It’s an unusual place for it, but a blog in Scientific American argues that the humanities should stop trying to be like sciences and instead embrace their non-quantifiable nature.
In the article “Humanities aren’t a science. Stop treating them like one.”, writer and psychology PhD candidate Maria Konnikova responds to the recent publication of “Universal Properties of Mythological Networks” by writing that some subjects aren’t, never have been, won’t ever be, and most of all shouldn’t be approached “scientifically.”
Konnikova’s critical article goes into greater depth on the subject than I did in my narrower and mostly positive response to the same article, posted here in late July. She critiques the specific study in question, but her real topic is the trend of which the study is but one current example.
The crux of Konnikova’s dislike of “scientific humanities” is straightforward: the humanities do not lend themselves to empirical investigation, and attempts to take these subjects in quantitative directions are misguided.
Every softer discipline these days seems to feel inadequate unless it becomes harder, more quantifiable, more scientific, more precise. That, it seems, would confer some sort of missing legitimacy in our computerized, digitized, number-happy world. But does it really? Or is it actually undermining the very heart of each discipline that falls into the trap of data, numbers, statistics, and charts?
Although I am more willing to entertain new approaches to these old subjects than Konnikova seems to be, I understand her concern. In several articles here, I have questioned overly-enthusiastic quantification of literature (see “Doing Hamlet by the Numbers,” from last year, for one example).
And while I wrote last month that “at least some quantitative methodologies are applicable to the study of literature,” at the end of my review of the mythology study I noted, much like Konnikova, that “as long as reading literature involves a stimulated human mind as part of the equation, analyzing just the concrete, on the page part of the activity must fall short of a complete explanation.”
Konnikova is not just a writer but also a psychologist, and she moves from literature to criticism of that field, which — despite all of the brain scans and fancy statistical analyses — she places firmly in the “social sciences” area, alongside subjects like political science and history.
She’s not as rigid as is my math PhD friend, who calls everything except math, physics, and inorganic chemistry a “soft science,” but her conviction is clear that the study of anything “human” can’t be fully understood merely by being fully quantified.
Sometimes, there is no easy approach to studying the intricate vagaries that are the human mind and human behavior. Sometimes, we have to be okay with qualitative questions and approaches that, while reliable and valid and experimentally sound, do not lend themselves to an easy linear narrative—or a narrative that has a base in hard science or concrete math and statistics. Psychology is not a natural science. It’s a social science. And it shouldn’t try to be what it’s not.
Throughout her article, Konnikova makes the case, both explicitly and implicitly, that there are things that will never be understood in the same linear, numerical way that we understand, say, Newtonian mechanics. The clear implication is that this “limitation” is not a flaw but a good thing, and that “science envy” shouldn’t be allowed to lead astray those subjects where art and judgement are more important than computation and statistics.
As a former teacher of literature, I should be wholeheartedly behind Konnikova in her warnings about the dangers of soul-less calculation. But I’m not. I don’t see that there’s any real danger in applying quantitative methods to those areas of the humanities where these methods make a real contribution.
It’s true that we don’t need to do charts of Hamlet to know that it’s a tragedy, but I don’t see much danger that eventually the only valid or useful analysis Shakespeare’s play will come from a computer algorithm.