 What both of these basic approaches have in common is that piecemeal monotonic convergence of model behavior to target system behavior is a mark for confirmation of the model (Koperski 1998). By either improving the quality of the initial data or improving the quality of the model, the model in question reproduces the target system’s behavior monotonically better and yields predictions of the future states of the target system that show monotonically less deviation with respect to the behavior of the target system. In this sense, monotonic convergence to the behavior of the target system is a key criterion for whether the model is confirmed. If monotonic convergence to the target system behavior is not found by pursuing either of these basic approaches, then the model is considered to be disconfirmed. 