“On the Frontier of the Empire of Chance”

Arwen Mohun, “On the Frontier of The Empire of Chance: Statistics, Accidents, and Risk in Industrializing America.” Science in Context 3 (2005): 337-357.

In “On the Frontier of The Empire of Chance,” author Arwen Mohun examines the rise in statistics and probabilistic thinking in the American vernacular context from the late nineteenth through the early twentieth centuries. Through the lens of a cultural historian of technology, Mohun takes a closer look at how the industrial-era quantification of risk altered the way people understood it; she asks why and how this transformation took place, and then delves into how these understandings were shaped and used in order to mold individual behavior and enact widespread change. Mohun argues that the actors in her narrative existed on the periphery of the Empire of Chance. While experts, primarily located in European centers of statistical theorizing, formed the “epicenter” of the empire, those on the frontier employed statistics as a tool in social manipulation. Far from relegating popular audiences to a primarily observational, inert role, however, the author also acknowledges their agency in the story by explaining how their motivations affected their choices regarding risk and reward.

Obviously, Mohun’s work builds off of the book she references in her title — The Empire of Chance. Her piece is different from that of Gigerenzer et al., however, in that it addresses how the methodological and intellectual developments of professional statisticians found their way into popular understandings of variability and the risks associated with it. This is reminiscent of Dr. Pandora’s assigned reading for her two weeks of 5990 at the beginning of the semester — Spectacular Nature and The Whale and the Supercomputer. Like Mohun’s work, Susan G. Davis looks at how ideas from the “top,” the professional scientists, filter down into the vernacular through institutions like SeaWorld. Mohun also looks at how institutions influence the way that popular audiences understand scientific theories, their consequences, and their uses. In contrast, Charles Wohlforth focuses on how non-professional ways of knowing had a major impact on the way scientists looked at and understood climate change in the arctic. Mohun mimics this approach when she includes in her analysis how the importance of individual experience affects the way that the average American understood and behaved in regards to risk-taking. When the approach involves popular science, both perspectives — top-down and bottom-up — are important for a holistic understanding of how science and vernacular audiences interact and influence one another, and in this regard, Mohun as clearly covered all of her bases.

Something I found particularly interesting in this piece was the discussion of the “pragmatic approach” to science that Mohun discusses primarily on pages 339 and 340. She argues that it was especially characteristic of American statisticians in the time period she covers, and cites as evidence their absence from histories of statistics. American statisticians worried less about developing sound theories and methods and more about applying their knowledge (no matter how unsound or theoretically dubious) to real-world problems. This embodied what I have come to understand as being a very Industrial-American ideal; the self-made, self-trained practitioner unconcerned with the useless, bookish knowledge so characteristic of their less hard-working, impractical European counterparts. I wonder if the different approaches caused animosity between American and European statisticians; they were obviously sharing ideas. What did these conversations look like, and how did they take place? Was it common for Americans to train abroad, or were universities in America training these frontiersmen of the Empire of Chance?

The Empire of Chance

The Empire of Chance: How Probability Changed Science and Everyday Life, Gerd Gigerenzer, Zeno Swijtink, Theodore Porter, Lorrain Daston, John Beatty, and Lorenz Krüger

            In their collaborative work, authors Gerd Gigerenzer, Zeno Swijtink, Theodore Porter, Lorrain Daston, John Beatty, and Lorenz Krüger attempt a cohesive study of how the science of statistics “transformed our ideas of nature, mind, and society.” (xiv) The first three chapters present a timeline on which the intellectual development of the science of statistics — with some consideration of its particular applications — is situated, the middle three deal with statistics in particular fields, and the last two concern broader implications of statistical analyses, ideologies, and methodologies. A central theme of the book is the idea that the science of statistics was both shaped and shaped by the sciences that it aided and that helped to develop it for their own explanatory and predictive goals. Professing to be the first of its kind, the survey offers detailed technical descriptions and examples that flesh out the mathematics and theories with which its actors are working.

The passages dealing with mid-nineteenth century debates surrounding the viability of statistical methods for physicians reminded me of S. Lochlann Jain’s criticisms of the same methods in her work, Malignant. Jain and her unlikely intellectual compatriots cite similar issues with the “numerical method” in medicine; it denies the complexity and uniqueness of the individual patient, aiming “not to cure the disease, but to cure the most possible out of a certain number” (Risueño d’Amador, 1836, 46). This results in the emotions Jain so skillfully articulates in her first-hand account as a cancer patient. Reduced to numbers, cancer sufferers are identified by the statistical methods their doctors use to diagnose and treat them. Equally concerning is the reliance of pharmaceutical companies on results from statistical studies to produce drugs that will target cancer on a broader scale, to the detriment of patients who would have benefitted from more personalized treatments. Perhaps these nineteenth century critics were not off base in their hesitancy to adopt such a dehumanizing method of handling disease.

Another bit I found particularly interesting was section 3.5, “Hybridization: the Silent Solution.” Having taken statistics and seen it in what I am now realizing was a surprising amount of my undergraduate science classes, I was struck by the fact that the statistical methods we learn as absolute and established are in fact far from it. Integral tenets to the type of statistics I was taught are, in actuality, theoretically at odds with one another, and yet, as the authors contend, “Statistics is treated as abstract truth, a monolithic logic of inductive inference.” (107) Because statistical methods are so widespread, I find it both surprising and alarming that these obvious impediments to its image as a well-established and unproblematic method of analysis are kept more or less hidden. It lead me into thinking about how oftentimes, when scientific disciplines are “successfully” mathematized, we deem them somehow more intelligible; they become more solid, their results more trust-worthy. Is this a valid logical jump to make, especially if statistics, one of the mathematical sciences that is employed most often, rests on shaky ground?