 Therefore, whether there is a perfect model or not for a target system, there is no guarantee of monotonic improvement with respect to the target system’s behavior. Traditional piecemeal confirmation strategies fail. This is the upshot of the failure of the principle of linear superposition. No matter how faithful the model, no guarantee of piecemeal monotonic improvement of a nonlinear model’s behavior with respect to the target system can be made (of course, if one waits for long enough times piecemeal confirmation strategies will also fail for linear systems). Furthermore, problems with these confirmation strategies will arise whether one is seeking to model point-valued trajectories in state space or one is using probability densities defined on state space. 