 The third point of contrast is historical. In contexts where linear superposition holds, a full specification of the instantaneous state plus the equations of motion yield all the information about the system there is to know (e.g., pendulum and projectile motion). Although a full specification of such states is impossible, very small errors in specifying such states lead to very small deviations between model and target system behaviors, at least for short times and good models. By contrast, in nonlinear contexts, where linear superposition fails, a full specification of an instantaneous state of the system plus the equations of motion does not yield all the information there is about the system, for example, if there are memory effects (hysteresis), or the act of measurements introducing disturbances that SDIC can amplify. In the former case, we also need to know the history of the system as well (whether it started out below the critical point or above the critical point, say). So chaos explanations must also take model histories into account. 