Mac Dilatush '21 — Darwin Isn’t an Empiricist, But That’s O.K.

Darwin Isn’t an Empiricist, But That’s O.K.

Mac Dilatush ’21

Darwin ranks among the fathers of modernity. His theory of natural selection, espoused in the landmark Origin of Species and later The Descent of Man, advanced science significantly and helped facilitate a shift away from religious fundamentalism by challenging prior evolutionary theories concerning intelligent design and the role of a higher power, or creator. However, some more recent thinkers have challenged Darwin’s credentials as an empiricist, defined as a thinker who believes that knowledge derives from sensory experience and scientific evidence alone. Michael Behe, in his fittingly titled Darwin’s Black Box, asserts that Darwin and other supporters of natural selection “interfere with the theory that flows naturally from observable scientific data”and fail to recognize “the conclusion of intelligent design” that derives “from the data itself” (Behe 600, 598). The debate between intelligent design and natural selection aside, Behe is correct — to an extent — that Darwin isn’t totally empiricist. Darwin sometimes re sorts to prognosticating, and he credits philosophers in his footnotes. Still, Behe’s charge fails to diminish Darwin’s general theory of natural selection and standing as a modern thinker, because current modern thinkers aren’t necessarily more empiricist than Darwin. In fact, analyzing modern standards of empiricism relative to Darwin reveals the importance of pairing and guiding data with philosophical inquiry.

Darwin often bases his conclusions on observations, asserting that “reason ought to conquer… imagination,” but he develops some of his claims through assumptions and theory instead of evidence (145). The most glaring example involves the geological record. Darwin acknowledges that his theory has been challenged to answer “why does not every collection of fossil remains afford plain evidence of the gradation and mutation of the forms of life?” (160). He admits that he “meet[s] with no such evidence” to refute the argument against his theory and that he “can answer these... objections only on the supposition that the geological record is far more imperfect than most geologists believe” (160). Darwin’s response reflects a failure to adhere to complete empiricism. He relies on an assumption to counter concerns about his evidence, or lack thereof. Similarly, Darwin contends that “although [he] do[es] not doubt that isolation is of considerable importance in the production of species, on the whole [he is] inclined to believe that largeness of area is of more importance,” but fails to provide any supporting anecdotal or empirical evidence (122). Rather, he launches into a page long discussion of the logic, or theory, behind why that might be true despite a lack of hard evidence. The weak word choice he uses to articulate his claim belies the lack of evidence. His use of both “inclined” and “believe” instead of more certain statements such as “is,” indicate that he recognizes his uncertain, in empiricist terms anyway, standing (122). Both incidents suggest that Darwin isn’t strictly empiricist. In other words, he doesn’t always utilize evidence or data to bolster his arguments and sometimes admits that he doesn’t currently possess convincing evidence for each of his convictions.

Darwin’s failure to adhere to a rigid notion of empiricism doesn’t make him less modern, though, because many modern thinkers and institutions are similarly untethered to all-encompassing empiricism. The “Grievance Studies” debacle made headlines in 2018. Three “left-leaning,” in the political sense, American college professors submitted a number of hoax papers to academic journals (Melchior). One of the articles purported to detail the results of “spen[ding] a year observing canine sexual misconduct” and was published in academic journal Gender, Place, & Culture (Melchior). According to the article’s author, math doctorate James Lindsay, the data presented was “was constructed to look outlandish on purpose. So asking us for the data would not have been out of sorts. It would have been appropriate, and we would have been exposed immediately” (Melchior). The journal, like some others implicated in the scandal, never inquired about the data reported by the hoax submissions, reflecting a lack of commitment — or perhaps failed commitment — to empiricism. Labeling the grievance incident failed empiricism suggests that just accepting any data as evidence, regardless of its quality, isn’t empiricism. By that standard, newspapers have also failed to use data in accordance with empiricism. Also in 2018, British newspapers proclaimed that “London’s monthly murder rate had exceeded New York’s” (Shaywitz). While the newspapers’ report was technically true, it ignored vital context in its display of facts; the homicide rate in each city had declined to a roughly similar level. Moreover, The New York Times was duped into publishing the false account of a Canadian man calling himself “Abu Huzafyfah” as a 12-part narrative titled “Caliphate.” The Canadian presented himself as “a member of the Islamic State who had taken part in killings in Syria” (New York Times Editorial Board). The paper discovered several discrepancies between his account and supposed facts, but chose to press ahead with the narrative. Two years later, the man was arrested for perpetrating a terrorist scam. The New York Times devoted one episode to exploring the factual contradictions of the account, but otherwise dwelled little on its potential errors and failed to highlight them in other episodes, leading the paper to admit “It is… clear that elements of the original fact-checking process were not sufficiently rigorous” (New York Times Editorial Board). The New York Times, then, also illustrates a case of modern thinkers falling short of empiricism. In The Structure of Scientific Revolutions, Thomas Kuhn echoes the view that current thinkers are not necessarily more empiricist than Darwin and others:

Historians confront growing difficulties in distinguishing the “scientific” component of past observation and belief from what their predecessors had readily labeled “error” and “superstition.” The more carefully they study, say, Aristotelian dynamics, phlogistic chemistry, or caloric thermodynamics, the more certain they feel that those once current views of nature were, as a whole, neither less scientific nor more that product of human idiosyncrasy than those current today. (2)

Translation: While scientists’ current methods of verification and epistemology may be more accurate (maybe!) than their predecessors, their methods are not therefore more “scientific” or more a product of odd human quirks or more empiricist in their commitment to data. Rather, the amount and type of evidence — and means of collecting it — has changed.

Some modern academics are pushing for a stronger commitment to data and empiricism, though, but due to the relationship between evidence and theory, their commitment to data as opposed to theory doesn’t inherently make them more effective. In a research paper titled “The Binding Force of Economics,” four economists share that “[e]conomics as a discipline has become increasingly defined by its common techniques of analysis, rather than its common theory or approach” while another paper labeled “Hamermesh (2013)... finds a similar decline in theory and rise in empirical analysis from the 1960s to 2010s” (Salter). Alexander William Salter, an associate professor of economics in the Rawls College of Business at Texas Tech University, construes the findings: “For years, economics has been getting less theoretical and more empirical. Economists are spending less time building and thinking through simple models, and more time collecting and analyzing data” (Salter). According to Salter, this is not good news: “better empirical work should certainly be applauded. But it came at a cost: an entire cohort of economists with serious theoretical blind spots” (Salter). The mantra of some of the economists less focused on theory is to “just let the data speak for itself” (Salter). That sounds great! Except, well, data cannot speak for itself. As Tim Harford writes in The Data Detective, data does not arrive through “divine providence from the numerical heavens” (Harford). David Shaywitz, a lecturer in the Department of Biomedical Informatics at Harvard Medical School, elaborates that “a data set begins with ‘somebody deciding to collect the numbers.’ It behooves us to grasp how this was done: what data were collected, who was asked — and who wasn’t” (Shaywitz). The initial questions researchers ask, which are often based on philosophical presumptions or non-empiricist intuitions, partially define the kind of data researchers will produce. Thus, forgoing theory for data can be a self-defeating premise; failing to understand theory and craft the proper question could influence data, potentially skewing results. Moreover, how data is measured depends on theory. For example, take James Lindsay’s faux study on dog parks or, as he called them, “petri dishes for canine rape culture” (Melchior). If Lindsay had actually performed the study, his results would depend on how he defined “canine sexual misconduct” (Melchior). If Lindsay defined dogs mounting each other as such, he would record each incident of mounting as an incident of canine sexual misconduct. However, Lindsay might not define mounting as sexual misconduct, because “veterinarians who specialize in canine behavior say it often is done for other reasons as well,” and he would not count such incidents as misconduct, leading him to report very different data — with less incidents of canine sexual misconduct — than he would have if his theory and its definition of terms led him to conclude that mounting constituted canine sexual misconduct (Eckstein). Ultimately, theory and data are dependent on one another. Theory determines what kind of data and evidence will be produced, and data and evidence determine what theories can be supported and to what extent. Total empiricism, then, isn’t ideal. It could impede the kind of thinking, philosophical and otherwise, necessary to ask the right questions which yield the most crucial data.

Darwin, despite his mild failings in empiricism, should still be considered a modern thinker. He heralded a scientific revolution around natural selection and attributed most of his theory to numerous facts and observations. Empiricism is not necessarily more modern, anyways — plenty of modern thinkers eschew total empiricism, and they may have good reason to; it is not always beneficial. Models and data are only as good as the assumptions they are built on.

Works Cited

Behe, Michael J. The Biochemical Challenge to Evolution. New ed., Free Press, 2006.

Darwin, Charles. Darwin, edited by Philip Appleman. 3rd ed., New York, Norton, 2001.

Eckstein, Sandy. “Humping: Why Do Dogs Do It?” WebMD, pets.webmd. com/dogs/features/humping-why-do-dogs-do-it#1.

Harford, Tim. The Data Detective: Ten Easy Rules to Make Sense of Statistics. New York, Riverhead Books, 2021.

Kuhn, Thomas S. The Structure of Scientific Revolutions. 4th ed., Chicago, University of Chicago P, 2015.

Melchior, Jillian Kay. “Fake News Comes to Academia.” Wall Street Journal, 5 Oct. 2018, Opinion sec., www.wsj.com/articles/fakenews-comes-to-academia-1538520950.

New York Times Editorial Board. "An Examination of 'Caliphate'". New York Times, 18 Dec. 2020.

Salter, Alexander William. “How Economics Lost Itself in Data.” Wall Street Journal, 27 Jan. 2020, Opinion sec., www.wsj.com/articles/howeconomics-lost-itself-in-data-11611775849?mod=searchresults_ pos11&page=2.

Shaywitz, David A. “’The Data Detective’ Review: Broadly Informed, Easily Misled.” Wall Street Journal, 28 Jan. 2021, Opinion sec., www. wsj.com/articles/the-data-detective-review-broadly-informed-easilymisled-11611875753?mod=searchresults_pos14&page=1.