If that’s what it sounds like then that is a failure of my description: the problem is nothing at all like overfitting, and casting it in that light would be pointless.
I was concerned a few years back when a "blip" at CERN resulted in theoretical physicists publishing 300+ different theories to explain it in a short period of time. All of these theories were presumably consistent with "the foundations of physics". And I guess that "blip" got rejected as a not something worth explaining anyway.
Sounds like post-hoc overfitting to me.
The actual events of the 750 GeV peak are much closer to neural networks hallucinating, and the diversity of models created doesen’t strike me as evidence of overfitting...
Anyway, you clearly didn’t trust my summary, and I no longer trust you have an honest interest in learning more, so I’ll stop here.
If your solution to the problem of not being able to generalize your model to new/other data is to add more parameters. You are probably overfitting.
These are the hallmarks of overfitting.
Sorry that you cannot learn from others and only expect them to learn from you.
Further, of the various frameworks used, many will have been created by imposing some further symmetry on the standard theory, in effect decreasing the number of free parameters!