 Moreover, an important feature of the semantic view is that models are only intended to capture the crucial features of target systems and always involve various forms of abstraction and idealization (see  models in science).  These caveats are potentially deadly in the context of nonlinear dynamics. Any errors in our models for such systems, no matter how accurate our initial data, will lead to errors in predicting actual systems as these errors will grow (perhaps rapidly) with time. This brings out one of the problems with the faithful model assumption that is hidden, so to speak, in the context of linear systems. In the latter context, models can be erroneous by leaving out “negligible” factors and, at least for reasonable times, our model predictions do not differ significantly with the target systems we are modeling (wait long enough, however, and such predictions will differ significantly). In nonlinear contexts, by contrast, it is not so clear there are any “negligible” factors. Even the smallest omission in a nonlinear model can lead to disastrous effects because the differences these terms would have made versus their absence potentially can be rapidly amplified as the model evolves (see  §3). 