Comment by cbkeller | Hacker News Reader

Comment by cbkeller | Hacker News Reader

Geologist here. This is super-important for hypothesis science.

At the same time, I think one of the biggest inaccuracies in the public perception of science (and one of my pet peeves) is the idea that all science is hypothesis science. It turns out there's also still plenty of discovery science to be done -- and while it's less common than it was 100 or 200 years ago, it's quite important!

In geology, this often quite literally takes the form of a blank space on the map -- there are plenty of unmapped quadrangles on the geologic map of the world at 1:24,000 and finer scales, and the USGS will pay you just to learn how to fill them in [1].

This is one of the few subjects on which the misinformation is so pervasive that even the wikipedia article [2] is substantially inaccurate (I blame the overly-simplified formulation of the scientific method that most of us are first exposed to in elementary school). The one part that you can correctly infer from the wp article though, is that discovery science is having a bit of a resurgence recently thanks to the proliferation and reuse of large datasets.

[1] https://ncgmp.usgs.gov/about/edmap.html [2] https://en.wikipedia.org/wiki/Discovery_science

One of the simplest distinguishing characteristics is that there's no such thing as a negative result in discovery science: if you're mapping a blank area, whatever you find will be something we didn't know before.

One of the most painful parts of Economics is how many mathematicians there are in the field. They treat discovery science as hypothesis science, and try to force reality fit their model - instead of gathering data about the real world first. Thus, we get models of Marginal Demand that don't include "not having enough money to buy something".

Edit: if this comment is unhelpful, please suggest an update.

I find it helpful, or at least informative, but since it confirms the model I already had of the field my opinion may not be trustworthy.

> Thus, we get models of Marginal Demand that don't include "not having enough money to buy something".

Which models?

Most of them, especially those developed before the 2000s.

Can you be more specific? “Not having enough money to buy something” sounds like a pretty important factor to miss.

I think neuroscience is much like exploration. You can make hypotheses, but we barely know what is going on, so the hypotheses aren't really well grounded. Find hats there, publish the result, build the map, so to speak, so that future scientists can make more informed hypotheses.

Yeah. In areas where we know so little that it's hard to make informed hypotheses, there's probably a substantial component of discovery science.

It's a bit of a yin-yang situation IMO: discovery science often leads to testable hypotheses for future work, and things we learn from hypothesis-testing can open up new avenues for discovery

Genetics too. RNA sequencing data is almost exclusively considered exploratory and is expected to be replicated at least technically (by using the same biological samples for qPCR to measure a small number of genes) but also biologically with novel samples and experiments.

Oh man, in neuro, discovery-centric science is really controversial. Not the findings themselves, but the funding. If you propose to just stain/viral-inject some tissue, electro-physiologically map some connections, etc, you'll just get back comments that say 'fishing expedition'. Maybe you can do this with NIH when you propose to contrast it with diseased tissues, but even then, it's unlikely (Well, less likely than the 'normal' low rate of proposals being funded).

I'm appalled. That fundamental science is called 'a fishing expedition' in a derogatory way, says whole volumes about how academia is failing.

Don’t be appalled. This is not a failure. There are limited funds. Hypothesis driven work is thought to be more probably productive.

If you were handing out the funding, assuming equally good teams, do you choose the work that targets Parkinson’s mechanisms or the functional map of a less-explored bit of CNS?

We are doing basic exploration as well. We are just prioritizing targeted work in a resource constrained environment.

Maaaaybe. Truth be told, it's all about networking. Grants go to the friends of the reviewers more so than to those deemed most deserving [0]. Granted, it's really hard to read a bunch of proposals and determine whose is 'most' deserving. So reviewers act like the humans they are and give the grants to their friends.

[0]Total anecdata, I have no source for this.

I don't review grants for my friends. This is a strong community norm. It's unethical to do so. This causes a shortage of qualified reviewers, but that's less of a moral hazard than conflicts of interest.

If you admit you do not know this, then why say it?

I've had opportunities to review papers from people I have co-authored with before and declined the opportunity. I've reviewed a paper, then got an invitation from another journal to review the same paper. I declined, but sent the editor my former review. I thing there are strong social mores in the scientific community.

In a field where we know nearly nothing about, putting any one of those above the other looks like a problem to me. A field should go from purely exploratory, to both, to mostly hypothesis driven as it matures, but no sooner (with fractal subdivisions where subfields go through all those stages too).

Thus either neurology is way more mature than I'm aware of (a real possibility, I'm far from knowledgeable on it) or we do have a problem.

Nit: neurology != neuroscience.

We do know a LOT about neuroscience, but we've no idea how much we've yet to discover. Likely, it's a fractal, so we're not anywhere close and never will be. Like in Physics, we know a LOT about electrons and how to build a bridge, but we know nothing about 'dark energy'. It's also true in neuroscience that we know a LOT about action potentials but nearly nothing about how astrocytes interact with synapses. It's not a smooth march, but a VERY spasmatic stumble.

I don't think fields should progress in any manner whatsoever. They progress as they do and at their own paces. Otherwise it wouldn't be research, it'd be following plans. It's not something that can be proscribed, but more described.

I think the problem is, solving large complex problems (like Parkinsons) requires a very solid foundation of basic research. If you don't pay for the basic research, you don't get the foundation - and everybody is essentially leaping around in the dark.

I don't think the issue is about resources per se. I think it's more about Universities prioritizing high impact research, as in, the kind of research that appeals to non-scientists, in industry, media, or academia in general.

Something like 1 in 10 proposals submitted by universities are funded. Priorities are set by funding agencies, not universities.

Not disagreeing with your sentiment, but not all academia is like this. The comment applies very specifically to NIH & the life sciences (which admittedly is a really big chunk of science, both by dollar amounts and labor force).

Oh man, that's like, 0.1% of the issues in academia.

I suppose this kind of discovery science would be considered "preliminary results," meaning funding agencies like to see it done but won't fund it? Sigh.

Discovery science is definitely harder to fund than hypothesis science.

To take geologic mapping as the example again, the economic ROI has fallen over the past few centuries, when there were bigger unknown areas and you were more likely to literally or figuratively strike gold with only a coarse survey. So while there's still funding for pure mapping, there's not as much as there used to be.

I would argue that the scientific benefits of pure discovery are often still quite high, but it's typically easier to convince reviewers to fund a project when you can point to specific well-defined hypotheses that you'll be testing rather than just "because we don't know what's out there".

Most neuroscience work have hypotheses, but I am arguing that it is more similar to discovery science even so.

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