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7/The authors interpret the paucity of prizes for discoveries since 1990 as evidence of declining #'s of important discoveries. But it could indicate the committee scrambling to award prizes to a huge # of important discoveries before the discoverers die (and become ineligible)!
8/The second methodological problem is that a scientific discovery's actual, real importance is not fixed in time. New discoveries build on old ones. Each new discovery makes the old discoveries it's based on grow in actual importance.
9/Consider all the recent discoveries that MIGHT lead to great things, or MIGHT be relatively dead-endish. Will CRISPR allow ubiquitous safe genetic engineering? Or will it be a mostly useless tool due to massive side effects? We don't know yet.
10/EVEN IF scientists are capable of accurately assessing a discovery's importance, graphs of importance vs. time are naturally biased against recent discoveries. It'll be decades before we know what discoveries from the 2010s really changed the world.
11/So I believe the Atlantic article's methodology is deeply flawed. But that said, I believe their conclusion is probably true - at least, in specific fields. Because we have other evidence.https://www.bloomberg.com/opinion/articles/2018-05-02/maybe-this-is-as-good-as-innovation-gets …
12/A recent paper by Bloom, Jones, Van Reenen, and Webb looks at specific scientific fields, and tries to measure research productivity - i.e., discoveries per researcher - for each one. The find declining productivity in each field. https://web.stanford.edu/~chadj/IdeaPF.pdf …
13/The authors are careful. They use a variety of measures of discoveries. Real measures like Moore's Law, crop yields, drug discoveries, etc. Measures of company performance in various fields, patents, etc. All measures show slowing productivity-per-researcher in all fields.
14/Now, does falling research productivity mean science as a whole is slowing down? Or that our investment in science has become uneconomical? NO. It does not! Read this post: https://www.bradford-delong.com/2018/08/nicholas-bloom-stanford-university-and-nber-john-van-reenen-mit-and-nber-charles-i-jones-stanford-university-and-nber-mich.html …
15/So why DO we care about slowing within-field productivity in scientific fields? Because it's a sign that we need to actively search for new fields to open up.https://www.bloomberg.com/opinion/articles/2018-11-05/scientists-do-too-much-research-on-the-old-instead-of-the-new …
16/Science doesn't progress by simply doing more of the stuff that worked in the past. It progresses by branching out in new directions. AI. Neurotech and biomechanical engineering. Genetic engineering. etc. Fields that were science fiction 50 years ago.
17/And that means that our granting agencies, the NIH and the NSF, need: 1) More funding rather than less 2) More money allocated to lesser-known institutions, and more for up-and-coming researchers rather than established ones.
18/Whether we're seeing "the end of science" is a question that will never be answered. But each specific line of inquiry eventually sees diminishing returns, so we need to always be opening up new lines. (end)
And to this point, both funding and self-selection of research has leaned away from "pure theory" in favor of "applied solutions," creating a deficit of that theory to build on going forward. This is a really serious problem....
That's interesting. I always thought that theory didn't need a lot of funding...all you really need are some smart people sitting around thinking about stuff and maybe playing with some computer models.
*cough* particle physics *cough*
Oh, I see, you mean TESTING basic theories... ;-) I hate to say this, but the real problem with particle physics is that new stuff is so high-energy that there's not much we can use it for.
Nonono. You need high-energy to take it apart and understand it. The actual applications quite likely won't need to do that, just understand better how it all works inside!
Hmm. What technologies have we gotten from QED and QCD so far? The PET scan is the only one I can think of.
Most semi-conductor advances these days require them! This is both in scaling down to (now) 5nm, but also all the quantum-bit technology.
Really? What do you need them for that you can't just do with QM?
I see this as the biggest issue. It’s easy to look back on past discoveries as significant when we see their impact with 20/20 vision and concrete evidence.
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