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来源类型Article
规范类型其他
DOI10.1038/s41598-018-28962-1
How driving rates determine the statistics of driven non-equilibrium systems with stationary distributions.
Corominas-Murtra B; Hanel R; Zavojanni L; Thurner S
发表日期2018
出处Scientific Reports 8 (1): e10837
出版年2018
语种英语
摘要Sample space reducing (SSR) processes offer a simple analytical way to understand the origin and ubiquity of power-laws in many path-dependent complex systems. SRR processes show a wide range of applications that range from fragmentation processes, language formation to search and cascading processes. Here we argue that they also offer a natural framework to understand stationary distributions of generic driven non-equilibrium systems that are composed of a driving- and a relaxing process. We show that the statistics of driven non-equilibrium systems can be derived from the understanding of the nature of the underlying driving process. For constant driving rates exact power-laws emerge with exponents that are related to the driving rate. If driving rates become state-dependent, or if they vary across the life-span of the process, the functional form of the state-dependence determines the statistics. Constant driving rates lead to exact power-laws, a linear state-dependence function yields exponential or Gamma distributions, a quadratic function produces the normal distribution. Logarithmic and power-law state dependence leads to log-normal and stretched exponential distribution functions, respectively. Also Weibull, Gompertz and Tsallis-Pareto distributions arise naturally from simple state-dependent driving rates. We discuss a simple physical example of consecutive elastic collisions that exactly represents a SSR process.
主题Advanced Systems Analysis (ASA)
关键词Statistical physics Statistics
URLhttp://pure.iiasa.ac.at/id/eprint/15377/
来源智库International Institute for Applied Systems Analysis (Austria)
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条目标识符http://119.78.100.153/handle/2XGU8XDN/131381
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Corominas-Murtra B,Hanel R,Zavojanni L,et al. How driving rates determine the statistics of driven non-equilibrium systems with stationary distributions.. 2018.
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