The common patterns of nature.
The common patterns of nature.
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DOI：
10.1111/j.1420-9101.2009.01775.x
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发表时间：
2009-08
影响因子：
2.1
通讯作者：
Frank SA
中科院分区：
文献类型：
--
作者：
Frank SA
我们通常观察到的大规模结果是由许多隐藏的小规模过程的相互作用产生的。例子包括发病年龄、氨基酸替代率和生态群落的组成。每个问题的宏观模式通常围绕一个特征形状而变化，这个特征形状可以由中性过程产生。中性生成模型假设每个微观过程遵循无偏或随机的随机波动：网络节点的随机连接；氨基酸替换对适应度无影响；从群落中随机出现或消失的物种。这些中性的生成模型通常与自然的常见模式相匹配。在本文中，我提出了理论背景，通过这些理论背景，我们可以理解为什么这些中性生成模型如此成功。我展示了经典模式的来源，如泊松模式、正态或高斯模式，以及许多其他模式。每个经典模式往往是由一个简单的中性生成模型发现的。中性模式有一个特殊的特征：它们描述了简单信息约束下的自然模式。例如，任何只保留均值和方差信息的过程集合都倾向于高斯模式；任何只保留均值信息的聚集都会被指数模式所吸引；任何只保留几何平均值信息的聚合都会被幂律模式所吸引。基于最大熵法，提出了一种简单一致的自然共同模式信息框架。该框架表明，每个中性生成模型都是一种特殊情况，有助于发现一组特定的信息约束；这些信息约束定义了一个更广泛的非中性生成过程领域，这些过程吸引了相同的中性模式。
We typically observe large-scale outcomes that arise from the interactions of many hidden, small-scale processes. Examples include age of disease onset, rates of amino acid substitutions, and composition of ecological communities. The macroscopic patterns in each problem often vary around a characteristic shape that can be generated by neutral processes. A neutral generative model assumes that each microscopic process follows unbiased or random stochastic fluctuations: random connections of network nodes; amino acid substitutions with no effect on fitness; species that arise or disappear from communities randomly. These neutral generative models often match common patterns of nature. In this paper, I present the theoretical background by which we can understand why these neutral generative models are so successful. I show where the classic patterns come from, such as the Poisson pattern, the normal or Gaussian pattern, and many others. Each classic pattern was often discovered by a simple neutral generative model. The neutral patterns share a special characteristic: they describe the patterns of nature that follow from simple constraints on information. For example, any aggregation of processes that preserves information only about the mean and variance attracts to the Gaussian pattern; any aggregation that preserves information only about the mean attracts to the exponential pattern; any aggregation that preserves information only about the geometric mean attracts to the power law pattern. I present a simple and consistent informational framework of the common patterns of nature based on the method of maximum entropy. This framework shows that each neutral generative model is a special case that helps to discover a particular set of informational constraints; those informational constraints define a much wider domain of non-neutral generative processes that attract to the same neutral pattern.
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影响因子：
3.3
作者：
Beisel, Craig J.;Rokyta, Darin R.;Joyce, Paul
通讯作者：
Joyce, Paul
影响因子：
56.9
作者：
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通讯作者：
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影响因子：
2.7
作者：
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通讯作者：
POUSSIGUE, G
DOI：
10.1209/epl/i2000-00481-1
发表时间：
2000-12-01
期刊：
EUROPHYSICS LETTERS
影响因子：
--
作者：
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通讯作者：
Rajagopal, AK
DOI：
10.1016/s0378-4371(01)00645-8
发表时间：
2002-03-01
影响因子：
3.3
作者：
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通讯作者：
Grigolini, P