 The first basic approach is to focus on successive refinements to the accuracy of the initial data used by the model while keeping the model itself fixed (e.g., Laymon 1989, p. 359). The idea here is that if a model is faithful in reproducing the behavior of the target system to some degree, refining the precision of the initial data fed to the model will lead to its behavior monotonically converging to the target system’s behavior. This is to say that as the uncertainty in the initial data is reduced, a faithful model’s behavior is expected to converge to the target system’s behavior. The import of the faithful model assumption is that if one were to plot the trajectory of the target system in an appropriate state space, the model trajectory in the same state space would monotonically become more like the system trajectory on some measure as the data is refined (I will ignore difficulties regarding appropriate measures for discerning similarity in trajectories; see Smith 2000). 