Long-range memory elementary 1D cellular automata: Dynamics and nonextensivity

TL;DR

This study explores how long-range memory influences the dynamics of elementary 1D cellular automata, revealing significant changes in behavior near a critical memory decay exponent and linking these effects to nonextensive statistical mechanics.

Contribution

It introduces a model of cellular automata with power-law decaying memory and analyzes its impact on dynamics, especially near the critical decay exponent, using numerical simulations.

Findings

Memory effects significantly alter CA dynamics near α=1.

The entropic index q varies with α, showing a phase transition at α≈1.

Finite size scaling indicates divergence of the power-law regime with system size.

Abstract

We numerically study the dynamics of elementary 1D cellular automata (CA), where the binary state $\sigma_i(t) \in \{0,1\}$ of a cell $i$ does not only depend on the states in its local neighborhood at time $t-1$, but also on the memory of its own past states $\sigma_i(t-2), \sigma_i(t-3),...,\sigma_i(t-\tau),...$. We assume that the weight of this memory decays proportionally to $\tau^{-\alpha}$, with $\alpha \ge 0$ (the limit $\alpha \to \infty$ corresponds to the usual CA). Since the memory function is summable for $\alpha>1$ and nonsummable for $0\le \alpha \le 1$, we expect pronounced %qualitative and quantitative changes of the dynamical behavior near $\alpha=1$. This is precisely what our simulations exhibit, particularly for the time evolution of the Hamming distance $H$ of initially close trajectories. We typically expect the asymptotic behavior $H(t) \propto t^{1/(1-q)}$, where $q$ is the entropic index associated with nonextensive statistical mechanics. In all cases, the function $q(\alpha)$ exhibits a sensible change at $\alpha \simeq 1$. We focus on the class II rules 61, 99 and 111. For rule 61, $q = 0$ for $0 \le \alpha \le \alpha_c \simeq 1.3$, and $q<0$ for $\alpha> \alpha_c$, whereas the opposite behavior is found for rule 111. For rule 99, the effect of the long-range memory on the spread of damage is quite dramatic. These facts point at a rich dynamics intimately linked to the interplay of local lookup rules and the range of the memory. Finite size scaling studies varying system size $N$ indicate that the range of the power-law regime for $H(t)$ typically diverges $\propto N^z$ with $0 \le z \le 1$. Similar studies have been carried out for other rules, e.g., the famous "universal computer" rule 110.