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.

Chinese Super-Science

Robert Samuelson has an op ed piece in today’s WAPO on how China has become a science superpower. The piece was timed with the release of NSF’s Science Indicators annual report (currently unavailable due to the government shutdown). I was last in China six years ago and it was clear even then that the Chinese were aiming, not just to become a peer of the US, but to exceed it in all areas of science and technology. Since that visit, we have seen the Chinese leap forward in Astronomy (the largest radio telescope), quantum computing (the world’s only satellite-based quantum encryption system), biomedical research (clinical studies that have statistical power far beyond those in the west) and even ecology (with their distributed environmental sensor network).

At the same time, US investments in science and technology have been quite stagnant. For Fiscal Year 2018, President Trump proposed an 11% cut to the NSF. He proposed an even larger cut of 22% for the NIH. These proposed cuts follow years of essentially flat funding during the Obama administration.  From a GDP perspective it’s even worse! Countries like South Korea, Germany and Japan made larger investments in science relative to their economy size.

If this trend continues, China will become the essential nation from a science perspective. And the geo-political consequences of that could be dire. Leading in science historically has led to non-incremental advances that create strategic surprise (e.g. nuclear weapons, the Internet, lasers). Imagine a US President being told that our spy satellites have been hacked leaving us blind to missile launches. Or that the location of our nuclear submarines was now available in real time to our global competitors?

What can be done? For one thing, it’s useful to remember that in the process of creating a budget, the President proposes, but Congress disposes. It is essential to reach out to members of Congress and let them know how important science is to the security of this country. But even more importantly, it’s time to open the channels of communication between those who are skeptical of the value of science investment and science advocates (including practitioners). In a recent conversation with one of this country’s most prominent science advocates, it became clear to me that science has taken on a political label that is not helpful. Science should not be political. Otherwise, it will become just another special interest in the eyes of its stakeholders. And the future of science is too important for that fate.

Tracking investments in graduate education

During my time leading the Biological Sciences Directorate at NSF, I learned that the agency spends around a billion dollars a year on graduate education—the training that is required after the undergraduate degree to turn an aspiring scientist into a true discoverer. Of that money, roughly 15% is spent on NSF’s flagship graduate research fellowships—a fantastic program that’s been around since the 1950’s and has played a central role in the early careers of many of the US science superstars. These are folks who have gone on to win Nobel prizes and the like. Winning these fellowships involves an intense competition of ideas and is peer-reviewed by the science community. I’m pleased that NSF tracks the career trajectory of these trainees pretty carefully. There is hard evidence that the graduate research fellowship program works.

Another 5% of the total is spent on trainee grants—the current version of these are the National Research Traineeships. These are training awards that go to universities which then award the support to graduate students that they select. I was trained under such a program (although it was NIH funded) when I was at the University of Michigan training in neuroscience. These are excellent funding programs and once again those folks who are supported in this way are tracked pretty carefully (I still get contacted regularly by the NIH asking what I’m up to).

But the vast majority, 75%, of what the NSF invests in graduate education is untracked. These embedded in the dollars that go to research grants of scientists at US universities who then hire graduate student research assistants to actually do the work. We don’t know what happens to these trainees. There simply is no easy way at getting at the data.

In strikes me as unwise to make such a large investment without getting feedback on how things are going. In particular, I am concerned that those graduate students are being inadequately mentored in some pretty substantive ways. For example, I fear that they are too often treated as an extra pair of hands rather than a future professional colleague. Time spent teaching these students about career options or how to effectively teach undergraduate students is time away from the laboratory bench.

There are ways of tracking such students. One such mechanism is the orcid id system. There are others. If all students supported by the NSF were required to be registered in such a system, then it would be possible to track their career easily (as long as they stayed in science). But success on that front requires one other thing: that journal publishers and data repository sites require that a person’s id be attached as meta-data to every single piece of scientific data from the results of a single bench top experiment or a field observation all the way to a finished journal article.

This is not impossible. I think it is important to move this direction because it will allow for evidence based decisions about how to optimize NSF’s graduate student support in the future.