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Comment by cosmic_ape | Hacker News Reader

I don't think there's a contradiction. My comment was about what it means to understand the model, or perhaps about what it means to do science, but not about what is necessary or sufficient for applied work.

But if we do talk about that though. Yes, science proceeds by modifying or sometimes totally abandoning the assumptions. No silver bullet, and the point is to assume as little as possible.

In general, if things "just work", its not a reason to abandon attempts to understand them. Things "just work" until they don't, especially in finance.

Consider two guys. Guy A, he predicts that the sun rises every day and is there until dusk. It "works", and so, as the joke goes, he does not touch anything. And guy B, he knows physics and has telescopes. And he can predict the solar eclipses. He knows the failure modes of the model of guy A. Consider the difference between the two.

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I feel like you’re arguing against yourself here. Guy A’s shallow model would easily be considered more interpretable than Guy B’s sophisticated astronomical physics model, though Guy B’s model is clearly able to articulate more detailed predictions about what might happen. Guy B’s model is less interpretable, but because it “just works” in a vastly greater number of ways (can be applied to the moon, constellations, other planets), people use it or care about it.

If all that astronomy offered was a more verbose description of why the sun rises, yielding literally zero different predictions from Guy A’s super simple model, no one would care, and might call Guy B a witch!

I’d argue that what matters for science is pure predictive efficacy. That’s it. If you can explain something, it means you can accurately predict something about an unknown state of affairs that would falsify your model if your prediction is wrong. That’s it. Any other kind of explainability is just a matter of linguistic convenience.

Of course there is overfitting, etc., but that’s just part of refining the model to yield greater predictive accuracy on unseen data (less generalization error). It’s still all about putting your predictions where your mouth is.

If the differing theories of Guy A and Guy B cannot be separated by actually testing the predictions they make, then there is no “interpretability” — just linguistic hand waving.

Incidentally I think a lot of modern focus on interpretability is actually about how to be a political gatekeeper or taste-maker through linguistic hand waving, and is not about developing models who better survive the rigors of being required to make real predictions about states of affairs.

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