(by Adam Elkus)
As Zenpundit readers may know from my previous entry, I am a PhD student in Computational Social Science at George Mason University. Though I am learning the technical craft of computer modeling of social processes, I have had a longstanding interest in future war and technology. I grew up in California, and in an environment very much shaped by the technology industry. This was diluted by the fact that I grew up in Southern California and also have had a mostly liberal arts education heavy on arts, (military and strategic) history, and social science. My own struggle these days is — having spent so long developing the humanities side of myself — to think more like an engineer in developing computational and quantitative approaches to studying social science. You can see some of my notes on this process at my own personal research journal.
My term project for my CSS 600 class is a very, very crude and simple agent-based model of military mobilization. I’m also working on an equally crude model of strategic learning and a very crude simulation of strategic effectiveness in alliances. I don’t like them, and am on the fence about whether I’ll want to post them on Github after all — though I am definitely going to post the alliance effectiveness model (which relies on an interesting optimization algorithm) to a larger audience.
That is OK. Though I began practicing Python and NetLogo all the way back in early spring, learning to program from scratch takes time and effort. Through my classes, tutoring, and plenty of sleepless nights I bootstrapped my way to being able to make computational models in Python, NetLogo, and Java. And this winter I will be practicing Java and Lisp in preparation for spring classes. I could probably, with more time and less distractions (readers who know me in person will know that I unfortunately have had to devote a lot more attention this semester to resolving some logistical problems outside of academics) have done better than the models I’m making for my classes. So I chose easier and simpler for my first models. However, I have grander ambitions in the long term.
This post is the first of a series that I am constructing from notes I have scribbled throughout this semester, my first at GMU. I have, over the last year, relentlessly explored and narrowed down my idea of my research agenda. Aaron Frank, Jay Ulfelder, Mark Safranski, A.E. Stahl, David Masad, Russell Thomas, Lynn Rees, Dan tdaxp, Daniel Trombly, Joshua Foust, Trey Causey, Alex Hanna, Sina K., Anton Strezhnez, Nick Prime, Daniel Bilar, Sam Liles, W.K. Winecoff, H. Lucien Gauthier III, Dave Lyle, Daniel Solomon, Jon Jeckell, Alex Olesker, Brett Fujioka, Robert Caruso, the mysterious Dr. Kypt3ia, and many others too numerous to mention have served as sounding boards for a successive array of both promising ideas and also half and even quarter-baked “dry holes.”
I have a gigantic array of TextEdit files, Moleskine journals, and even theories scribbled in pseudocode in my Sublime Text 2 text editor. So as I turn them into coherent posts, I will space them out individually. This series concerns the concept of “computational strategy,” which I am shaping my own studies around. For example, I will be taking a survey artificial intelligence class next semester — one of two survey courses that computer science majors (which I am not — I will have the same relationship with CS that political science has with probability and statistics in that I’ll try to borrow as much as I can but also will never be as good as an actual CS student) must take to survey breadth and depth of AI. I will also be taking a course on cognitive programming for computer models.
It will consist of the following posts:
(I) My own journey as a PhD student up to this point
(II) Contrasting generative social science with theoretical computer science — and their deficits when applied to strategy
(III) From “killer robots” to “robot historian” and computation as a universal language
(IV) Towards a preliminary research agenda for computational approaches for studying strategic theory
We begin with (I):
Between A Dead Prussian And Kenneth Waltz
Since my friend Aaron Frank convinced me to switch from International Relations to my current PhD program in Computational Social Science, I have experienced something of an identity crisis. Though I have an BA in Diplomacy in World Affairs, and two semesters’ of graduate coursework in International Relations, my largest substantive base of expertise is in military-strategic theory and history and War Studies. I am both self-taught in this subject (endless library hours in my BA) and have a MA in Security Studies from Georgetown with a concentration in Military Operations. Unfortunately, this has ensured that for most of my time in higher education I have been caught between various disciplinary boxes. International Relations and Political Science has been a home for strategic thinkers like Richard Betts, Michael Horowitz, and Eliot Cohen. But on the whole, International Relations and Strategy have diverged since the high point of the 1960s nuclear theorists (Brodie, Schelling, and others).
As A.E. Stahl wrote, IR’s interest usually stops (with few exceptions) once the war begins. This is actually mirrored by the state of military history itself, which increasingly shies away from the study of strategy, battle, and tactics. Comparative politics, ironically, has picked up the slack. Quantitative comparative politics has some of the most valuable research on sub-state violence and civil war, but it is not connected to the larger strategic picture. The danger in studying one part of warfare in isolation from the whole of war and strategy is that it is easy to begin to think that your field has rules somehow distinct from the larger picture. Counterinsurgency, as Colin Gray wrote, has different particulars but on the whole does not have a separate logic from war as a whole.
To make matters worse, there is also a disciplinary disconnect in the study of strategy between a number of different camps. Game theorists — from the classical variety to more exotic subtypes like algorithmic game theory and evolutionary game theory — explore strategic interaction with mathematical models. Business strategists explore strategy and innovation from an organizational standpoint. And military strategists examine topics from a qualitative-historical mindset derived from Carl von Clausewitz’s philosophy of “critical analysis.” Though all of these perspectives have value, few attempt to bring them together (and of those who do, few are successful). This does not have to be the case. Mid 20th century strategists like Thomas Schelling, John Boyd, and J.C. Wylie combined a set of eclectic influences. Lawrence Freedman’s new book, as I’ve been told (haven’t got a chance to read) — also takes an holistic view of strategy that manages to also throw in the Marxist social movement strategic thinking of foundational radicals (Lenin and Gramsci to Hardt/Negri). And applications of complexity science to the study of strategy have been congruent with classical strategic theory.
Given the problems I have had finding places where I could study strategy freely, I could have aimed to do my PhD in War Studies, like my friend Nick Prime. However, the PhD program he is in is best suited to those with a very concrete and well-formed plan of study. I did not have one when I was applying for my PhD. And I also am both a product of the American political science tradition and the classical strategy school. I thought I could combine the two in my PhD at an International Relations department.
After I switched to Computational Social Science, I briefly abandoned the thought of doing something on strategy and decided I was going to look at risk and complexity. This coincided with my own sense of uncertainty over what I would do after graduation. I had always thought I was getting my PhD so I could teach at a military institution or work in military research. But with sequestration devastating many places I wanted to work, I began to radically hedge. I thought to myself, “maybe I would be happy selling widgets with computer models and writing about strategy on the side.” But as I went through intensively pushing myself through remedial mathematics, programming, and computer science I began to fear going down a million complexity-theoretic rabbit roles without a strong anchor that would guide me at least through my PhD program.
Mathematics, code, and programs are after all only just formal languages. One must first know what they seek to say before they start talking. And I also simply could not get past the basic fact that I had devoted 7 years of my life (BA up until now) to studying war and strategy. I could either use my existing base of expertise as a source of research questions and subject matter knowledge, or force myself to develop entirely new bases of social science expertise. To reduce my own sense of schizophrenia, drift, and confusion I began to think about how I could make my new studies fit my interests.
To be continued.