Jay and Return On Investment from Research Funding

Jay takes on the research funding debate:

I noticed a curious phenomenon at the NSF. They define R&D performance in a way that is completely baffling to me as an industrial scientist. Take a look at the first paragraph here. Performance is defined as money spent.

The truth is that funding bodies have no sensible way to determine how effective their funding is. Without any sensible metric, we are left with stupid things like counting papers or students. You could find worse metrics, but the tax payer is likely to be unimpressed. You don’t fund companies by the number of products they put on the market or the number of employees they have.

So yes, we should work harder to make a case for the usefulness of our research if we need lots of money. But this case cannot be made using a single metric like a patent. A patent in software engineering often makes little sense whereas it might make a lot of sense in mechanical engineering. Let the researchers give you their individual metrics. Let them tell their story as to why their work is important.

Things go sour when Jay looks at percentages…

Now Ernie thinks that basic research spending is too low. On the other side of the coin, I’m not sure how much public research spending is too much. The NSF calims that it only funds about 1/3 of all grant applications. At some point you reach the point of diminishing returns, where all the really good ideas are well funded, and you begin to fund a lot more junk.

There I disagree strongly. The long tail in science is important: you cannot guess where the important discoveries might come from and they might not be all from the same guy.

Low acceptance rates only make sense when the research overhead and scalability is high. For example, if you need half a million dollar in gear to even start the research, then you better give the funding to a couple of good places and let other researchers migrate there. But you must also take into account scalability: how many projects and subprojects can a professor run efficiently? Giving all the money to one person means that every dollar gets a very tiny fraction of this professor attention and at some point, the money will be wasted for sure.

For theoretical research, you have low scalability (you can’t do 15 projects at once) and low overhead (no need for expensive gear), so you should aim to fund everyone a little bit. For other fields, the opposite is true.

So, is 1/3 low or high? For theory work, it is too low, for some high overhead research, it might be just the right number.

4 thoughts on “Jay and Return On Investment from Research Funding”

  1. “you cannot guess where the important discoveries come from and they might not all come from the same guy.”

    I don’t think we’re in disagreement, here. This was the point I was making in using the Fourier Transform as an example. The long tail is going to give some good results. But it’s also going to give a lot of crap. As I said in the previous post to this one, 90% of what is outside the box, is outside because it’s junk. And I do not see our scientific leadership taking on the difficult task of prioritizing projects. A low priority doesn’t mean zero funding, however, it just means that we don’t want to funnel a lot of resources (and hence create a lot of Ph.D.s) in those areas. That was the point of the questions in the last paragraph that I want the AAAS to address: post-docs are at the prime of their sceintific powers, and they are basically indentured servants to established profs. Should we switch some funding priorities to younger profs? The AAAS does not want to ask those questions – it is controlled by the long-tenured.

    I think that we are coming at this problem from different angles because the mental models we have about funding are shaped by our fields. My advisor had over $2 million per year in grant monies. That’s probably quite exorbitant in CS and Math circles. Also a lot of the total US R&D budget is taken up in the nearly $30 billion NIH budget, a lot of which is chewed up by the cost of human testing. I’m not convinved that private industry should not take on some of that, but reasonable people can disagree. My advisor was on a couple of funding committees at various agencies, and I saw some of the stuff that crossed his desk. There is a lot of dreck out there.

  2. Right.

    I don’t think you can switch the funding away from post-docs into tenure track positions because those don’t depend on research funding alone.

    The best strategy would be to reallocate the funding for students altogether so that you train fewer Ph.D.s, but possibly more M.Sc.s

    What I advocate is very simply: train fewer researchers, but fund them better.

  3. Ah, yes. I think that we need to hire technicians at Universities to run experiments. Quit using Ph.D. students as hired hands. This turns the graduate experience into a pnzi scheme ,but the profs need warm bodies in the lab in order to keep those grant dollars flowing.

    Make research assistant jobs permanent positions at living wages. Then figure out the rational number of Ph.D.s is. I still think that younger profs get the shaft, though.

  4. Younger profs have a hard time, and they’ll keep on having a hard time as long as we keep training too many Ph.D.s

    It is simply a market issue. If universities had a really hard time recruiting, say they got 5 candidates for each job instead of 150… then they would release the pressure on the young professors. The students would be better off too since instead of having professors who are freaked out by their next grant proposal, they might actually get rewarded for their teaching skills [in a less competitive market].

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