arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 0802.1840cond-mat.stat-mech
收藏

Tsallis统计量的大小限制

Size limiting in Tsallis statistics

Hari M. Gupta, Jose R. Campanha, Sidney J. Schinaider

摘要:

幂律标度性在许多物理、生物和社会经济复杂系统中被观察到,并且现在被认为是这些系统的一个重要性质。一般来说,分布的中心部分存在幂律。对于非常小和非常大的步长,它都偏离幂律。Tsallis通过非广延热力学解释了许多情况下的幂律分布,包括偏离幂律的情况,无论是小的还是非常大的步骤。在非常大的步骤的情况下,他们使用启发式交叉方法。在实际系统中,尺寸是有限的,因此,尺寸限制因素是重要的。在目前的工作中,我们提出了一种替代模型,在该模型中,我们认为,由于软化的长程相互作用或记忆的熵因子q随步长减小。这解释了对于非常大的步长幂律的偏差。最后,我们应用这个模型的科学家和考试成绩的引用指数的分布,并能够解释整个分布,包括偏离幂律。

英文摘要:

Power law scaling is observed in many physical, biological and socio-economical complex systems and is now considered as an important property of these systems. In general, power law exists in the central part of the distribution. It has deviations from power law for very small and very large step sizes. Tsallis, through non-extensive thermodynamics, explained power law distribution in many cases including deviation from the power law, both for small and very large steps. In case of very large steps, they used heuristic crossover approach. In real systems, the size is limited and thus, the size limiting factor is important. In the present work, we present an alternative model in which we consider that the entropy factor q decreases with step size due to the softening of long range interactions or memory. This explains the deviation of power law for very large step sizes. Finally, we apply this model for distribution of citation index of scientists and examination scores and are able to explain the entire distribution including deviations from power law.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑