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.

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.