Paul Krugman, the distinguished Princeton University economics professor and New York Times columnist, once explained the jejune motives for his choice of career. “In my early teens my secret fantasy was to become a psychohistorian,” he wrote, referring to the central gimmick, “psychohistory,” of Isaac Asimov’s Foundation trilogy. Krugman continued, “Someday there will exist a unified social science of the kind that Asimov imagined, but for the time being economics is as close to psychohistory as you can get.”
That’s risible, given the gulf between Asimov’s fantasy of a predictive calculus of human affairs and the actuality of mainstream economics–indeed, of any of the social sciences–as practiced during most of the last century. Recent decades, though, have seen new approaches. One of the most promising was described by Joshua Epstein, a senior fellow at the Brookings Institution, in Growing Artificial Societies: Social Science from the Bottom Up, a book he published in 1996 in collaboration with Robert Axtell. “Perhaps one day people will interpret the question, ‘Can you explain it?’ as asking ‘Can you grow it?’” Epstein suggested. “Artificial society modeling allows us to ‘grow’ social structures in silico demonstrating that certain sets of microspecifications are sufficient to generate the macrophenomena of interest.”
What does this mean? And why should we care? Epstein’s claim was twofold. First, he pointed out that while almost all the patterns that interest social scientists are emergent ones–that is, complex developments arising from a lot of relatively simple interactions–disciplines such as mainstream economics conceive of societies as tending toward some notional equilibrium. Standard explanations assume, too, that societies consist of highly rational agents who, possessing full knowledge, act always in their own best interest. When it comes to how real populations of diverse actors with limited rationality actually evolve their patterns of, say, wealth distribution, Epstein noted, the stock explanations have almost nothing to say. (See “A Letter to the Editor from Joshua Epstein.”)
Epstein was hardly alone in making those criticisms. But he proposed, secondly, that computer models in themselves could effectively describe societies. In the early 1990s, Epstein and Axtell had created a simulation called Sugarscape, a square grid representing a two-dimensional landscape inhabited by autonomous subprograms–agents–that were driven from square to square by crude artificial metabolisms that demanded a resource, designated “sugar.” When hundreds of these agents were programmed so that their ranges of vision and metabolic rates varied, even in simple ways, surprising patterns emerged.
Indeed, Epstein and Axtell would learn that with their models, “the trick [was] to get a lot out, while putting in as little as possible,” as Epstein writes in his latest book, Generative Social Science: Studies in Agent-Based Computational Modeling. In the early 1990s, the two men set up two regions of their Sugarscape grid to be rich in the sugar resource, so that agents quickly gravitated toward them. A few agents with superior vision and low metabolic rates accumulated large sugar stocks. Other agents, with weaker vision and high metabolic rates, subsisted or died in zones where sugar was in short supply. Essentially, Epstein and Axtell found, Sugarscape functioned as a model of a hunter-gatherer society, reproducing a common feature of human societies: skewed wealth distribution. Granted, the notion that crude automata moving around a computer grid suggest that wealth inequality is an innate feature of human existence will be disliked not only by Marxists but by most of the rest of us, given how varied we know our individual experiences to be. Nevertheless, nature is full of peculiarly consistent statistical relationships, which reoccur across dissimilar realms and which statisticians call “power laws.”