Comment by _dain_

How well does this approach scale for systems where the state is inherently large and complex? I write code that controls industrial machinery. There's just loads of different knobs and settings and modes that I can control with code, and a single production or test run will involve several heterogeneous machines, so the total state space is the cartesian product of all of those (not to mention the state of the physical widget they're all acting on, which we can only know imperfectly). I'm trying to imagine writing a data structure to represent all the different configurations the system can exist in, and it would need hundreds of different arguments just to instantiate it. The function to compute one state to another would be enormous and inscrutable. How do you deal with that?