Nicolis S C
Mathematics Department, Uppsala University, PO Box 480, SE-751 06 Uppsala, Sweden.
Phys Rev E Stat Nonlin Soft Matter Phys. 2011 Jul;84(1 Pt 1):011110. doi: 10.1103/PhysRevE.84.011110. Epub 2011 Jul 11.
An approach aiming to quantify the dynamics of information within a population is developed based on the mapping of the processes underlying the system's evolution into a birth and death type stochastic process and the derivation of a balance equation for the information entropy. Information entropy flux and information entropy production are identified and their time-dependent properties, as well as their dependence on the parameters present in the problem, are analyzed. States of minimum information entropy production are shown to exist for appropriate parameter values. Furthermore, uncertainty and information production are transiently intensified when the population traverses the inflexion point stage of the logisticlike growth process.
基于将系统演化背后的过程映射为生死型随机过程以及推导信息熵的平衡方程,开发了一种旨在量化群体内信息动态的方法。识别了信息熵通量和信息熵产生,并分析了它们随时间的特性以及它们对问题中存在的参数的依赖性。结果表明,对于适当的参数值,存在信息熵产生最小的状态。此外,当群体穿越逻辑斯蒂增长过程的拐点阶段时,不确定性和信息产生会暂时增强。