Chad Orzel has some thoughtful comments on my earlier questions about research funding. Here’s a few excerpts and some further thoughts:
… a good deal of the image problems that science in general has at the moment can be traced to a failure to grapple more directly with issues of funding and the justification of funding… In the latter half of the 20th century, we probably worked out the quantum details of 1000 times as many physical systems as in the first half, but that sort of thing feels a little like stamp collecting– adding one new element to a mixture and then re-measuring the band structure of the resulting solid doesn’t really seem to be on the same level as, say, the Schrödinger equation, but I’m at a loss for how to quantify the difference… The more important question, though, is should we really expect or demand that learning be proportional to funding?
This really gets to the nub of it. In research, as in so many other things, funding may hit a point of diminishing returns beyond which what we learn becomes more and more marginal. However, it is by no means obvious where the threshold is beyond which society as a whole would be better off allocating its resources to other more worthy causes.
And what, exactly, do we as a society expect to get out of fundamental research?
For years, the argument has been based on technology– that fundamental research is necessary to understand how to build the technologies of the future, and put a flying car in every garage. This has worked well for a long time, and it’s still true in a lot of fields, but I think it’s starting to break down in the really big-ticket areas. You can make a decent case that, say, a major neutron diffraction facility will provide materials science information that will allow better understanding of high-temperature superconductors, and make life better for everyone. It’s a little harder to make that case for the Higgs boson, and you’re sort of left with the Tang and Velcro argument– that working on making the next generation of whopping huge accelerators will lead to spin-off technologies that benefit large numbers of people. It’s not clear to me that this is a winning argument– we’ve gotten some nice things out of CERN, the Web among them, but I don’t know that the return on investment really justifies the expense.
The spinoff argument also has the problem that it’s hard to argue that these things wouldn’t have happened anyway. No disrespect to Tim Berners-Lee’s wonderful work, but it’s hard to believe that if he hadn’t started the web, some MIT student in a dorm room wouldn’t have done so shortly thereafter.
Of course, it’s not like I have a sure-fire argument. Like most scientists, I think that research is inherently worth funding– it’s practically axiomatic. Science is, at a fundamental level, what sets us apart from other animals. We don’t just accept the world around us as inscrutable and unchangeable, we poke at it until we figure out how it works, and we use that knowledge to our advantage. No matter what poets and musicians say, it’s science that makes us human, and that’s worth a few bucks to keep going. And if it takes millions or billions of dollars, well, we’re a wealthy society, and we can afford it.
We really ought to have a better argument than that, though.
As for the appropriate level of funding, I’m not sure I have a concrete number in mind. If we’ve got half a trillion to piss away on misguided military adventures, though, I think we can throw a few billion to the sciences without demanding anything particular in return.
One could attempt to frame this in purely economic terms: what’s the optimal rate at which to invest in research in order to maximize utility, under reasonable assumptions? This framing misses some of the other social benefits that Chad alludes to – all other things being equal, I’d rather live in a world where we understand general relativity, just because – but has the benefit of being at less passably well posed. I don’t know a lot about their conclusions, but I believe this kind of question has recently come under a lot of scrutiny from economists like Paul Romer, under the name endogeneous growth theory.
I think the particular social benefit you describe in your conclusion (knowing “just because”) is rapidly diminishing for fundamental science.
Let’s consider the LHC, which is expected to cost around $2 billion USD. If we assume that the cost is evenly distributed over the roughly one billion people living in developed countries, that’s $2 USD per person. If you explained to the average person what the LHC was for and what knowledge it might discover, do you think you could convince them it was worth $2 of their money? If the answer is no, we have a problem.
I know that, as a physicist, I derive a lot of “intrinsic value” from simply understanding something like quantum mechanics or general relativity. It’s not clear to me that the general public derives much–if any–value from knowing that there are a few people who understand GR. Fortunately with GR, interested laymen can at least understand what it’s about, which certainly boosts the value to the public. Once things get more specialized (string theory, I’m looking at you), that public benefit goes away.
Put another way, would it mean anything to you if you knew that some person somewhere knew the True Meaning of Life, but that you could never understand it? Would you pay money to help that person discover the TMoL, knowing that you could never know it yourself?
As for the technological benefits, it’s also not clear to me that that is a politically viable justification for funding basic science. A good deal of basic physics research that is being done will take decades to generate practical applications (if it does at all). Our society isn’t good at making investments on that timescale; as John Maynard Keynes said, “In the long run, we’re all dead.” Perhaps the focus on biomedical research is a good strategy–if people live longer, they’re more likely to care about the distant future. Funding research so that you can have flying cars five decades from now becomes a lot more compelling if you expect to still be alive to fly one fifty years hence.
“Would you pay money to help that person discover the TMoL, knowing that you could never know it yourself?”
I don’t think people look at it like that. So far as I can see, many people in the public at large are proud of the Einsteins and Cricks of the world, even while they don’t necessarily fully understand what those people have done.
To put it a different way, an awful lot of people bought Roger Penrose’s recent book, and I don’t think it’s because they wanted to read up on holomorphic functions; I think it’s because they enjoy and appreciate contact with great minds, even if they don’t fully understand what those minds do.
On the LHC, I have some of the same concerns as you. However, I don’t think it’s fair to generalize from this to all of fundamental science as you have done. There is a lot of excellent fundamental science being done that is many orders of magnitude cheaper than the LHC.
People have some idea of what Einstein did– E=mc^2 is one of the most famous equations in the world, after all. Penrose is still somewhat understandable, too. That said, book sales should be taken with a grain of salt: Deepak Chopra’s “The Book of Secrets” was a NYT Bestseller.
The problem is the trend; as it stands, we’re heading towards things being less comprehensible to the public, even as we ask for more and more funding. I picked on the LHC because, as someone pointed out at DAMOP (to subsequently be quoted on one of the other blogs discussing this topic), BEC experiments and many other areas of physics are asymptotically approaching particle physics in complexity, cost, and inaccessibility. One can also see parallels between topological quantum computing and string theory. If high energy physics is the model for the future of science, we’re in trouble.
Either we need to get much, much better at communicating, or the public is eventually going to lose faith that what we’re doing means anything. The less accessible science becomes, the more it starts to look from the outside like religion.
Romer says in part that all per-capita economic growth ultimately comes from technological improvement, and that R&D from the private sector will always be far less than the optimal. This doesn’t say how much gov’t-supported science is too much, but we should remember that it doesn’t take much to have an enormous spillover.
For example, maybe some MIT kids would have made the web a year later, but even that difference of a year probably means an enormous amount to the global economy.
Travi: “The less accessible science becomes, the more it starts to look from the outside like religion.”
I agree, although I’d probably say “look meaningless” instead of making the comparison to religion, which is a bit loaded. This is a big problem. I don’t know what the solution is, although I suspect that serious efforts at outreach by the scientific community would be a good start.
Aram: do you know enough about Romer et al’s work to know whether this question (what’s the optimal investment in basic research) could even be given an answer within their framework?
Your point about the year delay is well taken, although I’m not sure it isn’t a red herring. There are many contingencies here: maybe the web would have taken off faster if it had been invented a year later in an MIT dorm room.
(Of course, I’m not seriously trying to make this argument. All I’m trying to say is that evaluating the economic impact of basic research is pretty darn complicated, and one can’t just ascribe all or even much of the value of something like the web to the investment in CERN.)
You must know the answer to that question already, Michael.
If not, answer me this: Given a billion dollars, what is the optimal partition in basic research between, say, string theory, quantum gravity, and ultracold physics? If you can’t answer that, why can’t you answer it, and what does that say about your ability to determine an optimal lump sum to invest?
If you can answer it, how did you arrive at your answer?
Michael: I agree that we need much better science outreach, starting with vastly improved science education. Unfortunately, this is a chicken-and-egg problem: voters don’t know enough about science to know why improved science education should be a priority.
On a different note, let’s think about where the point of optimal investment might lie. If return on investment in basic science is linear, then either we reach an optimum only when we’re spending so much on science that we’re significantly neglecting other things (which have non-linear returns), or the optimum is essentially zero. Since science is currently a relatively small portion of total government spending, this suggests that we’re either spending way too much or way too little on science (or perhaps we’re in a transition between the two regimes).
Could the return on science be substantially non-linear? I can really only think of two ways this could happen. The first is if progress in any given area is inherently speed-limited, no matter how many people are thrown at the problem. In other words, there’s a limit to how many people can productively work on the same problem before you end up with too much duplication of effort and overhead. The second way is if science progress is limited by the number of capable researchers.
Given the relatively low percentage of grant applications that get funded, I’d guess the latter isn’t currently the case–we have plenty of people who could be doing quality research who currently aren’t getting funded. As for the former possibility, I suspect it’s probably also not true, except in a few very crowded fields.
To (partially) answer John, if the return on science is high, then the I know the optimal sum to invest is very large, even though I may know nothing about how much of that sum to invest in ultracold physics versus quantum gravity. It’s not necessary to solve the funding allocation problem at the micro level to solve it at the macro level.
Travis: I certainly think that the return on science is incredibly non-linear. Empirically, we spent a huge amount more on science in the second half of the twentieth century, yet the return (comparatively) didn’t match the investment. Don’t get me wrong, I still think the return was worth it, it just wasn’t as exceptional as earlier.
John: I have lots of ideas about how to decide this. But none that I feel all that confident of. A quote from Robin Hanson seems apt:
With that said, I tend to agree with Travis that at the larger scale we can make some plausible guesses based on past performance. But at the individual scale, where science changes very rapidly, this becomes much less useful as a predictive mechanism. String theory may have no economic impact; it may spawn multi-trillion dollar industries. I don’t know which, and I suspect no-one else does either. How should I compare that to AMO?
Robin Hanson’s ideas about idea futures may have some relevance here.
Michael: Were you implying that, in the first part of the 20th century, physics picked all the low-hanging fruit, and we are now left “mining the low-grade ore” (sorry for mixing metaphors)? That’s not incompatible with what I mean by “linear”. To better define it, what I mean that is that the return we get depends only on the amount we spend, and not on the rate. In other words, $1 billion spent over 1 year gets us the same return as $1 billion over 5 years. It’s still possible that the second billion we spend will get a lower rate of return than the first billion.
Taking the mining metaphor further, if we throw more money into science now, we’ll run out of high- and medium-grade “scientific ore” sooner, and have to start “mining” the low-grade stuff with its poor returns. This still fits within what I meant by “linear”, though. In such a scenario, we should mine as fast as we can, until we run out of “ore” that’s economical to extract. At that point, science is over, at least from an economically-rational viewpoint.
The question is–have we already hit that point? Have we passed “peak science”? At least in physics, we seem to be out of the high-grade ore (barring a strike of a “new vein” of good stuff).
Travis: I certainly don’t think the return is linear. The best funded fields in science are often very crowded, and this tends to diminish people, who will (often) narrow their ambition.
In regard to your question about “peak science” and physics: Physics has historically gone through many long dry spells, only to return to rapid major progress. There are lots of really big unsolved problems in physics, and some of those may turn out to have solutions as revolutionary as quantum mechanics or relativity; it’s also possible that for some of them a major advance may come in the very near future.
Michael,
I meant to answer my own not-quite-rhetorical question sooner, but it’s been a hectic week. In any event, I respect your answer for its honesty, but as most “I don’t know” answers, it’s not the most helpful one.
Here’s the key insight I was driving at with my questions: You can’t allocate between three broad research areas precisely because they’re research. If you could allocate properly, you would have to have some good foreknowledge of the results of the research, in which case it wouldn’t be research, would it? The same principle applies to trying to allocate an amount of funds in general to research in general.
Any time you allocate funds like that, you’re effectively placing a wager, which makes the reference to Robin Hansen’s “Idea Futures” an interesting one… although the pragmatist here must point out that we already have an idea futures market in effect by way of research univiersities, large technical corporations, and research arms of the various governments.
It’s worth pointing out another insight, related to the above– not all research is created equal, in that regard. Qualitatively, and not at all comprehensively, there’s a continuum ranging from basic, paradigm-shifting fundamental scientific research (say, reconciling GR and QM) at one end, down through more “technical” but still science-related research that expands and maps out semi-known territory (say, bioinformatics research), into more engineering-research fields (say, the first few decades after the development of the transistor, or some of the things my company does in defense applications), and finally down into more easily product-oriented engineering (say, cell phones and road construction techniques.)
That’s probably not an entirely single-axis continuum, but let that go for a moment. The thing is, the farther toward the product end of that scale you are, the more certain you are of both your results and of the cost of your goals. (And, I suppose, of the achievability of your goals in the first place.) The near term consequences of a road construction technique that slashes costs by half and slashes maintenance time by half are pretty easy to predict, in brad strokes– companies make more money, state governments either save more or provide more services, etc. You can generate a sophisticated model, plug in some assumptions (Which is more important– more roads, or smaller budget? Where will new roads go?) and get a range of sophisticated and plausible results.
The closer you move toward the basic science regime, the more inherent uncertainty you crank into the equation, and the less smooth the results are. The results, in fact, tend to be all or nothing, with the “all” being completely unpredictable even aside from that. I hate to be the naysayer, but I cannot imagine even the shape of a better solution than the market. Perhaps there are tweaks or moderate experiments to be made, though:
Perhaps an experimental fund of, say, $10,000,000 per year to be allocated more directly by an idea futures market subject to some constraints (e.g., only people with a post graduate degree in science, engineering, or related technical fields are allowed to wager.)
I take that back, actually.
Two ideas futures market funds, with the same amount of money in each, administered by the same rules, with one difference: One open only to holders of the proper credentials, the other open to anyone.