Classification of complex systems by their sample-space scaling exponents

TL;DR

This paper introduces a scaling expansion method to classify complex stochastic systems based on how their phase space volume scales with system size, unifying various growth behaviors into universality classes.

Contribution

It proposes a universal scaling expansion of phase space volume that extends classification to all stochastic systems, including correlated and super-exponential ones.

Findings

Defines universality classes via scaling exponents

Extends classification to super-exponential systems

Connects phase space growth to thermodynamic properties

Abstract

The nature of statistics, statistical mechanics and consequently the thermodynamics of stochastic systems is largely determined by how the number of states $W(N)$ depends on the size $N$ of the system. Here we propose a scaling expansion of the phasespace volume $W(N)$ of a stochastic system. The corresponding expansion coefficients (exponents) define the universality class the system belongs to. Systems within the same universality class share the same statistics and thermodynamics. For sub-exponentially growing systems such expansions have been shown to exist. By using the scaling expansion this classification can be extended to all stochastic systems, including correlated, constraint and super-exponential systems. The extensive entropy of these systems can be easily expressed in terms of thee scaling exponents. Systems with super-exponential phasespace growth contain important systems, such as magnetic coins that combine combinatorial and structural statistics. We discuss other applications in the statistics of networks, aging, and cascading random walks.