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Complexity as a form of transition from dynamics to thermodynamics: Application to sociological and biological processes.

机译:复杂性是从动力学到热力学过渡的一种形式:应用于社会学和生物学过程。

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摘要

This dissertation addresses the delicate problem of establishing the statistical mechanical foundation of complex processes. These processes are characterized by a delicate balance of randomness and order, and a correct paradigm for them seems to be the concept of sporadic randomness. First of all, we have studied if it is possible to establish a foundation of these processes on the basis of a generalized version of thermodynamics, of non-extensive nature. A detailed account of this attempt is reported in Ignaccolo and Grigolini (2001), which shows that this approach leads to inconsistencies. It is shown that there is no need to generalize the Kolmogorov-Sinai entropy by means of a non-extensive indicator, and that the anomaly of these processes does not rest on their non-extensive nature, but rather in the fact that the process of transition from dynamics to thermodynamics, this being still extensive, occurs in an exceptionally extended time scale. Even, when the invariant distribution exists, the time necessary to reach the thermodynamic scaling regime is infinite. In the case where no invariant distribution exists, the complex system lives forever in a condition intermediate between dynamics and thermodynamics. This discovery has made it possible to create a new method of analysis of non-stationary time series which is currently applied to problems of sociological and physiological interest.
机译:本文探讨了建立复杂过程统计力学基础的微妙问题。这些过程的特点是随机性和顺序之间的微妙平衡,而针对它们的正确范例似乎是零星随机性的概念。首先,我们研究了是否有可能基于非广义性质的热力学的广义形式为这些过程建立基础。 Ignaccolo和Grigolini(2001)报道了这种尝试的详细说明,这表明这种方法会导致不一致。结果表明,没有必要通过一个非广义指标来推广Kolmogorov-Sinai熵,并且这些过程的异常并非基于其非广义性质,而在于从动力学到热力学的转变(这仍然很广泛)发生在非常长的时间范围内。即使存在不变分布,达到热力学定标范围所需的时间也是无限的。在不存在不变分布的情况下,复杂系统永远处于动力学和热力学之间的状态。这一发现使创建一种新的非平稳时间序列分析方法成为可能,该方法目前已应用于社会学和生理学的问题。

著录项

  • 作者

    Ignaccolo, Massimiliano.;

  • 作者单位

    University of North Texas.;

  • 授予单位 University of North Texas.;
  • 学科 Statistics.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 122 p.
  • 总页数 122
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 统计学;
  • 关键词

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