  Cellular automata (CA) consist of an array of identical cells, each of which
may take one of a finite number of possible states. The entire array evolves in
discrete time steps by iterating a global evolution G. Further, this global
evolution G is required to be shift-invariant (it acts the same everywhere) and
causal (information cannot be transmitted faster than some fixed number of
cells per time step). At least in the classical, reversible and quantum cases,
these two top-down axiomatic conditions are sufficient to entail more
bottom-up, operational descriptions of G. We investigate whether the same is
true in the probabilistic case. Keywords: Characterization, noise, Markov
process, stochastic Einstein locality, screening-off, common cause principle,
non-signalling, Multi-party non-local box.
