Comment by ajkjk
I think it's a big improvement. Stiffness is something you can picture directly, so the data -> conclusions inference "stiffness" -> "mass and short range" follows directly from the facts you know and your model of what they mean. Whereas "particles have mass" -> "short range" requires someone also telling you how the inference step (the ->) works, and you just memorize this as a fact: "somebody told me that mass implies short range". You can't do anything with that (without unpacking it into the math), and it's much harder to pattern-match to other situations, especially non-physical ones.
It seems to me like the right criteria for a good model is:
* there are as few non-intuitable inferences as possible, so most conclusions can be derived from a small amount of knowledge
* and of course, inferences you make with your intuition should not be wrong
(I suppose any time you approximate a model with a simpler one---such as the underlying math with a series of atomic notions, as in this case---you have done some simplification and now you might make wrong inferences. But a lot of the wrongness can be "controlled" with just a few more atoms. For instance "you can divide two numbers, unless the denominator is zero" is such a control: division is intuitive, but there's one special case, so you memorize the general rule plus the case, and that's still a good foundation for doing inference with)
Intuition does not work in quantum mechanics. Intuition is based on observations at your scale, and this breaks dramatically at quantum levels. So this is not a good criterium.