本的微观结构(例如分子或基本粒子的运动)，并得出结论，该系统有低的熵值<ref>See f.i. Carlo Rovelli: The mystery of time, 2017, part 10: Perspective, p.105-110</ref>

有序系统的低熵值可以看作是主观涌现的一个例子: 观察者通过忽略基本的微观结构(例如分子或基本粒子的运动)，并得出结论，该系统有低的熵值<ref>See f.i. Carlo Rovelli: The mystery of time, 2017, part 10: Perspective, p.105-110</ref>