Category: Uncategorized

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.

Spiral Development

When I was at NSF, we had a big problem child of a project, NEON, the National Ecological Observatory Network. Comprised of cyber-infrastructure, robotic sensors, human field sampling and airborne platforms extending from the Arctic Ocean to Puerto Rico, the nearly half-billion dollar project had chronic issues with costs and schedules. To fix those problems, the NSF brought in USAF Lt. General James Abrahamson  because he had been the fixer-in-chief on projects as diverse as the F-16 and the Space Shuttle.

One of the things that the General taught us to do, as far as fixing NEON, was to use spiral development: build a little, test a little, build a little more, test a little more. We learned that one of the root problems with the NEON design was that it had been “frozen in place” back in the first years of the new century and hence was technologically obsoleted before we finished construction. Spiral development was one of the key approaches we used to fix NEON.

Here’s a new article in Space News on how that approach is being currently deployed in the USAF. It strikes me that this approach should be used in many science R&D areas where the time-line is lengthy and the consequences for failure are large.

Mountain Top Plant Species Richness: An Effect of Climate Change?

As my colleagues know, I read the paper version of Nature every week while reading Science on-line. I find that with the hard copy of the journal on my desk, I read (or at least skim) every article rather than skipping around to what’s in my discipline. So, from Nature, last week, this article popped up. It’s a European finding with what looks like several scores of authors—they looked at plant species diversity data from mountain tops across Europe from a time series of 145 years. The results were striking—an acceleration in “richness” (diversity) with 5X species enrichment during the last decade as compared with the decade 50 years ago.

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….

Teaching again…

I am now three weeks into the semester and surprisingly, it’s been fairly easy.  The routine of teaching, grading, seminar preparation and the like are relaxing, even enjoyable. My students are graduate level in the School of Public Policy at George Mason. Because we are in D.C., some of my students are as senior as I am. And, I am learning from all of them.

At the same time, I have started a book project and am busy shopping out an Op Ed about the  President’s science budget–which hasn’t been released yet. Although… there was a leak that made it to the Washington Post in the last day or so.

For fun, over Spring Break, I’ll be headed to Paris with my wife. We plan to take advantage of all the excellent advice that we have received from friends and even ex-colleagues at NSF. So enjoying life…

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.

The communications problem…

As in the communications problems of scientists as they try to explain the intellectual merit of their work to non-scientists in plain language. Here’s a terrific essay by Samuel Matlack on that problem within the context of physics. This is not some feel-good exercise. Unless and until scientists develop this knack, they will continue to be viewed with skepticism by the folks who hold the purse strings.

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.

Origin of Life and NSF

There are many interesting open scientific questions, but one of the most intriguing is how did life originate in the universe. This is the central question of astrobiology and this week three astronomers authored a commentary in Nature that argues, among other things, for NSF to “replace elements…of the Astronomy, Geophysics and Ecosystem Studies program…by one exoplanetary systems science program.” The lead author, Caleb Scharf is the director of astrobiology at Columbia University.

Origin of life biology goes far beyond the study of exoplanets, as the authors acknowledge. Fundamental questions in redox chemistry far from chemical equilibrium lead to the origins of metabolism, massively conserved across earth’s living forms. At the same time, the information flow from DNA to RNA and thence to proteins drives questions about the origin of the ribosome translation machinery. In 2015, while I was still heading up NSF BIO, we collaborated with NASA to fund an IDEAS lab to explore across these two threads (metabolism and RNA). These are two communities of thought that don’t often communicate.

But Scharf and his co-authors raise important points. There needs to be better cross-talk between astronomy, geosciences and life sciences if we are to make progress. I am not averse to supporting a reorganization of NSF programs in support of such trans disciplinary cross-pollination, but will just point out that it’s often easier to ignore bureaucratic boundaries than to redraw them.