That predicting behavior at larger scales is easier than predicting it at small scales is a principle you'll encounter repeatedly in the physical science and engineering disciplines. In the physical sciences, models are often top-down, as opposed to in computing where they are almost always bottom-up. In fluid dynamics, for example, the behavior air in a turbulent flow is chaotic at the small scale. Yet, we can still design airplane wings because we have pretty good approximate models for how such flows behave at larger scales. Or to use a physics example: it's easy to predict where a baseball will land, but very complicated to predict where an electron will be in a molecule.