Stationary and dynamical properties of information entropies in nonextensive systems

TL;DR

This paper investigates the stationary and dynamical properties of Tsallis and Fisher information entropies in nonextensive systems modeled by coupled Langevin equations, analyzing their dependence on noise, inputs, and system parameters through analytical and numerical methods.

Contribution

It provides a detailed analytical and numerical analysis of information entropies in nonextensive Langevin systems, including their transient responses and effects of colored noise.

Findings

Stationary entropies depend on noise, inputs, and system size.

Transient responses of entropies to signals are characterized.

Extended Fisher entropy relates to the Cramér-Rao inequality.

Abstract

The Tsallis entropy and Fisher information entropy (matrix) are very important quantities expressing information measures in nonextensive systems. Stationary and dynamical properties of the information entropies have been investigated in the $N$-unit coupled Langevin model subjected to additive and multiplicative white noise, which is one of typical nonextensive systems. We have made detailed, analytical and numerical study on the dependence of the stationary-state entropies on additive and multiplicative noise, external inputs, couplings and number of constitutive elements ($N$). By solving the Fokker-Planck equation (FPE) by both the proposed analytical scheme and the partial difference-equation method, transient responses of the information entropies to an input signal and an external force have been investigated. We have calculated the information entropies also with the use of the probability distribution derived by the maximum-entropy method (MEM), whose result is compared to that obtained by the FPE. The Cram\'{e}r-Rao inequality is shown to be expressed by the {\it extended} Fisher entropy, which is different from the {\it generalized} Fisher entropy obtained from the generalized Kullback-Leibler divergence in conformity with the Tsallis entropy. The effect of additive and multiplicative {\it colored} noise on information entropies is discussed also.