My Conversion to Chaos
- Meg Spohn, Ph.D., Denver, CO
Something wasn’t right. If the model was going to work, the numbers had to be simpler. If they were simpler, though, they were too artificial or something — the model wouldn’t predict jack-all. For days, I kept setting the work down, doing something else, coming back to it...from what I could tell, the model was actually working the way it was supposed to. It just, well, sucked. It sucked out loud, it sucked in nine keys, Marvin K. Mooney, it sucked, sucked, sucked.
The trouble was that the Cold War was over, and the field of international relations was suddenly without any grand theory (and still is). The bipolar order had melted away, a fifty-year historical fluke, and the last generation of scholars to have studied and understood anything else could not be reached for comment. As a young grad student, the Cold War models I had inherited were crude linear statistical ones, and they worked okay a little more often than they didn’t. With only two variables to deal with, that was probably good enough. But the world had become messy again, and those models could barely handle the two poles they had—they were not set up to handle a mess, otherwise known as reality. Plus, the field of international relations attracts people with superior analytical skills, but who fear math. I thought it showed in the models, but being old enough to have had my teachers tell me that girls were good at English while boys were good at math, I had giant cavernous holes in my own mathematical education and I didn’t really know what was wrong. I just knew why. And that the models sucked. That much was obvious.
Old-school game theory (like the Prisoner’s Dilemma) had brought me to dispute resolution systems design: trying to find new patterns and methods of resolving conflicts. I had been doing largely qualitative research, but some patterns are only visible in the numbers, and they stay hidden within the purely qualitative, like those dot tests they use to figure out if you’re colorblind. Being numberblind was keeping the insights—and the solutions—away.
I remembered hearing about chaos theory, and someone recommending James Gleick’s Chaos to me, so in an effort to fill in some of those educational holes and understand what was exactly wrong with the models (besides glaring suckitude), I read it. It changed my life.
Algebra hadn’t made sense to me because I didn’t understand what the applications were. Why the hell did I need to know what the slope of a line was? What, for the luvva Mike, was that ever going to do for me? Fix my car? Make me a sandwich? Get me laid? My teachers wouldn’t tell me what it was good for, or they couldn’t. Many years later, I learned that you could use it to determine things like how steep to build bridges, but by then it was too late. I was already a heretic.
Life is messy, Chaos taught me. It is not bipolar, and it never was. Natural and human systems don’t behave in a tidy, linear way—but they are also far from random. We can look at complex patterns that appear random (if only until we know what we’re looking at), and glean insights from them. We don’t have to look at little scraps of the system, we can look at the whole thing and process the patterns, large and small. Chaos was (and is) a new science, but it’s heading toward things like math that could be used for building clouds, and I thought, the holy grail of analysis: real-world predictive models.
The question was whether I really bought it. It made a lot of sense to my head, but at one point, so had parachute pants. I asked the professor who was holding my hand (in a dignified, helpful, scholarly way, not in a priest-and-Cub Scout way) in my forays into dispute resolution systems design. He said I was wasting my time and I shouldn’t bother with it. The few others I cautiously consulted didn’t think chaos had any value at all, and discouraged me from continuing to look into it. But the theories they did value didn’t make sense anymore. They were theoretical dead ends in the “new” world order, leftover pieces from a lost board game. Cold War Monopoly had only had that one set of dice, too. I had to test whether chaos worked—at least better than parachute pants.
I found one concept I thought I would be able to test without a huge computer, which I didn’t have access to, then, and that I could probably even test by hand: the Cantor Dust. It was used to understand the pattern of noise in data transmissions. They scale: that is, they are self-similar — they form similar patterns and shapes of noise and silence whether you look at them from the perspective of the whole transmission, or a piece of it, or a piece of the piece. It reminded me of what soldiers say about conflict: months of boredom followed by minutes of battle and violence and raw terror, followed by more boredom. Essentially, I wondered if battles scaled like Cantor Dusts. It seemed like a good test.
I went deep into the bowels of Widener Library, many floors underground, where you could go to do research and not see another living soul, and began to look up the details of conflicts. I decided to measure intensity by battle deaths, because they were the easiest number to get. I checked out a number of different (U.S.) conflicts over time, plotted them with graph paper and a mechanical pencil, busted out a calculator when I had a fair amount of data points, and lo and behold, the battle deaths did seem self-similar: they had the same kind of pattern over the century, over a part of the century, over the course of a conflict, over the course of part of that conflict, a year, a month... and sometimes that was about as close as I could look at it with the information I could get. Still, the results gave me chills. I was already in this spooky old library with retrofitted electricity and the smell of bookbinder’s glue in the stale air, of course, but it was eerie in itself nonetheless. An underground door next to me opened and air pressure moved a sort of stale wind through it whose molecules had maybe been fresh air when George Marshall delivered his famous keynote address on the ground above.
It all made intuitive sense to me: the colliding eddies in air and history and thought, and I found my way back up from the dry, dark underworld into a crisp autumn day full of fractals. I still had to do my frowned-upon research in secret, but I was a true believer, newly converted. I’d have to go elsewhere to complete my graduate work, or to declare jihad on sucky models. I had also become a lot more interested in the beginning of conflict than I had ever been in the end of it. I was a heretic. So be it.
Eventually, I found enough other chaoticists, chaoticians, fuzzy logicians, topologists, complexity theorists, and other nonlinear dynamicists of one kind or another that we could actually have conferences that were legit enough to get reimbursed and stuff. I guess that makes me more of a cultist or a zealot than a heretic now (legitimacy in numbers and all that), but it has really helped with making less sucky models. My most recent article, which appeared in the January 2008 issue of Nonlinear Dynamics, Psychology and Life Sciences, is about how societies’ stability unravels into violence. I used a method called orbital decomposition to coax the patterns out of four qualitative case studies. Each of the analyses turned up interesting things that weren’t obvious in the qualitative data, like a good non-sucky model should. I hope it helps. Because that, by definition, would also not suck.
