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Algorithmic information content was defined (Kolmogorov, 1965; Chaitin, 1977) as the amount of information contained in a string of symbols given by the length of the shortest computer program that generates the string. Highly regular, periodic or monotonic strings may be computed by programs that are short and thus contain little information, while random strings require a program that is as long as the string itself, thus resulting in high (maximal) information content. Algorithmic information content (AIC) captures the amount of randomness of symbol strings, but seems ill suited for applications to biological or neural systems and, in addition, has the inconvenient property of being uncomputable. For further discussion see [[algorithmic information theory]].
Algorithmic information content was defined (Kolmogorov, 1965; Chaitin, 1977) as the amount of information contained in a string of symbols given by the length of the shortest computer program that generates the string. Highly regular, periodic or monotonic strings may be computed by programs that are short and thus contain little information, while random strings require a program that is as long as the string itself, thus resulting in high (maximal) information content. Algorithmic information content (AIC) captures the amount of randomness of symbol strings, but seems ill suited for applications to biological or neural systems and, in addition, has the inconvenient property of being uncomputable. For further discussion see [[algorithmic information theory]].
生成一个符号串的'''算法信息内容 Algorithmic Information Content''' 的定义(Kolmogorov，1965; Chaitin，1977)是生产这个符号串所需的最短计算机程序所包含的信息量。高度规则、周期性或单调的符号串可以由短小的程序生产，因此包含的信息很少，而完全随机的符号串需要一个和该符号串本身至少一样长的程序，因此产生高(最大)信息内容。算法信息内容(AIC)捕获了符号串的随机性，但似乎不适合应用于生物或神经系统。此外，算法信息内容虽然是一个精妙的概念，但在数学上是不可计算的，因此有诸多不便。
生成一个符号串的'''算法信息内容 Algorithmic Information Content''' 的定义(Kolmogorov，1965; Chaitin，1977)是生产这个符号串所需的最短计算机程序所包含的信息量。高度规则、周期性或单调的符号串可以由短小的程序生产，因此包含的信息很少，而完全随机的符号串需要一个和该符号串本身至少一样长的程序，因此产生高(最大)信息内容。举个例子，“0.333333...”所包含的算法信息内容很小，可以用“1/3”来生成；“3.14159265358974”次之，可以用求解圆周率的程序生成；“27648.73649325932”的算法信息内容最大，因为他是完全随机的（我随便敲键盘敲出来的），只能用它自己生产自己。算法信息内容(AIC)捕获了符号串的随机性，但似乎不适合应用于生物或神经系统。此外，算法信息内容虽然是一个精妙的概念，但在数学上是不可计算的，因此有诸多不便。
Effective measure complexity (Grassberger, 1986) quantifies the complexity of a sequence by the amount of information contained in a given part of the sequence that is needed to predict the next symbol. Effective measure complexity can capture structure in sequences that range over multiple scales and it is related to the extensivity of entropy (see below).
Effective measure complexity (Grassberger, 1986) quantifies the complexity of a sequence by the amount of information contained in a given part of the sequence that is needed to predict the next symbol. Effective measure complexity can capture structure in sequences that range over multiple scales and it is related to the extensivity of entropy (see below).
对于一个序列，如果已知其中的多少部分，就能预测剩下来的部分？这已知部分的序列中包含的信息，就是整个序列的'''有效度量复杂性 Effective Measure Complexity''' (Grassberger，1986)。有的书读了开头就能预测生下来的全部内容，那这本书的有效信息就只有开头所包含的信息量而已。有效度量复杂度可以捕捉在多个尺度范围内的序列中的结构，这与熵的扩展性有关(见下文)。