English

Long memory constitutes a unified mesoscopic mechanism consistent with nonextensive statistical mechanics

Statistical Mechanics 2015-05-28 v1

Abstract

We unify two paradigmatic mesoscopic mechanisms for the emergence of nonextensive statistics, namely the multiplicative noise mechanism leading to a {\it linear} Fokker-Planck (FP) equation with {\it inhomogenous} diffusion coefficient, and the non-Markovian process leading to the {\it nonlinear} FP equation with {\it homogeneous} diffusion coefficient. More precisely, we consider the equation p(x,t)t=x[F(x)p(x,t)]+1/2D2x2[ϕ(x,p)p(x,t)]\frac{\partial p(x,t)}{\partial t}=-\frac{\partial}{\partial x}[F(x) p(x,t)] + 1/2D \frac{\partial^2}{\partial x^2} [\phi(x,p)p(x,t)], where DRD \in {\cal R} and F(x)=V(x)/xF(x)=-\partial V(x) /\partial x, V(x)V(x) being the potential under which diffusion occurs. Our aim is to find whether ϕ(x,p)\phi(x,p) exists such that the inhomogeneous linear and the homogeneous nonlinear FP equations become unified in such a way that the (ubiquitously observed) qq-exponentials remain as stationary solutions. It turns out that such solutions indeed exist for a wide class of systems, namely when ϕ(x,p)=[A+BV(x)]θ[p(x,t)]η\phi(x,p)=[A+BV(x)]^\theta [p(x,t)]^{\eta}, where AA, BB, θ\theta and η\eta are (real) constants. Our main result can be sumarized as follows: For θ1\theta \neq 1 and arbitrary confining potential V(x)V(x), p(x,){1β(1q)V(x)}1/(1q)eqβV(x)p(x,\infty) \propto \lbrace 1-\beta(1-q)V(x)\rbrace ^{1/(1-q)} \equiv e_q^{-\beta V(x)}, where q=1+η/(θ1)q= 1+ \eta/(\theta-1). The present approach unifies into a single mechanism, essentially {\it long memory}, results currently discussed and applied in the literature.

Keywords

Cite

@article{arxiv.1106.3100,
  title  = {Long memory constitutes a unified mesoscopic mechanism consistent with nonextensive statistical mechanics},
  author = {Ananias M. Mariz and Constantino Tsallis},
  journal= {arXiv preprint arXiv:1106.3100},
  year   = {2015}
}

Comments

5 pages including 1 figure

R2 v1 2026-06-21T18:23:05.630Z