>"the problem has been that there was a theory that could explain almost perfectly everything within a certain region of physics, but can't easily be extended."
Sounds a lot like overfitting.
Sounds a lot like overfitting.
Compare the classical physics formula for momentum, p=mv, vs relativistic formula, p=γmv. γ is almost 1 for most low velocities, it only starts jumping up to infinity when we get close to c.
The point being that the classical formula is pretty good in it's zone of low velocities, but as soon as you get too far out of the implicit term's "constraints" the formula breaks down and you need to add more to it to get it working for both low and high velocities. Which doesn't sound easy.
But they can pare all that down to something expressed in terms people are familiar with. And that has the bonus purpose of helping them understand why the familiar terms were familiar: the "correction factor" is small under circumstances we encounter, and only becomes large under circumstances we rarely do.
If that intrigues somebody enough to learn the actual physics, they'll encounter a completely different and more-encompassing formulation which looks not at all like overfitting. One that turns out to be more elegant, in fact, cramming more information into less notation. But it's information nobody needs until they're doing fairly advanced physics, so we're not going to be teaching it in elementary school any time soon.
This is not a "theory" problem, it has to do with matching the theory to observations.
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!