Let's see if I can simplify these ideas. Do they make a good bullet point to later pursue implementing?
Parts - tracking simulated agents is transformable to tracking trees of probability distributions, choice processes, or simulation processes - in a simple example, if everything is a sum of uniform distributions, a possible state can be found by solving for the widest distribution in an equation, based on sampling the others. this is simple subtraction, but likely produces an incorrect output distribution and should be treated as a heuristic. so each agent has a function that calculates state at a time point I came up with a class of agent where state is decided based on discrete difference from previous state, using uniform distributions the proposal is to make a uniform distribution class that produces trees when summed. these trees can then sample their state given constraints on some of their operands. it's still big for a bullet point, but sounds meaningful to pursue.
