jegp

You make some great points that I largely agree with. I'm wondering whether I'm misrepresenting you though, because I'm not arriving at the same "everything looks like a nail" conclusion.

One thing I'd like to surface is the distinction you're making between software and physics. You point out that social and medical sciences are only now becoming approachable with scientific methodology. We can now, to a higher degree than before at least, model and predict what'll happen in social and medical scenarios. Isn't that an excellent example of how subject matters can be virtualized? I can't find good arguments for why this would stop there; why wouldn't our scientific models improve to such a degree that we can predict and "interfacte" with more and more of reality. Put plainly: why can't experiments be done in code one day? Particularly if we imagine having better robotics.

Do you see my point about closing the gap between the theorizing and experimenting? Or are they fundamentally different things? The "reasonably skilled person" seems like an excellent heuristic > 2 years ago that's rapidly being replaced by automated thinking machines.

I've seen quite a few academics wrangling with patents. What should come first? Proliferation of the sciences or (potential) profit? Your point about the golden goose is pretty interesting and others in this thread has pointed to the misaligned incentive structure in academia. I'm curious, do you think things like JOSS could help open up data and "the golden goose"? I'm not sure the funding bodies I'm interacting with would respect this kind of initiative, but maybe it's just a matter of time.

In principle I would agree. But, on a more philosophical level, couldn't you make that same argument about C? Or even assembly? Or even digital computers? Less facetious, it seems to me that the particular abstraction is less important. As long as it's unambiguous and widespread.

Regarding the biggest: nixpkgs sits at around 140k packages, way more than others.

Best: I still argue that Docker and Conda are more accessible, but my point was to go for reproducible, declarative science. Nix environments are exactly that. They're not perfect and are, as you point out, not standardized. But they cover much more ground that Docker. If you trust the upstream nix repo, you can get bit-level equality at every single build you (or anyone else) does. Docker relies on huge binary blobs that you can't inspect and that can be pretty much arbitrarily swapped around.

I'm totally fine if Guix takes over. Or the next big thing. As long as it's declarative and reproducible.

I'd love to hear you expand on how I cherry pick terms and definitions.

The Turing Way seems great and I'm all for education. One of the most interesting conversation topics in this thread is, to me, how to create the necessary incentive structures. Know how is only part of the way. We need to secure the credit assignment for "openness" both in academia and industry.

This seems to be strongly US-centric. In other (welfare) countries, publicly funded registries are anonymized and made available to research. For every single case. Of course, there are tons of data we don't see, but that shouldn't be an argument for not trying. The 99.99% unpublished cases is because our models/explanations/knowledge can't efficiently condense the medical mystery into a diagnosis code.

Everything can be prevented given sufficient knowledge. That's not the point. The point is how to prevent as much as possible.

Well said! And thank you for recognizing the effort.

The important part here is, as you say, to light candles and insist on rigor. Coincidentally, history tells us that that also gets us further. So by pure memetic selection, this strategy should win

I agree that a lot of the practices in academia are misaligned with the original goal. But can't you say that for other systems/institutions as well? Point being, what about keeping the scientific method as the north star - as a good heuristic to avoid BS arguments and awarding low-quality research. And, crucially, to stay sane. My post is pretty naive, but I stand by the ideal of pushing knowledge as reproducible models.

100%. I'm not saying the vision we're talking about is there yet, I just think it's an enticing thought. The existence of (predatory) publishers is a sad and miserable joke in its own right.

I think the idea does: the point is that closed-source software isn't useful for anyone else than the (copyright)owner. In scientific standards, that is. Similar to esoteric and cryptic theories that resist accessible explanations.

:-) I can comfort you with the fact that the text is not AI generated. I did use AI to proof read it and I liked some of its suggestions to improve the flow of the text. English isn't my first language, so this is a great help for me.

Erm. Why?

Thank you for engaging. This is a much more insightful take.

If I'm reading yiur argument right, you're saying that deployed models are downstream versions (reified) of aa theory. Theory, being the actual object of science.

I think this misrepresents science. Science is the ability to build testable knowledge. From that,how would you separate the test from the science? In fact, in an ideal world, why wouldn't you want your theory to be put in a format that's executable? I'm not saying that those things are always the same, my (provocative) title is based on a dream where we can imagine theory and model coexist because software is now a thing.