Mid-term election: science implications I

Most of the results of the mid-term election are now in and can be reviewed on-line. Jeff Mervis at SCIENCE has a nice summer of what the changes in the House mean, here. My own sense is that with Eddie Bernice Johnson (D-TX) as the likely chair for House Science, the tenor of that Committee’s relationship with the non-biomedical US Science R&D agencies is going to improve significantly. Specifically with regards to Climate Change, and more generally with regards to a less adversarial oversight role. I think that’s probably a good thing.

NASA and probably also NSF lost a key advocate in John Culberson (R-TX) as chair of CJS, the appropriations committee responsible for the two agencies. On the other hand, NASA will probably be able to finesse the timing of when they send a probe to Europa and NSF’s contacts with Chinese science may be a bit less fettered (although the one from the White House is still pretty hawkish).

Barbara Comstock’s loss in Virginia is complex. While she could be a thorn in the side of NSF (e.g. NEON), she was extremely supportive of the DC metro area federal workforce and this benefited science agencies who depend on expert staff to keep the wheels moving.

My sense is that NIH is still coming out of this smelling like a rose. A more conservative senate may put the brakes on some hot-button research topics, but in general, I am pretty  optimistic about the biomedical sector.

 

One proposal per year…

I’m hearing a lot about NSF BIO’s new policy of one proposal per year for each Principal Investigator. In general, I’m hearing complaints from more senior investigators and positive interest from younger ones. This is somewhat counter-intuitive for me since I’d expect junior PI’s to be quite anxious to get as many proposals as possible in within the time window of their tenure clock. But I suppose they also see this new policy as potentially reducing the competition from the old fogies (an aside, this is the same logic of those who rejoice when NSF or NIH have funding downturns because they see those as driving out the competition).

In any case, I’m agnostic about this. It is certainly good that NSF is discouraging the recycling of proposal failures. I find it annoying that I can only be PI on one proposal for the coming year–although it will incentivize me to make it as excellent as possible. I do think that the rather negative report on this new policy in SCIENCE was insufficiently nuanced and would be happy to discuss with the reporter.

The latest from NEON

NEON, the National Ecological Observatory Network, is a major research instrumentation asset that the NSF has built for scientists investigating how the environment and ecosystems interact at a continental scale. Here is the latestIMG_1104.jpg from Observatory Director and Chief Scientist, Sharon Collinge. It’s really good to see that this project is coming to a successful fruition.

There’s no photo credit on the image because it’s my photo. I took it at the NEON tower at Harvard Forest in central Massachusetts. Among many data products being produced, one of the most exciting are carbon flux measurements using the eddy-flux methodology. These are important because they provide a window into an ecosystem as it essentially breathes, just like we do. And that has enormous implications for climate change.

The location of this particular NEON tower (one of many across the United States) is particularly interesting because there is also a very long time series (25 years or so) of such measurements produced by the Ameriflux Network. If NEON can take advantage of such older measurements in a way that calibrates rigorously between the two systems, the power of continental scale (3-dimensions) will be enriched by a fourth dimension, time.

A bit about my new gig….

The summer break here at George Mason is coming to an end, classes begin in about two weeks and I thought it would good to write a bit about my new life as a plain old professor here at the Schar School. When I left NSF in January, I had negotiated my return to the University to reflect the public policy experience involved in running the Biological Sciences Directorate. Additionally, it had become clear to me that after 23 years in one administrative role after another, I wanted a change in the direction of more time to teach and do research. So when it was approved that my faculty line would be moved from the Krasnow Institute to the Schar School here in Arlington I was really jazzed. There was the additional benefit that the commuting distance would be halved.

I did start though with some trepidation. I had effectively been out of academia for more than three years—that in spite of NSF’s program for supporting rotator to stay involved with research at their home institution. That might work at the Program Director level at NSF, but it’s really not practical when you are responsible for an entire directorate. As a result, I was very rusty from the standpoint of both teaching and research—the two things I would be expected to do as a professor. Hence, it was a real confidence builder to get a grant in the first weeks that I was back and to actually jump back into teaching (rather than worrying about it).

 

I find that these past months have been some of the most satisfying of my life from a professional standpoint. The sheer pleasure of quiet time to think about science rather than have to instantly react to some crisis is something not to be underestimated. And I have found that my interests extend across a much wider landscape than before I left Mason for NSF. My current grant is on AI. The next one will probably be on metagenomics. Who knows what will come next!

Rules of Life: SBE Version

Many readers are aware of NSF’s 10 Big Ideas. One of them, Rules of Life: Predicting Phenotype originated in the Biological Sciences Directorate while I headed it up. We also used a similar set of words to frame all of the Directorate’s investments—from scale of an individual ion channel up to that of an ecosystem: Understanding the Rules of Life (URL). The intellectual idea here was that simple rule sets can, on the one hand, constrain nature and yet on the other produce vast complexity. An example of a very simple such rule is the Pauli Exclusion Principle from Chemistry. Pauli constrains atomic configurations by requiring electrons occupying the same orbital to have opposite spins. That simple rule produces the Period Chart of the Elements and by extension carbon chemistry (i.e. organic chemistry, the backbone of living things).

 

Biology itself has many such examples. Evolution itself consists of a rule involving history and contingency. Neuronal synapses (the connections between nerve cells) in the brain are constrained by the tree-like morphology of neurons: if branches of adjacent neurons aren’t close enough, then there is no possibility for the formation of a new synapse. The DNA dogma itself is a compact rule set that leads from base pairing through the genetic code to the construction of polypeptides that we call proteins.

 

The NSF has another Directorate for Behavioral, Social and Economic Sciences (SBE). It deals with all things human, particularly the emergent properties of human beings interacting with one another in constructs such as cities or, in a more abstract example, markets. Wars, mass migrations, stock market crashes and the World Cup are the types of emergent properties that are referred to here. They are concrete, consequential and produced as a result of many individual human agents behaving together in the biosphere. The current climate disruption on the Earth is thought by many of my colleagues to be anthropogenic in nature, an emergent of human development since the Industrial Revolution.

 

Not surprisingly, SBE was (and presumable is) enthusiastic about the Rules of Life Big Idea at NSF. After all human beings are living things, embedded and integral to the biosphere. If you are investing in social, behavioral and economic sciences, then by definition, you must be curious about the rules that govern these disciplines. And I think such an outlook can only strengthen the social sciences (writ large). Rules of Life as a framework can help create a theoretical scaffolding for the SBE fields in the same way that quantum mechanics does for physics and chemistry. Scientists seek to do more than collect and describe. Above all, they seek to predict and generalize.

 

A larger question though is, what are the rules that govern the production of human societal emergent properties? Is it possible that we could write them down in a compact fashion as we can for the game of Chess?

 

As I look out over the global political landscape these days, with the populist electoral success extending from the Philippines to Brexit Britain…and certainly including my own country…. I am curious whether there is a hidden rule set that relates these movements to a certain societal incivility that seems to be spreading as a social contagion. Another phenomenon that seems to be recently emergent is an increasing acceptance of lying on the part of political leaders. Instead of being viewed as shameful, such actions seem to viewed by many as reflecting strength and genuineness. Is there a human societal rule set that governs the acceptance of deception?

 

I had lunch yesterday with a colleague from our economics department yesterday and we both wondered whether the decline of organized religion had something to do with the recent political landscape, however humans have been in such dark places before in times when organized religion was very strong. In any case, a lunchtime conversation is not the way to elucidate a rule set for human societal behavior.

 

What would be the way to reveal such rule sets? One notion is to use agent-based modeling. In this approach, human beings are modeled in silico as software agents. The agents interact according to rule sets created by the experimentalist (a computational social scientist) in a massive manner, limited only by Moore’s Law. The emergent behaviors of the whole system are what is measured and the idea is to understand the relationship between the designed social rule set for the agents and the resultant emergent behavior of the model. The problem with this approach is that humans are very complex—much more complex that the modular pieces of software that comprise agents.

 

Another approach is to use college students as experimental subjects in behavioral economic experiments. This was the invention of another former colleague, also an economist, who won the Nobel Prize as a result of this idea. In such experiments, human subjects are paid real money as they interact with each other or computers under designed rule sets, similar to those used in agent-based modeling. The famous Prisoner’s Dilemma is an example of such a designed rule set. Here, the experimental results are quantifiable (how much money each student has at the end of each experiment) and the agents are real human beings (albeit a bit young). A neuroscientific bonus to this type of research is that the human subjects can be brain scanned as they interact revealing the neural substrates for their actions. The problem with this approach is that the number of experimental subjects is orders of magnitude less than the number of human agents interacting in real social phenomena such as stock markets. Hence, in general, such behavioral economic experiments are statistically under-powered relative to the social behavior they try to explain.

 

I think it’s time for by SBE friends to invent a new rule discovery approach. The timing is ripe: the relevance of such rule sets to our survival on the planet is clear. With the advent of ubiquitous AI, such rule sets will be of crucial importance to the engineering of ethical, legal and social frameworks for robots and the like as they interact with human beings. And it would be interesting to discover how human history relates to our social natures, not just in a qualified way, but one that is predictive and generalizable.

120 Days Out…

It’s been four months now since I’ve left NSF and returned to my university. During that time, I’ve gotten my first grant, taught two courses and given sundry talks around the state all towards the notion that, in life science, for Virginia, the whole is more than the sum of the parts. In our Commonwealth, even with a wealth of research university talent, too often we compete with each other for the crumbs rather than going after the big prizes that are out there.

 

What do I mean by the crumbs? Well, at the university level, these are the sponsored research opportunities that would be meaningful and significant at the individual PI level, but that are not a good return on investment (of time and energy) on the part of the institution as a whole, to say nothing of the state.

 

Contrast that to what I saw routinely during my time at NSF—where institutions within a state would coalesce around competitions for major center awards (and larger)—each institution supporting her sisters in a complementary style. This type of energy was visible, not only for the usual suspects like California or Massachusetts, but also for states that one might not expect.

 

I’ll be writing more about this subject matter in future blog entries….

Graduate Tuition Support at NSF

One thing that I didn’t know, before I came to NSF in 2014 was that support for graduate student research assistants as part of regular research grants includes tuition support that is not capped. According to this NSF FAQ:

Tuition remission is generally treated as part of an organization’s fringe benefit rate or as a direct cost. NSF’s policy is that colleges and universities should budget tuition remission consistent with its established indirect cost rate methodology and negotiated rate agreement. If tuition remission is budgeted as a direct cost, it should be listed in the “Other” category of the Budget under “Other Direct Costs.

Note that there is nothing about a cap in the above guidance.

In contrast, NIH does cap tuition support for graduate research assistants at around $16K. Here is the relevant NIH policy:

Undergraduate and Predoctoral Trainees and Fellows:  For institutional training grants (T32, T34, T35, T90, TL1, TL4) and individual fellowships (F30, F31), an amount per predoctoral trainee equal to 60% of the level requested by the applicant institution, up to $16,000 per year, will be provided.

This difference between the two science agencies is trivial for a lot of cases, were graduate students are paying in-state tuition at a public university. You can find some of the relevant data from the College Board here. However, in the case of some of the private research universities, this can be a very large amount of money. Here is the relevant tuition information for Princeton. And here in the same for Boston University. Even for public institutions, the out-of-state tuition can be very large in comparison to $16K (Rackham graduate school, University of Michigan).

Taken to its logical conclusion, NSF risks becoming a tuition-support agency instead of a science agency as tuition costs continue to rise across the country. This makes no sense. NSF should cap tuition support just like NIH does.