2017 : WHAT SCIENTIFIC TERM OR CONCEPT OUGHT TO BE MORE WIDELY KNOWN?

Over the centuries, reflective individuals have speculated about the causes of differences among individuals (who is talented and why, what makes certain persons influential) and among societies (why have certain cultures thrived and others vanished; which societies are bellicose and why). In the 19^th century, scholars began systematic study of such issues; work in this vein has continued and expanded. This line of study has been dubbed historiometrics or, alternatively, cliometrics. These terms, while literally and etymologically appropriate, are hardly transparent or snappy—among the reasons that historiometric scholars and approaches have yet to receive due credit.

Among continental scholars, the Belgian statistician Adolphe Quetelet is often credited with opening up this line of work. But in the Anglo-American scholarly community, the British polymath Francis Galton is generally seen as the patron saint of historiometric studies. As a member of the distinguished Darwin family, Galton was particularly interested in the nature and incidence of genius. Using statistical methods, he demonstrated how genius thrived in certain families and attributed this distribution to hereditary factors. Yet Galton was also sensitive to the possible confounds between hereditary and environmental contributions; and so he pioneered in comparisons of identical twins, fraternal twins, and other members of the same family. On both sides of the English channel, historiometric studies had been launched!

In the 20^th century, these lines of work, whether or not officially labeled as historiometry, continued in many locales. Deserving special mention is the American psychologist, Dean Keith Simonton, who has devoted several decades to historiometric study—carrying out dozens of intriguing studies as well as explicitly laying out the methods available in the armamentarium of the historiometric scholar. If you are speculating about the kinds of issues alluded to above—for example, during which decade of life do scholars in specific domains carry out their most influential research, during which decade of life do artists create their most enduring works—chances are that Simonton has already carried out relevant research; and, if not, he will readily suggest how such studies might be conducted and their results interpreted.

I’ve often wondered why Simonton’s work is not more widely known and appreciated. I suspect it’s because the work spans standard social science (psychology, sociology) and humanistic studies (history, the arts)—and most scholars work comfortably within, rather than across, these two cultures. It’s also notable that Simonton has worked largely alone—with neither a big staff nor a large research budget—and that is an unusual research profile in our time.

Enter big data. Neither Quetelet nor Galton had significant computational aids—pencils and paper on one’s desk were the media of choice. When Simonton began his studies in the 1970s, we were in the era of large mainframes, punch cards, and limited computing power. To be sure, Simonton (and other self-styled cliometricians like Charles Murray) has kept up with advances in technology. But only in the last decade or so has it become possible, indeed easy, to pursue historiometric puzzles, drawing on vast amounts of data that are sitting on one’s lap, or, more precisely, on one’s laptop.

Occasionally, historiometric findings have found their way into the mainstream media. A few months ago, a team of researchers led by Roberta Sinatra introduced the Q phenomenon. Curious about the distribution of influential work over the investigative lifespan of productive scientists, the researchers examined publication records of scientists drawn from seven disciplines. And they discovered—presumably to their and others’ surprise—that “the highest-impact work in a scientist’s career is randomly distributed within her body of work.” Many commentators, prominent among them Simonton, have reacted to this claim and it’s safe to say that we have not heard the last of the Q phenomenon.

Has the moment for historiometry finally arrived? Given the fascination of historiometric questions, and the relative ease these days of researching them, what was once an exotic exercise of eccentric European savants can now become a regular part of the disciplinary terrain.

I am not quite persuaded that the moment has arrived. To be sure, the availability of vast sources of data and powerful data mining techniques have greatly enhanced the "metric" part of historiometrics. Yet, the "historio" part is equally important. Historians should be judged by the quality of the questions that they raise, and the sense that they are able to make of what they have uncovered. These are issues of judgment, not merely issues of measurement. (As has long been quipped, "garbage in, garbage out.") And so, to return to the Q phenomenon, the unexpected findings of the Sinatra team open up a slew of possible explanations and interpretations. But the available data themselves will never tell us which issues to pursue next—one needs a solid historical sense as well as a dollop of historical humility. And whether the Sinatra team—or some other team or individual—will itself significantly raise the stock of historiometry depends on its historical wisdom as well as its data analytic prowess.

There may be broader lessons here. Scholarship—whether scientific or humanistic—always entails a dialectic between issues worth pursuing and the methods available for pursuing them.

Historiometric curiosity dates back to Classical times; but it has been the advent of measurement techniques (statistics, data analytics) that has permitted this curiosity to be pursued with increasing power and elegance. It’s desirable to maintain a balance between questions/curiosity/judgment, on the one hand, and analytic measures, on the other: when either becomes dominant, the pursuit itself can be compromised.

And for extra credit: when you want to explain to others what you are up to, find a succinct and memorable descriptor!