Characterisation of limit measures of higher-dimensional cellular automata

TL;DR

This paper characterizes the limit measures of higher-dimensional cellular automata, showing they are governed by computability conditions similar to Turing machines, and proves many properties are undecidable.

Contribution

It extends the computability characterization of limit measures from 1D to higher dimensions and demonstrates the undecidability of certain properties in this context.

Findings

Limit measures are characterized by computability conditions.

Cellular automata exhibit the same complexity as Turing machines.

Many properties of limit measures are undecidable.

Abstract

We consider the typical asymptotic behaviour of cellular automata of higher dimension (greater than 2). That is, we take an initial configuration at random according to a Bernoulli (i.i.d) probability measure, iterate some cellular automaton, and consider the (set of) limit probability measure(s) as time tends to infinity. In this paper, we prove that limit measures that can be reached by higher-dimensional cellular automata are completely characterised by computability conditions, as in the one-dimensional case. This implies that cellular automata have the same variety and complexity of typical asymptotic behaviours as Turing machines, and that any nontrivial property in this regard is undecidable (Rice-type theorem). These results extend to connected sets of limit measures and Ces\`aro mean convergence. The main tool is the implementation of arbitrary computation in the time evolution of a cellular automata in such a way that it emerges and self-organises from a random configuration.