At first "overfitting" was the concept that leaped to mind as I read it, but overfitting is sort of something different. You have a complicated data set, and when you overfit, you keep adding dimensions of freedom, and then setting those dimensions of freedom to produce the original values that were passed in, such that it fails to correctly predict any other instance drawn from some conceptual "same distribution".

In this case, the theories do the "adding many dimensions of freedom" part, but they don't really fix the values; they probably haven't got data to fix in the first place, and it's not mathematical enough to be that precise about the fitting.

I think it's more like these theories are often not so much hypotheses themselves, but the descriptions of hypotheses spaces, and the addition of "nuance" means that in a lot of cases, what sounds like a "theory" is really just the description of a space that happens to contain all or most of the possibility space in question. As much fun as it may be to do that, and as much as it may even be a necessary first step to a real theory, in practice that is not generally the difficult part of the process. (There are exceptions to that which may leap to mind but I submit they would be the exceptions, rather than the rule.) When one is working with fuzzy words (even if by necessity!) and not math, the fuzzy words make it very easy to make this mistake, I think. English does not have a strong distinction between a hypothesis space and a specific hypothesis in that space, as evidenced by the very fact I have to dip into jargon to even make that point!

From that perspective, the solution that leaps to my mind is implement the observation that in many ways, the important thing about a theory is not what it describes, but what it excludes. It is too easy and cognitively tempting to try to create a theory that, when you fiddle with the parameters, describes everything you see, but the real questions are, what is excluded by your theory? Can I fiddle with the knobs in your theory to describe something that you say ought to exist, but does not seem to? Is your theory even well-enough specified that these are meaningful questions to ask about it?

I don't know the answers to these questions for sociology. I have to admit I've tried to engage with it a couple of times, because what fan of Isaac Asimov doesn't want to learn about some real-life psychohistory, but I've always found it difficult to get traction with with my engineer mind. I can engage from a philosophical point of view, but that is not generally what I was looking for; I've got, well, philosophy itself for that. (Repeat previous sentence for history, psychology, and economics.)

To p4wnc6's question, I'd submit that as dismal as the Dismal Science may sometimes be, it does often meet this bar. I fear economists themselves may often be quite sluggish at recognizing when their own theories are contradicted by the evidence because the theories are saying that what is currently happening (in the general sense, not the literal March 27, 2016 sense) is excluded by the theory, but that is a human problem, not an economic theory problem. Economic theories do exclude things; if supply rises and demand holds steady, the vast majority of possibilities where price goes up are excluded, and if price were to go up, the theories would be useful in investigating how such a thing actually happened by examining what such theories would still permit (e.g., perhaps supply isn't really rising because the new supply is actually a higher quantity of lower quality, and too much of the demand is for a higher quality than the new supply is supplying which means "real" supply is dropping, or something like that). They certainly aren't perfect and a given theory may even be wrong, but, well, the very fact that a theory can be wrong is evidence that it is falsifiable, and thus not "nuanced" into infinite psudo-explanatory power.