A few days ago I ran across a Scientific American blog post that struck me as interesting but somewhat disappointing: Humanities aren’t a science. Stop treating them like one. The writer, Maria Konnikova, begins by noting, quite reasonably, that precise, mathematical approaches to knowledge are not always appropriate. This idea that quantitative approaches aren’t universally applicable is repeated several times throughout the piece, but overall it sounds more like a “barbarians at the gate” polemic, only in this case the barbarians are the number-crunchers who are taking over the humanities. I was disappointed by this because I think there are a lot of interesting things to be said about when and where mathematical approaches should be used or avoided.
For starters, I think the point about excessive reliance on numbers and statistics and “hard science” techniques is valid in some cases, but it’s not limited to the humanities. Certain areas of the biological sciences in particular are just about as squishy and hard to pin down as anything in the humanities. I would bet that there are plenty of ecologists or animal ethologists, for example, who are quite interested in things that are not easily quantified. Mathematical models of ecosystems don’t tell the whole story (although that doesn’t make them worthless). I’d also guess that old-fashioned field work and qualitative observations are being downplayed to one degree or another in favor of more high-tech pursuits.
At the Consilience Conference in St. Louis this April, I heard several talks by humanists who use mathematics or statistics in their work. Granted, it’s a small sample, but all of them seemed sensitive to the lurking pitfalls and careful about defining the role of statistical work. One of the presenters, Jonathan Gottschall, has also conducted a statistical analysis of fairy tales, which I read about just before the conference. His goal in that study was to examine a particular claim about fairy tales: that European ones more or less uniquely reinforce a particular set of patriarchal gender roles, and more broadly that gender roles are much more influenced by nurture than by nature. He and his colleagues did this by a careful statistical analysis of tales from around the world. (Short answer: Overall, the portrayal of gender roles appears to be broadly similar worldwide. The study appears in the book The Literary Animal.)
In the introduction to that paper, he traced the use of statistics in the study of human populations and behavior back to the 1660s and pointed out that ever since they first began to be used, statistics have revealed unexpected or counterintuitive facts and relationships that help a field of study to grow and mature. He predicts that their “limited and judicious use” can do the same for literary studies and even improve the “power and precision” of more qualitative work. (The impression I get from reading his work and that of Joseph Carroll, perhaps the original literary Darwinist, is that, at least among their established colleagues, they still need to argue for wider acceptance of the idea that evolutionary science and statistical studies are appropriate to the study of literature. I’m no literary scholar myself, but it doesn’t look to me like they’re riding a wave of overwhelming approval or that their approach is steamrollering the humanities.)
Konnikova seems skeptical that mathematical studies of literary works can enrich the field. Carroll, Gottschall, John A. Johnson, and Daniel J. Kruger recently wrote Graphing Jane Austen, a book that analyzes the characters in approximately two hundred 19th-century British novels. They asked literary scholars to fill out questionnaires about the characters, analyzed the results, and came up with insights into how these works reflect views on personality and gender roles and how they fit into an evolutionary understanding of human nature. It’s clearly not the only way to study literature, but it certainly seems worthwhile and even fascinating. Judging from the number of young people at the conference who seemed enthusiastic about the work of Carroll, Gottschall, and others, it looked to me like the humanities are maybe not so much being “relegated … to a bunch of trends and statistics and frequencies,” as Konnikova claims, as rejuvenated.
In addition, the fact that numerical data are difficult to obtain or that they ignore parts of the picture doesn’t make them useless. Konnikova quotes Carol Tavris on how psychological researchers often ignore the ways their findings might be affected by factors such as social class, culture, or personal history. This doesn’t mean that those things can’t be taken into account in future studies; the results may be valuable even if they’re still imperfect. Human societies and human individuals are “fuzzy” at all levels of analysis, and there is a danger in pretending that things are more hard-edged than they are. (For example, any psychological study on the behavior of five hundred 20-year-olds at a midwestern university is not giving you anything like the whole picture on human nature, and there’s even some suggestion that people in western industrialized nations are not necessarily all that representative of humankind in general.) But then, human bodies are by no means uniform and their operation is far from clear-cut, and it’s still worth doing medical research to try to arrive at general truths, even if those must then be refined.
Along the same lines, Peter Turchin spoke at the conference about Cliodynamics (history as science; the name comes from the muse of history, Clio). He began by describing part of his motivation for using a data-driven, analytical approach. Historians have pretty much stopped proposing general laws of history, but when they construct a historical narrative, they also propose explanations for why things happened as they did. They may not articulate general laws for, say, how an empire falls, but Turchin feels that the laws are lurking there implicitly in these explanations. The very richness and complexity of historical information make it difficult to reason by historical analogy when seeking causes (too many plausible stories can be constructed with no way to test them).
He thinks the only way forward is to build general models and test them using historical data. He noted that you have to be careful when searching for appropriate proxies for whatever it is you want to study (e.g., he used the number of filibusters as one way to get at whether America’s social capital is declining), and how hard it can be to find usable long-term data; Konnikova also mentions this point, but Turchin described some ways to work within these limitations (relying on multiple proxies and checking them against each other, for example). The difficulties don’t invalidate the approach.
I suspect that my main problem with the SciAm blog post was the confusion over what the sciences are and do compared to the humanities. It seemed to identify numerical analysis solely with the hard sciences (even though psychology and sociology have relied heavily on statistical studies for decades) and the hard sciences more or less entirely with precise mathematical methods (even though the biological and geological sciences, for example, have relied on qualitative and descriptive methods). (It also wasn’t clear what counts as a social science and what counts as the humanities.) There was also some confusion about whether math makes things harder or easier. Math supposedly makes things seem tidy, linear, and easily graspable, even if they’re not, but then political science and psychology rely too much on “fancy statistics.” My limited exposure to statistics has been enough to tell me that sophisticated statistics are not necessarily simple or clean-cut, either in their application or in their interpretation (although I will admit that careless news stories sometimes make them seem that way). There’s also quite a lot to be said about the role of imagination, intuition, and similar intangibles in the scientific process. The humanities indeed are not the sciences, but there’s more common ground (and a far more intricate and complex set of similarities and differences) than came through in that post. I’d love to hear from humanists and scientists both about their perceptions of their fields.