Stochastic cellular automaton

Stochastic cellular automata or 'probabilistic cellular automata' (PCA) or 'random cellular automata' or locally interacting Markov chains are an important extension of cellular automaton. Cellular automata are a discrete-time dynamical system of interacting entities, whose state is discrete. Stochastic cellular automata or 'probabilistic cellular automata' (PCA) or 'random cellular automata' or locally interacting Markov chains are an important extension of cellular automaton. Cellular automata are a discrete-time dynamical system of interacting entities, whose state is discrete. The state of the collection of entities is updated at each discrete time according to some simple homogeneous rule. All entities' states are updated in parallel or synchronously. Stochastic Cellular Automata are CA whose updating rule is a stochastic one, which means the new entities' states are chosen according to some probability distributions. It is a discrete-time random dynamical system. From the spatial interaction between the entities, despite the simplicity of the updating rules, complex behaviour may emerge like self-organization. As mathematical object, it may be considered in the framework of stochastic processes as an interacting particle system in discrete-time.See for a more detailed introduction. As discrete-time Markov process, PCA are defined on a product space E = ∏ k ∈ G S k {displaystyle E=prod _{kin G}S_{k}} (cartesian product) where G {displaystyle G} is a finite or infinite graph, like Z {displaystyle mathbb {Z} } and where S k {displaystyle S_{k}} is a finite space, like for instance S k = { − 1 , + 1 } {displaystyle S_{k}={-1,+1}} or S k = { 0 , 1 } {displaystyle S_{k}={0,1}} . The transition probability has a product form P ( d σ | η ) = ⊗ k ∈ G p k ( d σ k | η ) {displaystyle P(dsigma |eta )=otimes _{kin G}p_{k}(dsigma _{k}|eta )} where η ∈ E {displaystyle eta in E} and p k ( d σ k | η ) {displaystyle p_{k}(dsigma _{k}|eta )} is a probability distribution on S k {displaystyle S_{k}} .In general some locality is required p k ( d σ k | η ) = p k ( d σ k | η V k ) {displaystyle p_{k}(dsigma _{k}|eta )=p_{k}(dsigma _{k}|eta _{V_{k}})} where η V k = ( η j ) j ∈ V k {displaystyle eta _{V_{k}}=(eta _{j})_{jin V_{k}}} with V k {displaystyle {V_{k}}} a finite neighbourhood of k. See for a more detailed introduction following the probability theory's point of view. There is a version of the majority cellular automaton with probabilistic updating rules. See the Toom's rule. PCA may be used to simulate the Ising model of ferromagnetism in statistical mechanics.Some categories of models were studied from a statistical mechanics point of view. There is a strong connectionbetween probabilistic cellular automata and the cellular Potts model in particular when it is implemented in parallel. The Galves-Locherbach model is an example of a generalized PCA with a non Markovian aspect.