In a recent think piece from Drexel University’s The Smart Set, author Michael Lund boldly proclaims that it’s time for western academia to throw in the towel on a decades-long failed experiment: “hard” social science.
If you’ve ever gotten a “stimulus check” and taken it directly to the bank, or bought your favorite brand of lip balm even though the store’s was cheaper, you might understand Lund’s frustration with fields like modern economics. The idea that a society or an economy can be modeled like a fluid—subject to forces of supply and demand, and bound by the universal Theory of the Rational Consumer—has been growing ever more popular since WWII, and Lund argues that it’s been nothing but trouble.
These and similar ideas, which have grown under the name of “neoclassical economics”, stem from misguided attempts to express social sciences in a way that’s just as “mathy” as the formulae that describe the physical sciences, founded in the notion that at some fundamental level, society obeys laws just like any composite physical system. It’s an understandable desire from a lot of perspectives; what politician wouldn’t want an equation that could tell him which way public opinion is going to swing based on some “unemployment coefficient”? And given larger academia’s history of skeptical derision for fields like literary criticism and computational linguistics, scholars in these areas undoubtedly feel social pressure to prove that their science can be as hard and predictively useful as things like physics.
And it’s tempting to draw connections between physical phenomena and social ones, especially as nature often rewards such fractal thinking with surprising insights. It’s pleasing to imagine a society on the brink of revolution as “superheated”, ready to change phase violently once its container is jostled hard enough. It can be fun to think of cars on the road as electrons in wires, spreading out and taking parallel paths to avoid a junction with a high-resistance traffic light. As a social movement gains popularity, then faces a backlash, then gains ground again as the backlash-to-the-backlash emerges, it’s hard not to be reminded of a mass on a spring, or the motion of a pendulum. The problem arises when people extrapolate these similarities farther than they can reasonably be expected to go. Anyone who imagines that something as complex as a region’s political spectrum could be modeled like the behavior of a pendulum must have left the room before the professor attached a second pendulum to the first.
Once upon a time, a fortune could be made in the stock market with enough startup capital, simply by regarding prices with clinical detachment and applying the principle of action and reaction; Roger Babson founded the Gravity Research Foundation with money he made by keeping in mind the principle that “what goes up must come down”, a principle which protected him from the stock market crash that preceded the Great Depression. However, like in a crowd where everyone is trying to push their way to the front, this has become harder and harder to do as more investors attempt to employ the same strategy. In more modern times, some mathematicians—known in the financial sector as “quant”s—have begun making fortunes of their own by applying formulaic strategies of buying and selling, but these strategies work largely because they prey upon formulae written by other economists at large hedge funds and investment firms. By reverse-engineering the trading strategies of such major players, quants make their money from an almost comical meta-application of the “rational consumer choice” axiom: investors, acting in accordance with a vastly oversimplified view of what drives consumer behavior, will behave far more reliably than the consumers themselves.
The problem with trying to treat social science like the physical sciences—to hold it to the same standard of predictive “mathiness”—is that there’s no reason to expect social systems to behave with the same regularity as physical systems. Math is such a powerful tool for describing physical systems because, for all practical purposes, one water molecule is exactly like another. As a result, we can consider a trillion of them all at once, under the equations of fluid mechanics, and adequately predict the system’s behavior.
However, in a social system, Lund says, our most powerful tool is not the equation, but
“…‘understanding’ in the sense of insight based on imaginative identification with another person. If you want to understand why Napoleon invaded Russia, you have to put yourself in Napoleon’s place. You have to imagine that you are Napoleon and look at the world from his perspective at the moment of his decision. The skills that this exercise requires of the historian or political scientist are more akin to those of the novelist or dramatist than those of the mathematician or physicist.”
This assertion, somewhat ironically, is backed up by recent behavioral research. In attempting to simulate how people evacuate a room during a fire, scientists tested a number of different models. Some, called flow-based models, treat individuals in a room like particles in a fluid. Others, called Multi-Agent System (MAS) models, simulate each individual, capable of making dynamic decisions based on a number of factors. The results with MAS models are generally far less efficient than their flow-based counterparts, but this may make them better models for humans’ erratic behavior.
If we want to treat society like a body of water, Lund seems to argue, it won’t do to try and find the viscosity. Instead, we should focus on the quantum unit of that society, the water molecule: the psyche. And perhaps once we know how that molecule’s shape influences the way it bonds to its neighbors, we can begin to understand the geometry of a snowflake, which might in turn tell us something about how social avalanches form. But let’s stop short of trying to find the angle of repose; that way madness lies.