Side note, it's not super helpful to tell me what I need to study in broad terms without telling me about the specific results that your argument rests on. They may or may not require deep study, but you don't know what my background is and I don't have the time to go read a textbook just because someone here tells me that if I do, I'll understand how my thinking is wrong.
That said, I really do appreciate this exchange and it has helped me clarify some ideas, and I appreciate the time it must take you to write this out. And yes, I'll happily put things on my reading list if that's the best way to learn them.
Let me offer another example that I believe captures more clearly the essence of what you're saying: A model that learns addition from everyday examples might come up with an infinite number of models like mod(a+b, N), as long as N is extremely large.
(Another side note, I think it's likely that something like this does in fact happen in currently SOTA AI.)
And, the fact that human physicists will be quick to dismiss such a model is not because it fails on data, but because it fails a heuristic of elegance or maybe naturalness.
But, those heuristics in turn are learnt from data, from the experience of successful and failing experiments aggregated over time in the overall culture of physics.
You make a distinction between experiment and observation - if this was a fundamental distinction, I would need to agree with your point, but I don't see how it's fundamental.
An experiment is part of the activity of a meta-model, a model that is trained to create successful world models, where success is narrowly defined as making accurate physical predictions.
This implies that the meta-model itself is ultimately trained on physical predictions, even if its internal heuristics are not directly physical and do not obviously follow from observational data.
In the Fermi anecdote that you offer, Fermi was talking from that meta-model perspective - what he said has deep roots in the culture of physics, but what it really is is a successful heuristic; experimental data that disagree with an elegant model would still immediately disprove the model.
If you're asking where in the books to find these results, pick up Hacking's book, he gets into it right from the get go.
With your example it is very easy to create examples where it fails on data.A physicist isn't rejecting the model because of lack of "naturalness" or "elegance", they are rejecting it because it is incorrect.
Correct. Because while an observation is part of an experiment an experiment has much more than an observation. Here's a page that goes through interventional statistics (and then moves into counterfactuals)[0]. Notice that to do this you can't just be an observer. You can't just watch (what people often call "natural experiments"), you have to be an active participant. There's a lot of different types of experiments though. While yes, physical predictions are part of how humans created physics, it wasn't the only part.That's the whole thing here. THERE'S MORE. I'm not saying "you don't need observation" I'm saying "you need more than observations". Don't confuse this. Just because you got one part right doesn't mean all of it is right.
[0] https://www.inference.vc/causal-inference-2-illustrating-int...