Eckard:
I think Ian senses, along with some other physicists, that physics may have gotten lost somewhere along the way. He is looking for a method that will flag an abstraction as having no parallel in the real world. There are many ways to approach a problem like this, and if you have an open mind insights can be obtained that give you the ability to tell the difference even when the rule is not absolute.
The solution begins by understanding what abstraction is in general, and then applying that understanding to math. The human brain is designed to abstract our experiences into words so we can form a mental model that we use individually and communicate to others. This ability allows us to continue the process by abstracting some words into numbers or mathematical structures. In other words, we can create abstractions of abstractions, and with math that is how the process begins.
The process of abstraction has no natural limit, so with so-called abstract math we have created abstractions of abstractions of abstractions, etc. This gives us the first probabilistic rule of "real" abstractions. We can state it thus: The higher the level of the abstraction, the less probable an attempted application of the abstraction will work in the real world. This is not the only useful probabilistic rule, but is one of several.
To understand this rule we have to become more concrete by working with examples - a process which is a hint of other rules. Whenever examples are elusive or hard to work with, you have probably landed on an abstraction that is useless in the vast majority of situations. As a first example, consider negative numbers: are they "real"? The answer is, yes they are, but they are more abstract than positive numbers and thus apply to fewer situations. For example, if you are happily crunching away with your equations and come up with a negative human height you immediately know you have an error. Likewise with negative frequencies, negative wavelentghs, negative volume, etc. The artful mathematician might immediately jump in, beat his breast and insist, "I can make people negative 9 feet tall!" That isn't the point. The point is that it is necessary to strain mightily to find the required example, and this is true to the extent that when you end up with a negative human height in a real world problem you almost always have an error in your calculation. What we want to know is the probability of error, given only the nature of the abstraction itself.
The scarcity of negative human height, negative frequency, etc. in the real world should serve as a warning about the danger of over-abstraction. Negative numbers are one of the most useful 3rd-level abstractions (abstractions of abstractions of abstractions), but even they apply to a much smaller set of real world situations than positive numbers. The higher the level of abstraction the more useless the application as measured by the ratio of errors to correct answers in real world problems.
A corollary to this rule is that when you find yourself working with 12th level abstractions, you should have the sense to realize that applications are highly improbable, even if they aren't impossible. You are in a situation where you will make zillions of errors for every useful result, and they will be so subtle you will probably never find them or even sense that they are there.
Whenever you are in this situation, you are a fool if you don't do two things: 1) Seek lower-level reality checks, preferably in abundance. 2) Try to reduce the problem to a lower level of abstraction. If you can't do either one of these things, you have to accept the reality that your model has an unknown but extremely low probability of being correct.
Stan