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.

What is Strategic Surprise and how can AI help us?

We have been considering the notion of strategic surprisein the context of a report that we are preparing for the USAF. The AI part of this so far had been that US might suddenly face the reality of something like general AIbeing used against us at some point. But it doesn’t have to be like that. It could be that AI could help us delineate strategic surprises in other non-AI domains that human analysts haven’t anticipated (e.g. commercial airliners being used as very smart cruise missiles during 9/11). This intrigues me. Imagine if an AI had guided the original investigators who discoveredan adaptive bacterial immune system (CRISPR) that it could be used as a gene editor way back in 1987. So could AI act as a discovery accelerator?

March for Science redux…

So we had the anniversary yesterday. And once again, like a year ago, scientists made signs and got out there for some collective action. I applaud this. During my time at NSF, I had to be more circumspect, but to my mind the first step to regaining the trust that science deserves is to humanize the discoverers themselves so that they are seen by the media and public as real people as opposed to TV caricatures.

Will this work in the short-term? I don’t think so. If you take a look at the protest signs in the hyperlink, the sense of humor is a bit too ironic to have mass appeal, but I do think the faces of young US scientists, obviously passionate about their life’s work–that will get noticed and it may begin a process which is long overdue: where scientists are valued as members of the workforce in the same way that physicians or pilots are–to say nothing of police officers or military members.

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….

Fusion: just around the corner? Redux

This interesting news about a collaboration between MIT and Commonwealth Fusion Inc (at Nature and potentially behind a firewall for some readers). In any case, the key idea here is that a new generation of superconductors will be used to create a magnetic confinement for the hot fusion plasma.

Many years ago, before I went to graduate school, during an internship on the Hill, I got very interested in fusion. But that was several energy crises ago. These days I would be interested in fusion power as a potential play for mitigating climate change….or possibly Bitcoin mining (not).