The talk presents recent results on how to solve the general problem of computing the time evolution of means and moments of ``occurence counts in rule-based models; computability is in the sense of Weihrauch. Roughly, established techniques, namely adaptive uniformization and finite state projection, can be reused – provided that occurrence counts (and their powers) are bounded by a polynomial in the ``size of the rule-based system and the ``size'' of the system does not explode. The most interesting results can be obtained for context-free systems, which can be thought of as branching processes equipped with structure; for these systems we have PTIME computability.

All results will be exemplified for the case of string rewriting, aiming for a widest possible audience of computer scientists, assuming only basic knowledge of continous time Markov chains with countable state space. Computing the time evolution of mean word counts in stochastic string rewriting is already highly non-trivial, even if it is the most basic example. The GEM OF THE TALK is the computation of mean word counts for context-free grammars!