Emergence of Tsallis Statistics from a Self-Referential Nonlinear Operator: A Variational Framework

TL;DR

This paper introduces a variational framework linking nonlinear operators to Tsallis statistics, deriving the entropic index q from structural exponents without prior assumptions.

Contribution

It establishes an operator-theoretic foundation for nonextensive statistical mechanics, deriving Tsallis distributions from self-referential feedback mechanisms.

Findings

Tsallis q-exponential distribution emerges as equilibrium state

The entropic index q equals the sum of structural exponents alpha and beta

The framework predicts a generalized equation of state and critical temperature

Abstract

We develop a variational thermodynamic framework for statistical systems governed by a self-referential nonlinear operator Omega characterized by structural exponents alpha > 0, beta >= 0, a symmetric kernel K, and a self-coupling constant kappa >= 0. The central object is the self-consistency entropy S[Psi] = -D_KL(Psi || Omega Psi), which vanishes at the fixed points of Omega and serves as a natural Lyapunov functional. Within the local kernel (mean-field) approximation, minimization of the free energy F = U - T S admits the Tsallis q-exponential distribution as an equilibrium state, with the entropic index q = alpha + beta emerging directly from the fixed-point structure of the operator rather than being postulated. The framework yields a consistent thermodynamic description, including a generalized equation of state PV = (2 - q) T, response functions, and a critical temperature associated with spontaneous symmetry breaking. The relation q = alpha + beta connects independently measurable structural exponents of the feedback mechanism to the observed tail index, providing a parameter-free criterion that distinguishes this approach from superstatistics, constrained entropy maximization, and q-deformed formalisms. This work establishes an operator-theoretic foundation for nonextensive statistical mechanics in which nonlinear self-referential feedback naturally generates Tsallis statistics in the mean-field limit.