Ergodicity detection algorithms: Scaling of ergodicity in random symbolic dynamics

Abstract

The mathematical definitions of distinct concepts that are needed in building an ergodicity detection algorithm are introduced in a framework. This algorithmic framework is expressed in a discrete setting in an accessible manner for broader quantitative practitioners without loss of generality. At the same time, the common misconceptions of the requirement of visiting all available states in the time-averaged quantities for physical systems and non-existence of an ergodic process are resolved by introducing the distinction between Gibbs-Boltzmann and von Neumann-Birkhoff ergodic regimes. For this purpose, we introduce a new concept which is called sufficiency of sparse visit. We use finite symbolic random sequences as a pedagogical tool in establishing the different approaches for the detection of ergodic regimes of dynamical systems with vector patterns. The simple example system conveys the different attitudes in ergodicity regimes and offers guidance for building computational tools for its algorithmic detection.