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.