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	▲	godelski 5 days ago
		`> without telling me about the specific results that your argument rests on` We've been discussing it the whole time. You even repeated it in the last comment. `A model that is accurate does not need to be causal` By causal I mean that the elements involved are directly related. We've seen several examples. The most complex one I've mentioned is the geocentric model. People made very accurate predictions with their model despite their model being wrong. I also linked two papers on the topic giving explicit examples where a LLM's world model was extracted and found to be inaccurate (and actually impossible) despite extremely high accuracy. 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. `> is not because it fails on data, but because it fails a heuristic of elegance or maybe naturalness.` 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. `> You make a distinction between experiment and observation` 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. `> This implies that the meta-model itself is ultimately trained on physical predictions` 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...