Mazviita Chirimuuta

But it runs through science as a kind of justification for the pursuit of mathematical representations, even when they sort of depart from known facts about the concrete physical systems in reality.

The idea that the mathematical representation is getting you more to the truth, the underlying truth of how things are, as opposed to what I call the down-to-earth view of what

abstraction is and mathematical representation is that it's something that we do because of our cognitive limitations.

So instead of thinking that the abstraction gets you like the higher level of reality, just saying that we do abstraction because we're finite knowers, there's limits to how much

complexity any individual person or group of people can actually encompass in their modeling strategies or representations.

And actually, it's only by pretending things that are more simple than they actually are that we get some traction.

So that's like the down to earth mundane explanation of why abstraction is so much used in science.

I think the notion of patterns and real patterns, to invoke Dennett's term there, is a helpful one.

So one thing that you could say is going on here is that, yes, there's lots of complexity there in the natural world.

apparent in the data, but if you just denoise the data a bit, underlying there, there's a real pattern and we don't have to be like Platonists and weird about it, but there's just regularity that is sometimes masked by noise.

that doesn't seem like too metaphysically problematic.

But one of the questions that I posed to that as a challenge to that very moderate view, and I say this frequently in the book, is when you're saying that some of the apparent dysregularity in the data is irrelevant,

That's your decision as a scientist.

It's not relevant to you at the moment, but it could be relevant to someone else.

It could be really important to how that system works in the natural world for reasons that you're not aware of.

So when we sort of classify the signal versus noise in our data sets,

we shouldn't ignore the fact that those are decisions that we're bringing to bear on our investigation.

We shouldn't assume that we're just reading off the signal, the real pattern that is there in reality, and that there aren't very many other significant real patterns there.

And to the extent that we're probably also kind of creating pattern through the very denoising process that we bring about.

Yeah.