Forked

He had already heard that the Roman armies were hemmed in between the two passes at the Caudine Forks, and when his son’s courier asked for his advice he gave it as his opinion that the whole force ought to be at once allowed to depart uninjured. This advice was rejected and the courier was sent back to consult him again. He now advised that they should every one be put to death. On receiving these replies … his son’s first impression was that his father’s mental powers had become impaired through his physical weakness. … [But] he believed that by taking the course he first proposed, which he considered the best, he was establishing a durable peace and friendship with a most powerful people in treating them with such exceptional kindness; by adopting the second he was postponing war for many generations, for it would take that time for Rome to recover her strength painfully and slowly after the loss of two armies.
There was no third course.
Titus LiviusAb Urbe Condita. Book IX 

 

Of course, we want both,” wrote Lee C. Bollinger, the president of Columbia University, in 2012, about whether “diversity in post-secondary schools should be focused on family income rather than racial diversity.” But while many might wish to do both, is that possible? Can the American higher educational system serve two masters? According to Walter Benn Michaels of the University of Illinois at Chicago, Bollinger’s thought that American universities can serve both economic goals and racial justice has been the thought of “every academic” with whom he’s ever discussed the subject—but Michaels, for his part, wonders just how sincere that wish really is. American academia, he says, has spent “twenty years of fighting like a cornered raccoon on behalf of the one and completely ignoring the other”; how much longer, he wonders, before “‘we want both’ sounds hollow not only to the people who hear it but to the people who say it?” Yet what Michaels doesn’t say is just why, as pious as that wish is, it’s a wish that is necessarily doomed to go unfulfilled—something that is possible to see after meeting a fictional bank teller named Linda.

Linda”—the late 1970s creation of two Israeli psychologists, Amos Tversky and Daniel Kahneman—may be the most famous fictional woman in the history of the social sciences, but she began life as a single humble paragraph:

Linda is thirty-one years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.

Following that paragraph, there were a series of eight statements describing Linda—but as the biologist Stephen Jay Gould would point out later, “five are a blind, and only three make up the true experiment.” The “true experiment” wouldn’t reveal anything about Linda—but it would reveal a lot about those who met her. “Linda,” in other words, is like Nietzsche’s abyss: she stares back into you.

The three pointed statements of Kahneman and Tversky’s experiment are these: “Linda is active in the feminist movement; Linda is a bank teller; Linda is a bank teller and is active in the feminist movement.” The two psychologists would then ask their test subjects to guess which of the three statements was more likely. Initially, these test subjects were lowly undergraduates, but as Kahneman and Tversky performed and then re-performed the experiment, they gradually upgraded: using graduate students with a strong background in statistics next—and then eventually faculty. Yet, no matter how sophisticated the audience to which they showed this description, what Kahneman and Tversky found was that virtually everyone always thought that the statement “Linda is a bank teller and active in the feminist movement” was more likely than the statement “Linda is a bank teller.” But as only a little thought requires, that is impossible.

I’ll let the journalist Michael Lewis, who recently published a book about the work of the pair of psychologists entitled The Undoing Project: A Friendship That Changed Our Minds, explain the impossibility:

“Linda is a bank teller and is active in the feminist movement” could never be more probable than “Linda is a bank teller.” “Linda is a bank teller and is active in the feminist movement” was just a special case of “Linda is a bank teller.” “Linda is a bank teller” included “Linda is a bank teller and is active in the feminist movement” along with “Linda is a bank teller and likes to walk naked through Serbian forests” and all other bank-telling Lindas. One description was entirely contained by the other.

“Linda is a bank teller and is active in the feminist movement” simply cannot be more likely than “Linda is a bank teller.” As Louis Menand of Harvard observed about the “Linda problem” in The New Yorker in 2005, thinking that “bank teller and feminist” is more likely than the “bank teller” description “requires two things to be true … rather than one.” If the one is true so is the other; that’s why, as Lewis observed in an earlier article on the subject, it’s “logically impossible” to think otherwise. Kahneman and Tversky’s finding is curious enough on its own terms for what it tells us about human cognition, of course, because it exposes a reaction that virtually every human being ever encountering it has made. But what makes it significant in the present context is that it is also the cognitive error Lee C. Bollinger makes in his opinion piece.

“The Linda problem,” as Michael Lewis observed in The Undoing Project, “resembled a Venn diagram of two circles, but with one of the circles wholly contained by the other.” One way to see the point, perhaps, is in relation to prison incarceration. As political scientist Marie Gottschalk of the University of Pennsylvania has observed, although the

African-American incarceration rate of about 2,300 per 100,000 people is clearly off the charts and a shocking figure … [f]ocusing so intently on these racial disparities often obscures the fact that the incarceration rates for other groups in the United States, including whites and Latinos, is also comparatively very high.

While the African-American rate of imprisonment is absurdly high, in other words, the “white incarceration rate in the United States is about 400 per 100,000,” which is at least twice the rate of “the most punitive countries in Western Europe.” What that means is that, while it is possible to do something regarding, say, African-American incarceration rates by lowering the overall incarceration rates, it can’t be done the other way.“Even,” as Gottschalk says, “if you released every African American from US prisons and jails today, we’d still have a mass incarceration crisis in this country.” Releasing more prisoners means fewer minority prisoners, but releasing minority prisoners still means a lot of prisoners.

Which, after all, is precisely the point of the “Linda problem”: just as “bank teller” contains both “bank teller” and any other set of descriptors that could be added to “bank teller,” so too does “prisoner” include any other set of descriptors that could be added to it. Hence, reducing the prison population will necessarily reduce the numbers of minorities in prison—but reducing the numbers of minority prisoners will not do (much) to reduce the number of prisoners. “Minority prisoners” is a circle contained within the circle of “prisoners”—saying you’d like to reduce the numbers of minority prisoners is essentially to say that you don’t want to do anything about prisons.

Hence, when Hillary Clinton asked her audience during the recent presidential campaign “If we broke up the big banks tomorrow … would that end racism?” and “Would that end sexism?”—and then answered her own question by saying, “No,” what she was effectively saying was that she would do nothing about any of those things, racism and sexism included. (Which, given that this was the candidate who asserted that politicians ought to have “both a public and a private position,” is not out of the question.) Wanting “both,” or an alleviation of economic inequality and discrimination—as Lee Bollinger and “every academic” Walter Benn Michaels has ever talked to say they want—is simply the most efficient way of not getting either. As Michaels says, “diversity and antidiscrimination have done and can do [emp. added] nothing whatsoever to mitigate economic inequality.” The sooner that Americans realize that Michaels isn’t kidding—that anti-discrimination, identity politics is not an alternative solution, but in fact no solution—and why he’s right, the sooner that something could be done about America’s actual problems.

Assuming, of course, that’s something anyone really wants.

Advertisements

Size Matters

That men would die was a matter of necessity; which men would die, though, was a matter of circumstance, and Yossarian was willing to be the victim of anything but circumstance.
Catch-22.
I do not pretend to understand the moral universe; the arc is a long one, my eye reaches but little ways; I cannot calculate the curve and complete the figure by the experience of sight; I can divine it by conscience. And from what I see I am sure it bends towards justice.
Things refuse to be mismanaged long.
—“Of Justice and the Conscience.

 

monte-carlo-casino
The Casino at Monte Carlo

 

 

Once, wrote the baseball statistician Bill James, there was “a time when Americans” were such “an honest, trusting people” that they actually had “an unhealthy faith in the validity of statistical evidence”–but by the time James wrote in 1985, things had gone so far the other way that “the intellectually lazy [had] adopted the position that so long as something was stated as a statistic it was probably false.” Today, in no small part because of James’ work, that is likely no longer as true as it once was, but nevertheless the news has not spread to many portions of academia: as University of Virginia historian Sophia Rosenfeld remarked in 2012, in many departments it’s still fairly common to hear it asserted—for example—that all “universal notions are actually forms of ideology,” and that “there is no such thing as universal common sense.” Usually such assertions are followed by a claim for their political utility—but in reality widespread ignorance of statistical effects is what allowed Donald Trump to be elected, because although the media spent much of the presidential campaign focused on questions like the size of Donald Trump’s … hands, the size that actually mattered in determining the election was a statistical concept called sample size.

First mentioned by the mathematician Jacob Bernoulli made in his 1713 book, Ars Conjectandi, sample size is the idea that “it is not enough to take one or another observation for such a reasoning about an event, but that a large number of them are needed.” Admittedly, it might not appear like much of an observation: as Bernoulli himself acknowledged, even “the most stupid person, all by himself and without any preliminary instruction,” knows that “the more such observations are taken into account, the less is the danger of straying from the goal.” But Bernoulli’s remark is the very basis of science: as an article in the journal Nature put the point in 2013, “a study with low statistical power”—that is, few observations—“has a reduced chance of detecting a true effect.” Sample sizes need to be large enough to be able to eliminate chance as a possible factor.

If that isn’t known it’s possible to go seriously astray: consider an example drawn from the work of Israeli psychologists Amos Tversky (MacArthur “genius” grant winner) and (Nobel Prize-winning) Daniel Kahneman—a study “of two toys infants will prefer.” Let’s say that in the course of research our investigator finds that, of “the first five infants studied, four have shown a preference for the same toy.” To most psychologists, the two say, this would be enough for the researcher to conclude that she’s on to something—but in fact, the two write, a “quick computation” shows that “the probability of a result as extreme as the one obtained” being due simply to chance “is as high as 3/8.” The scientist might be inclined to think, in other words, that she has learned something—but in fact her result has a 37.5 percent chance of being due to nothing at all.

Yet when we turn from science to politics, what we find is that an American presidential election is like a study that draws grand conclusions from five babies. Instead of being one big sample—as a direct popular national election would be—presidential elections are broken up into fifty state-level elections: the Electoral College system. What that means is that American presidential elections maximize the role of chance, not minimize it.

The laws of statistics, in other words, predict that chance will play a large role in presidential elections—and as it happens, Tim Meko, Denise Lu and Lazaro Gamio reported for The Washington Post three days after the election that “Trump won the presidency with razor-thin margins in swing states.” “This election was effectively decided,” the trio went on to say, “by 107,000 people”—in an election in which more than 120 million votes were cast, that means that election was decided by less than a tenth of one percent of the total votes. Trump won Pennsylvania by less than 70,000 votes of nearly 6 million, Wisconsin by less than 30,000 of just less than three million, and finally Michigan by less than 11,000 out of 4.5 million: the first two by just more than one percent of the total vote each—and Michigan by a whopping .2 percent! Just to give you an idea of how insignificant these numbers are by comparison with the total vote cast, according to the Michigan Department of Transportation it’s possible that a thousand people in the five largest counties were involved in car crashes—which isn’t even to mention people who just decided to stay home because they couldn’t find a babysitter.

Trump owes his election, in short, to a system that is vulnerable to chance because it is constructed to turn a large sample (the total number of American voters) into small samples (the fifty states). Science tells us that small sample sizes increase the risk of random chance playing a role, American presidential elections use a smaller sample size than they could, and like several other presidential elections, the 2016 election did not go as predicted. Donald Trump could, in other words, be called “His Accidency” with even greater justice than John Tyler—the first vice-president to be promoted due to the death of his boss in office—was. Yet, why isn’t that point being made more publicly?

According to John Cassidy of The New Yorker, it’s because Americans haven’t “been schooled in how to think in probabilistic terms.” But just why that’s true—and he’s essentially making the same point Bill James did in 1985, though more delicately—is, I think, highly damaging to many of Clinton’s biggest fans: the answer is, because they’ve made it that way. It’s the disciplines where many of Clinton’s most vocal supporters make their home, in other words, that are most directly opposed to the type of probabilistic thinking that’s required to see the flaws in the Electoral College system.

As Stanford literary scholar Franco Moretti once observed, the “United States is the country of close reading”: the disciplines dealing with matters of politics, history, and the law within the American system have, in fact, more or less been explicitly constructed to prevent importing knowledge of the laws of chance into them. Law schools, for example, use what’s called the “case method,” in which a single case is used to stand in for an entire body of law: a point indicated by the first textbook to use this method, Christopher Langdell’s A Selection of Cases on the Law of Contracts. Other disciplines, such as history, are similar: as Emory University’s Mark Bauerlein has written, many such disciplines depend for their very livelihood upon “affirming that an incisive reading of a single text or event is sufficient to illustrate a theoretical or historical generality.” In other words, it’s the very basis of the humanities to reject the concept of sample size.

What’s particularly disturbing about this point is that, as Joe Pinsker documented in The Atlantic last year, the humanities attract a wealthier student pool than other disciplines—which is to say that the humanities tend to be populated by students and faculty with a direct interest in maintaining obscurity around the interaction between the laws of chance and the Electoral College. That doesn’t mean that there’s a connection between the architecture of presidential elections and the fact that—as Geoffrey Harpham, former president and director of the National Humanities Center, has observed—“the modern concept of the humanities” (that is, as a set of disciplines distinct from the sciences) “is truly native only to the United States, where the term acquired a meaning and a peculiar cultural force that it does not have elsewhere.” But it does perhaps explain just why many in the national media have been silent regarding that design in the month after the election.

Still, as many in the humanities like to say, it is possible to think that the current American university and political structure is “socially constructed,” or in other words could be constructed differently. The American division between the sciences and the humanities is not the only way to organize knowledge: as the editors of the massive volumes of The Literary and Cultural Reception of Darwin in Europe pointed out in 2014, “one has to bear in mind that the opposition of natural sciences … and humanities … does not apply to the nineteenth century.” If that opposition that we today find so omnipresent wasn’t then, it might not be necessary now. Hence, if the choice of the American people is between whether they ought to get a real say in the affairs of government (and there’s very good reason to think they don’t), or whether a bunch of rich yahoos spend time in their early twenties getting drunk, reading The Great Gatsby, and talking about their terrible childhoods …well, I know which side I’m on. But perhaps more significantly, although I would not expect that it happens tomorrow, still, given the laws of sample size and the prospect of eternity, I know how I’d bet.

Or, as another sharp operator who’d read his Bernoulli once put the point:

The arc of the moral universe is long, but it bends towards justice.”

 

All Even

George, I am an old man, and most people hate me.
But I don’t like them either so that makes it all even.

—Mr. Potter. It’s A Wonderful Life (1946).

 

dscf3230-1

Because someone I love had never seen it, I rewatched Frank Capra’s 1946 It’s A Wonderful Life the other night. To most people, the film is the story of how one George Bailey comes to perceive the value of helping “a few people get outta [the] slums” of the “scurvy little spider” of the film, the wealthy banker Mr. Potter—but to some viewers, what’s important about the inhabitants of Bedford Falls isn’t that they are poor by comparison to Potter, but instead that some of them are black: the man who plays the piano in the background of one scene, for instance, or Annie, the Bailey family’s maid. To Vincent Nobile, a professor of history at Rancho Cucamonga’s Chaffey College, the casting of these supporting roles not only demonstrates that “Capra showed no indication he could perceive blacks in roles outside the servant class,” but also that Potter is the story’s villain not because he is a slumlord, but because he calls the people Bailey helps “garlic eaters” (http://historynewsnetwork.org/article/1846). What makes Potter evil, in other words, isn’t his “cold monetary self-interest,” but because he’s “bigoted”: to this historian, Capra’s film isn’t the heartwarming story of how Americans banded together to stop a minority (rich people) from wrecking things, but instead the horrifying tragedy of how Americans banded together to stop a minority (black people) from wrecking things. Unfortunately, there’s two problems with that view—problems that can be summarized by referring to the program for a football game that took place five years before the release of Capra’s classic: the Army-Navy game of 29 November, 1941.

Played at Philadelphia’s Franklin Memorial Stadium (once home of the NFL’s Philadelphia Eagles and still the home of the Penn Relays, one of track and field’s premier events), Navy won the contest 14-6; according to Vintage College Football Programs & Collectibles (collectable.wordpress.com [sic]), the program for that game contains 212 pages. On page 180 of that program there is a remarkable photograph. It is of the USS Arizona, the second and last of the American “Pennsylvania” class of super-dreadnought battleships—a ship meant to be, according to the New York Times of 13 July 1913, “the world’s biggest and most powerful, both offensively and defensively, superdreadnought ever constructed.” The last line of the photograph’s caption reads thusly:

It is significant that despite the claims of air enthusiasts, no battleship has yet been sunk by bombs.”

Slightly more than a week later, of course, on a clear bright Sunday morning just after 8:06 Hawaiian time, the hull of the great ship would rest on the bottom of Pearl Harbor, along with the bodies of nearly 1200 of her crew—struck down by the “air enthusiasts” of the Empire of the Sun. The lesson taught that morning, by aircraft directed by former Harvard student Isoroku Yamamoto, was a simple one: that “a saturation attack by huge numbers of low-value attackers”—as Pando Daily’s “War Nerd” columnist, Gary Brecher, has referred to this type of attack—can bring down nearly any target, no matter how powerful (http://exiledonline.com/the-war-nerd-this-is-how-the-carriers-will-die/all/1/). (A lesson that the U.S. Navy has received more than once: in 2002, for instance, when during the wargame “Millennium Challenge 2002” Marine Corps Lieutenant General Paul K. Riper (fictionally) sent 16 ships to the bottom of the Persian Gulf with the creative use of, essentially, a bunch of cruise missiles and several dozen speedboats loaded with cans of gasoline driven by gentlemen with, shall we say, a cavalier approach to mortality.) It’s the lesson that the cheap and shoddy can overcome quality—or in other words that, as the song says, the bigger they come, the harder they fall.

It’s a lesson that applies to more than merely the physical plane, as the Irish satirist Jonathan Swift knew: “Falsehood flies, and the Truth comes limping after,” the author of Gulliver’s Travels wrote in 1710. What Swift refers to is how saturation attacks can work on the intellectual as well as physical plane—as Emory University’s Mark Bauerlein (who, unfortunately for the warmth of my argument’s reception, endorsed Donald Trump in this past election) argued, in Partisan Review in 2001, American academia has over the past several generations essentially become flooded with the mental equivalents of Al Qaeda speedboats. “Clear-sighted professors,” Bauerlein wrote then, understanding the conditions of academic research, “avoid empirical methods, aware that it takes too much time to verify propositions about culture, to corroborate facts with multiple sources, to consult primary documents, and to compile evidence adequate to inductive conclusions” (http://www.bu.edu/partisanreview/books/PR2001V68N2/HTML/files/assets/basic-html/index.html#226). Discussing It’s A Wonderful Life in terms of, say, the economic differences between banks like the one owned by Potter and the savings-and-loan run by George Bailey—and the political consequences therein—is, in other words, hugely expensive in terms of time and effort invested: it’s much more profitable to discuss the film in terms of its hidden racism. By “profitable,” in other words, I mean not merely because it’s intrinsically easier, but also because such a claim is much more likely to upset people, and thus attract attention to its author: the crass stunt once called épater le bourgeois.

The current reward system of the humanities, in other words, favors those philosopher Isaiah Berlin called “foxes” (who know a great many things) rather than “hedgehogs” (who know one important thing). To the present defenders of the humanities, of course, such is the point: that’s the pro-speedboat argument noted feminist literary scholar Jane Tompkins made so long ago as 1981, in her essay “Sentimental Power: Uncle Tom’s Cabin and the Politics of American Literary History.” There, Tompkins suggested that the “political and economic measures”—i.e., the battleships of American political discourse—“that constitute effective action for us” are, in reality, merely “superficial”: instead, what’s necessary are “not specific alterations in the current political and economic arrangements, but rather a change of heart” (http://engl651-jackson.wikispaces.umb.edu/file/view/Sentimental+Power.pdf). To those who think like Tompkins—or apparently, Nobile—discussing It’s A Wonderful Life in terms of economics is to have missed the point entirely: what matters, according to them, isn’t the dreadnought clash of, for example, the unit banking system of the antebellum North (speedboats) versus the branch banking system of the antebellum South (battleships) within the sea of the American economy. (A contest that, incidentally, not only did branch banking largely win in 1994, during Bill Clinton’s administration, but a victory that in turn—because it helped to create the enormous “too big to fail” interstate banks of today—arguably played no small role in the crash of 2008). Instead, what’s important is the seemingly-minor attack of a community college teacher upon a Titanic of American culture. Or, to put the point in terms popularized by Silicon Valley: the sheer BS quality of Vincent Nobile’s argument about It’s A Wonderful Life isn’t a bug—it’s a feature.

There is, however, one problem with such tactics—the same problem described by Rear Admiral Chuichi (“King Kong”) Hara of the Imperial Japanese Navy after the Japanese surrender in September 1945: “We won a great tactical victory at Pearl Harbor—and thereby lost the war.” Although, as the late American philosopher Richard Rorty commented before his death in his Achieving Our Country: Leftist Thought in Twentieth Century America, “[l]eftists in the academy” have, in collaboration with “the Right,” succeeded in “making cultural issues central to public debate,” that hasn’t necessarily resulted in a victory for leftists, or even liberals (https://www.amazon.com/Achieving-Our-Country-Leftist-Twentieth-Century/dp/0674003128). Indeed, there’s some reason to suppose that, by discouraging certain forms of thought within left-leaning circles, academic leftists in the humanities have obscured what Elizabeth Drew, in the New York Review of Books, has called “unglamorous structural questions” in a fashion ultimately detrimental not merely to minority communities, but ultimately all Americans (http://www.nybooks.com/articles/2016/08/18/american-democracy-betrayed/).

What Drew was referring to this past August was such matters as how—in the wake of the 2010 Census and the redistricting it entailed in every state in the Union—the Democrats ended up, in the 2012 election cycle, winning the popular vote for Congress “by 1.2 per cent, but still remained in the minority, with two hundred and one seats to the G.O.P.’s two hundred and thirty-four.” In other words, Democratic candidates for the House of Representatives got, as Katie Sanders noted in Politifact in 2013, “50.59 percent of the two-party vote” that November, but “won just 46.21 percent of seats”: only “the second time in 70 years that a party won the majority of the vote but didn’t win a majority of the House seats” (http://www.politifact.com/truth-o-meter/statements/2013/feb/19/steny-hoyer/steny-hoyer-house-democrats-won-majority-2012-popu/). The Republican advantage didn’t end there: as Rob Richie reported for The Nation in 2014, in that year’s congressional races Republicans won “about 52 percent of votes”—but ended “up with 57 percent of seats” (https://www.thenation.com/article/republicans-only-got-52-percent-vote-house-races/). And this year, the numbers suggest, the Republicans received less than half the popular vote—but will end up with fifty-five percent (241) of the total seats (435). These losses, Drew suggests, are ultimately due to the fact that “the Democrats simply weren’t as interested in such dry and detailed stuff as state legislatures and redistricting”—or, to put it less delicately, because potentially-Democratic schemers have been put to work constructing re-readings of old movies instead of building arguments that are actually politically useful.

To put this even less delicately, many people on the liberal or left-wing side of the political aisle have, for the past several generations, spent their college educations learning, as Mark Bauerlein wrote back in 2001, how to “scoff[…] at empirical notions, chastising them as ‘näive positivism.’” At the same time, a tiny minority among them—those destined to “relax their scruples and select a critical practice that fosters their own professional survival”—have learned, and are learning, to swim the dark seas of academia, taught by their masters how to live by feeding upon the minds of essentially defenseless undergraduates. The lucky ones, like Vince Nobile, manage—by the right mix of bowing and scraping—to land some kind of job security at some far-flung outpost of academia’s empire, where they make a living entertaining the yokels; the less-successful, of course, write deeply ironic blogs.

Be that as it may, while there isn’t necessarily a connection between the humanistic academy’s flight from what Bauerlein calls “the canons of logic” and the fact that it was so easy—as John Cassidy of The New Yorker observed after this past presidential election—for so many in the American media and elsewhere “to dismiss the other outcome [i.e., Trump’s victory] as a live possibility” before the election, Cassidy at least ascribed the ease with which so many predicted a Clinton victory then to the fact that many “haven’t been schooled in how to think in probabilistic terms” (http://www.newyorker.com/news/john-cassidy/media-culpa-the-press-and-the-election-result). That lack of education, which extends from the impact of mathematics upon elections to the philosophical basis for holding elections at all (which extends far beyond the usual seventeenth-century suspects rounded up in even the most erudite of college classes to medieval thinkers like Nicholas of Cusa, who argued in 1434’s Catholic Concordance that the “greater the agreement, the more infallible the judgment”—or in other words that speedboats are more trustworthy than battleships), most assuredly has had political consequences (http://www.cambridge.org/us/academic/subjects/politics-international-relations/texts-political-thought/nicholas-cusa-catholic-concordance?format=PB&isbn=9780521567732). While the ever-more abstruse academic turf wars between the sciences and the humanities might be good for the ever-dwindling numbers of tenured college professors, in other words, it’s arguably disastrous, not only for Democrats and the populations they serve, but for the country as a whole. Although Clarence, angel second class, says to George Bailey, “we don’t use money in Heaven”—suggesting the way in which American academics swear off knowledge of the sciences upon entering their secular priesthood—George replies, “it comes in real handy down here, bub.” What It’s A Wonderful Life wants to tell us is that a nation whose leadership balances so precariously upon such a narrow educational foundation is, no matter what the program says, as vulnerable as a battleship on a bright Pacific morning.

Or a skyscraper, on a cloudless September one.

Don Thumb

Then there was the educated Texan from Texas who looked like someone in Technicolor and felt, patriotically, that people of means—decent folk—should be given more votes than drifters, whores, criminals, degenerates, atheists, and indecent folk—people without means.
Joseph Heller. Catch-22. (1961).

 

“Odd arrangements and funny solutions,” the famed biologist Stephen Jay Gould once wrote about the panda’s thumb, “are the proof of evolution—paths that a sensible God would never tread but that a natural process, constrained by history, follows perforce.” The panda’s thumb, that is, is not really a thumb: it is an adaptation of another bone (the radial sesamoid) in the animal’s paw; Gould’s point is that the bamboo-eater’s thumb is not “a beautiful machine,” i.e. not the work of “an ideal engineer.” Hence, it must be the product of an historical process—a thought that occurred to me once again when I was asked recently by one of my readers (I have some!) whether it’s really true, as law professor Paul Finkelman has suggested for decades in law review articles like “The Proslavery Origins of the Electoral College,” that the “connection between slavery and the [electoral] college was deliberate.” One way to answer the question, of course, is to pour through (as Finkelman has very admirably done) the records of the Constitutional Convention of 1787: the notes of James Madison, for example, or the very complete documents collected by Yale historian Max Farrand at the beginning of the twentieth century. Another way, however, is to do as Gould suggests, and think about the “fit” between the design of an instrument and the purpose it is meant to achieve. Or in other words, to ask why the Law of Large Numbers suggests Donald Trump is like the 1984 Kansas City Royals.

The 1984 Kansas City Royals, for those who aren’t aware, are well-known in baseball nerd circles for having won the American League West division despite being—as famous sabermetrician Bill James, founder of the application of statistical methods to baseball, once wrote—“the first team in baseball history to win a championship of any stripe while allowing more runs (684) than they scored (673).” “From the beginnings of major league baseball just after the civil war through 1958,” James observes, no team ever managed such a thing. Why? Well, it does seem readily apparent that scoring more runs than one’s opponent is a key component to winning baseball games, and winning baseball games is a key component to winning championships, so in that sense it ought to be obvious that there shouldn’t be many winning teams that failed to score more runs than their opponents. Yet on the other hand, it also seems possible to imagine a particular sort of baseball team winning a lot of one-run games, but occasionally giving up blow-out losses—and yet as James points out, no such team succeeded before 1959.

Even the “Hitless Wonders,” the 1906 Chicago White Sox, scored more runs than their opponents  despite hitting (according to This Great Game: The Online Book of Baseball) “a grand total of seven home runs on the entire season” while simultaneously putting up the American League’s “worst batting average (.230).” The low-offense South Side team is seemingly made to order for the purposes of this discussion because they won the World Series that year (over the formidable Chicago Cubs)—yet even this seemingly-hapless team scored 570 runs to their opponents’ 460, according to Baseball Reference. (A phenomenon most attribute to the South Siders’ pitching and fielding: that is, although they didn’t score a lot of runs, they were really good at preventing their opponents’ from scoring a lot of runs.) Hence, even in the pre-Babe Ruth “dead ball” era, when baseball teams routinely employed “small ball” strategies designed to produce one-run wins as opposed to Ruth’s “big ball” attack, there weren’t any teams that won despite scoring fewer runs than their opponents’.

After 1958, however, there were a few teams that approached that margin: the 1959 Dodgers, freshly moved to Los Angeles, scored only 705 runs to their opponents’ 670, while the 1961 Cincinnati Reds scored 710 to their opponents 653, and the 1964 St. Louis Cardinals scored 715 runs to their opponents’ 652. Each of these teams were different than most other major league teams: the ’59 Dodgers played in the Los Angeles Coliseum, a venue built for the 1932 Olympics, not baseball; its cavernous power alleys were where home runs went to die, while its enormous foul ball areas ended many at-bats that would have continued in other stadiums. (The Coliseum, that is, was a time machine to the “deadball” era.) The 1961 Reds had Frank Robinson and virtually no other offense until the Queen City’s nine was marginally upgraded through a midseason trade. Finally, the 1964 Cardinals team had Bob Gibson (please direct yourself to the history of Bob Gibson’s career immediately if you are unfamiliar with him), and second they played in the first year after major league baseball’s Rules Committee redefined the strike zone to be just slightly larger—a change that had the effect of dropping home run totals by ten percent and both batting average and runs scored by twelve percent. In The New Historical Baseball Abstract, Bill James calls the 1960s the “second deadball era”; the 1964 Cardinals did not score a lot of runs, but then neither did anyone else.

Each of these teams was composed of unlikely sets of pieces: the Coliseum was a weird place to play baseball, the Rule Committee was a small number of men who probably did not understand the effects of their decision, and Bob Gibson was Bob Gibson. And even then, these teams all managed to score more runs than their opponents, even if the margin was small. (By comparison, the all-time run differential record is held by Joe DiMaggio’s 1939 New York Yankees, who outscored their opponents by 411 runs: 967 to 556, a ratio may stand until the end of time.) Furthermore, the 1960 Dodgers finished in fourth place, the 1962 Reds finished in third, and the 1965 Cards finished seventh: these were teams, in short, that had success for a single season, but didn’t follow up. Without going very deeply into the details then, suffice it to say that run differential is—as Sean Forman noted in the The New York Times in 2011—“a better predictor of future win-loss percentage than a team’s actual win-loss percentage.” Run differential is a way to “smooth out” the effects of chance in a fashion that the “lumpiness” of win-loss percentage doesn’t.

That’s also, as it happens, just what the Law of Large Numbers does: first noted by mathematician Jacob Bernoulli in his Ars Conjectandi of 1713, that law holds that “the more … observations are taken into account, the less is the danger of straying from the goal.” It’s the principle that is the basis of the insurance industry: according to Caltech physicist Leonard Mlodinow, it’s the notion that while “[i]ndividual life spans—and lives—are unpredictable, when data are collected from groups and analyzed en masse, regular patterns emerge.” Or for that matter, the law is also why it’s very hard to go bankrupt—which Donald Trump, as it so happens, has—when running a casino: as Nicholas Taleb commented in The Black Swan: The Impact of the Highly Improbable, all it takes to run a successful casino is to refuse to allow “one gambler to make a massive bet,” and instead “have plenty of gamblers make series of bets of limited size.” More bets equals more “observations,” and the more observations the more likely it is that all those bets will converge toward the expected result. In other words, one coin toss might be heads or might be tails—but the more times the coin is thrown, the more likely it is that there will be an equal number of both heads and tails.

How this concerns Donald Trump is that, as has been noted, although the president-elect did win the election, he did not win more votes than the Democratic candidate, Hillary Clinton. (As of this writing, those totals now stand at 62,391,335 votes for Clinton to Trump’s 61,125,956.) The reason that Clinton did not win the election is because American presidential elections are not won by collecting more votes in the wider electorate, but rather through winning in that peculiarly American institution, the Electoral College: an institution in which, as Will Hively remarked remarkably presciently in a Discover article in 1996, a “popular-vote loser in the big national contest can still win by scoring more points in the smaller electoral college.” Despite how weird that bizarre sort of result actually is, however, according to some that’s just what makes the Electoral College worth keeping.

Hively was covering that story in 1996: his Discovery story was about how, in the pages of the journal Public Choice that year, mathematician Alan Natapoff tried to argue that the “same logic that governs our electoral system … also applies to many sports”—for example, baseball’s World Series. In order “to become [World Series] champion,” Natapoff noticed, a “team must win the most games”—not score the most runs. In the 1960 World Series, the mathematician wrote, the New York Yankees “scored more than twice as many total runs as the Pittsburgh Pirates, 55 to 27”—but the Yankees lost game 7, and thus the series. “Runs must be grouped in a way that wins games,” Natapoff thought, “just as popular votes must be grouped in a way that wins states.” That is, the Electoral College forces candidates to “have broad appeal across the whole nation,” instead of playing “strongly on a single issue to isolated blocs of voters.” It’s a theory that might seem, on its face, to have a certain plausibility: by constructing the Electoral College, the delegates to the constitutional convention of 1787 prevented future candidates from winning by appealing to a single, but large, constituency.

Yet, recall Stephen Jay Gould’s remark about the panda’s thumb, which suggests that we can examine just how well a given object fulfills its purpose: in this case, Natapoff is arguing that, because the design of the World Series “fits” the purpose of identifying the best team in baseball, so too does the Electoral College “fit” the purpose of identifying the best presidential candidate. Natapoff’s argument concerning the Electoral College presumes, in other words, that the task of baseball’s playoff system is to identify the best team in baseball, and hence it ought to work for identifying the best president. But the Law of Large Numbers suggests that the first task of any process that purports to identify value is that it should eliminate, or at least significantly reduce, the effects of chance: whatever one thinks about the World Series, presumably presidents shouldn’t be the result of accident. And the World Series simply does not do that.

“That there is”—as Nate Silver and Dayn Perry wrote in their ESPN.com piece, “Why Don’t the A’s Win In October?” (collected in Jonah Keri and James Click’s Baseball Between the Numbers: Why Everything You Know About the Game Is Wrong)—“a great deal of luck involved in the playoffs is an incontrovertible mathematical fact.” It’s a point that was


argued so early in baseball’s history as 1904, when the New York Giants refused to split the gate receipts evenly with what they considered to be an upstart American League team (Cf. “Striking Out” https://djlane.wordpress.com/2016/07/31/striking-out/.). As Caltech physicist Leonard Mlodinow has observed, if the World Series were designed—by an “ideal engineer,” say—to make sure that one team was the better team, it would have to be 23 games long if one team were significantly better than the other, and 269 games long if the two teams were evenly matched—that is, nearly as long as two full seasons. In fact, since it may even be argued that baseball, by increasingly relying on a playoff system instead of the regular season standings, is increasing, not decreasing, the role of chance in the outcome of its championship process: whereas prior to 1969, the two teams meeting in the World Series were the victors of a paradigmatic Law of Large Numbers system—the regular season—now many more teams enter the playoffs, and do so by multiple routes. Chance is playing an increasing role in determining baseball’s champions: in James’ list of sixteen championship-winning teams that had a run differential of less than 1.100: 1, all of the teams, except the ones I have already mentioned, are from 1969 or after. Hence, from a mathematical perspective the World Series cannot be seriously argued to eliminate, or even effectively reduce, the element of chance—from which it can be reasoned, as Gould says about the panda’s thumb, that the purpose of the World Series is not to identify the best baseball team.

Natapoff’s argument, in other words, has things exactly backwards: rather than showing just how rational the Electoral College is, the comparison to baseball demonstrates just how irrational it is—how vulnerable it is to chance. In the light of Gould’s argument about the panda’s thumb, which suggests that a lack of “fit” between the optimal solution (the human thumb) to a problem and the actual solution (the panda’s thumb) implies the presence of “history,” that would then intimate that the Electoral College is either the result of a lack of understanding of the mathematics of chance with regards to elections—or that the American system for electing presidents was not designed for the purpose that it purports to serve. As I will demonstrate, despite the rudimentary development of the mathematics of probability at the time at least a few—and these, some of the most important—of the delegates to the Philadelphia convention in 1787 were aware of those mathematical realities. That fact suggests, I would say, that Paul Finkelman’s arguments concerning the purpose of the Electoral College are worth much more attention than they have heretofore received: Finkelman may or may not be correct that the purpose of the Electoral College was to support slavery—but what is indisputable is that it was not designed for the purpose of eliminating chance in the election of American presidents.

Consider, for example, that although he was not present at the meeting in Philadelphia, Thomas Jefferson possessed not only a number of works on the then-nascent study of probability, but particularly a copy of the very first textbook to expound on Bernoulli’s notion of the Law of Large Numbers: 1718’s The Doctrine of Chances, or, A Method of Calculating the Probability of Events in Play, by Abraham de Moivre. Jefferson also had social and intellectual connections to the noted French mathematician, the Marquis de Condorcet—a man who, according to Iain McLean of the University of Warwick and Arnold Urken of the Stevens Institute of Technology, applied “techniques found in Jacob Bernoulli’s Ars Conjectandi” to “the logical relationship between voting procedures and collective outcomes.” Jefferson in turn (McLean and Urken inform us) “sent [James] Madison some of Condorcet’s political pamphlets in 1788-9”—a connection that would only have reaffirmed a connection already established by the Italian Philip Mazzei, who sent a Madison a copy of some of Condorcet’s work in 1786: “so that it was, or may have been, on Madison’s desk while he was writing the Federalist Papers.” And while none of that implies that Madison knew of the marquis prior to coming to Philadelphia in 1787, before even meeting Jefferson when the Virginian came to France to be the American minister, the marquis had already become a close friend, for years, to another man who would become a delegate to the Philadelphia meeting: Benjamin Franklin. Although not all of the convention attendees, in short, may have been aware of the relationship between probability and elections, at least some were—and arguably, they were the most intellectually formidable ones, the men most likely to notice that the design of the Electoral College is in direct conflict with the Law of Large Numbers.

In particular, they would have been aware of the marquis’ most famous contribution to social thought: Condorcet’s “Jury Theorem,” in which—as Norman Schofield once observed in the pages of Social Choice Welfare—the Frenchman proved that, assuming “that the ‘typical’ voter has a better than even chance of choosing the ‘correct’ outcome … the electorate would, using the majority rule, do better than an average voter.” In fact, Condorcet demonstrated mathematically—using Bernoulli’s methods in a book entitled Essay on the Application of Analysis to the Probability of Majority Decisions (significantly published in 1785, two years before the Philadelphia meeting)—that adding more voters made a correct choice more likely, just as (according to the Law of Large Numbers) adding more games makes it more likely that the eventual World Series winner is the better team. Franklin at the least then, and perhaps Madison next most-likely, could not but have been aware of the possible mathematical dangers an Electoral College could create: they must have known that the least-chancy way of selecting a leader—that is, the product of the design of an infallible engineer—would be a direct popular vote. And while it cannot be conclusively demonstrated that these men were thinking specifically of Condorcet’s theories at Philadelphia, it is certainly more than suggestive that both Franklin and Madison thought that a direct popular vote was the best way to elect a president.

When James Madison came to the floor of Independence Hall to speak to the convention about the election of presidents for instance, he insisted that “popular election was better” than an Electoral College, as David O. Stewart writes in his The Summer of 1787: The Men Who Invented the Constitution. Meanwhile, it was James Wilson of Philadelphia—so close to Franklin, historian Lawrence Goldstone reports, that the infirm Franklin chose Wilson to read his addresses to the convention—who originally proposed direct popular election of the president: “Experience,” the Scottish-born Philadelphian said, “shewed [sic] that an election of the first magistrate by the people at large, was both a convenient & successful mode.” In fact, as William Ewald of the University of Pennsylvania has pointed out, “Wilson almost alone among the delegates advocated not only the popular election of the President, but the direct popular election of the Senate, and indeed a consistent application of the principle of ‘one man, one vote.’” (Wilson’s positions were far ahead of their time: in the case of the Senate, Wilson’s proposal would not be realized until the passage of the Seventeenth Amendment in 1913, and his stance in favor of the principle of “one man, one vote” would not be enunciated as part of American law until the Reynolds v. Sims line of cases decided by the Earl Warren-led U.S. Supreme Court in the early 1960s.) To Wilson, the “majority of people wherever found” should govern “in all questions”—a statement that is virtually identical to Condorcet’s mathematically-influenced argument.

What these men thought, in other words, was that an electoral system that was designed to choose the best leader of a nation would proceed on the basis of a direct national popular vote: some of them, particularly Madison, may even have been aware of the mathematical reasons for supposing that a direct national popular vote was how an American presidential election would be designed if it were the product of what Stephen Jay Gould calls an “ideal engineer.” Just as an ideal (but nonexistent) World Series would be at least 23, and possibly so long as 269 games—in order to rule out chance—the ideal election to the presidency would include as many eligible voters as possible: the more voters, Condorcet would say, the more likely those voters would be to get it right. Yet just as with the actual, as opposed to ideal, World Series, there is a mismatch between the Electoral College’s proclaimed purpose and its actual purpose: a mismatch that suggests researchers ought to look for the traces of history within it.

Hence, although it’s possible to investigate Paul Finkelman’s claims regarding the origins of the Electoral College by, say, trawling through the volumes of the notes taken at the Constitutional Convention, it’s also possible simply to think through the structure of the Constitution itself in the same fashion that Stephen Jay Gould thinks about, say, the structure of frog skeletons: in terms of their relation to the purpose they serve. In this case, there is a kind of mathematical standard to which the Electoral College can be compared: a comparison that doesn’t necessarily imply that the Constitution was created simply and only to protect slavery, as Finkelman says—but does suggest that Finkelman is right to think that there is something in need of explanation. Contra Natapoff, the similarity between the Electoral College and the World Series does not suggest that the American way of electing a head of state is designed to produce the best possible leader, but instead that—like the World Series—it was designed with some other goal in mind. The Electoral College may or may not be the creation of an ideal craftsman, but it certainly isn’t a “beautiful machine”; after electing the political version of the 1984 Kansas City Royals—who, by the way, were swept by Detroit in the first round—to the highest office in the land, maybe the American people should stop treating it that way.

Lex Majoris

The first principle of republicanism is that the lex majoris partis is the fundamental law of every society of individuals of equal rights; to consider the will of the society enounced by the majority of a single vote, as sacred as if unanimous, is the first of all lessons in importance, yet the last which is thoroughly learnt. This law once disregarded, there is no other but that of force, which ends necessarily in military despotism.
—Thomas Jefferson. Letter to Baron von Humboldt. 13 June 1817.

Since Hillary Clinton lost the 2016 American presidential election, many of her supporters have been quick to cry “racism” on the part of voters for her opponent, Donald Trump. According to Vox’s Jenée Desmond-Harris, for instance, Trump won the election “not despite but because he expressed unfiltered disdain toward racial and religious minorities in the country.” Aside from being the easier interpretation, because it allows Clinton voters to ignore the role their own economic choices may have played in the broad support Trump received throughout the country, such accusations are counterproductive even on their own terms because—only seemingly paradoxically—they reinforce many of the supports racism still receives in the United States: above all, because they weaken the intellectual argument for a national direct election for the presidency. By shouting “racism,” in other words, Hillary Clinton’s supporters may end up helping to continue racism’s institutional support.

That institutional support begins with the method by which Americans elect their president: the Electoral College—a method that, as many have noted, is not used in any other industrialized democracy. Although many scholars and others have advanced arguments for the existence of the college through the centuries, most of these “explanations” are, in fact, intellectually incoherent: while the most common of the traditional “explanations” concerns the differences between the “large states” and the “small,” for instance, in the actual United States—as James Madison, known as the “Father of the Constitution,” noted at the time—there had not then, and has not ever been since, a situation in American history that involved a conflict between larger-population and smaller-population states. Meanwhile, the other “explanations” for the Electoral College do not even rise to this level of incoherence.

In reality there is only one explanation for the existence of the college, and that explanation has been most forcefully and clearly made by law professor Paul Finkelman, now serving as a Senior Fellow at the University of Pennsylvania after spending much of his career at obscure law schools like the University of Tulsa College of Law, the Cleveland-Marshall College of Law, and the Albany Law School. As Finkelman has been arguing for decades (his first papers on the subject were written in the 1980s), the Electoral College was originally invented by the delegates to the Constitutional Convention of 1787 in order to protect slavery. That such was the purpose of the College can be known, most obviously, because the delegates to the convention said so.

When the means of electing a president were first debated, it’s important to remember that the convention had already decided, for the purposes of representation in the newly-created House of Representatives, to count black slaves by the means of the infamous three-fifths ratio. That ratio, in turn, had its effect when discussing the means of electing a president: delegates like James Madison argued, as Finkelman notes, that the existence of such a college—whose composition would be based on each state’s representation in the House of Representatives—would “guarantee that the nonvoting slaves could nevertheless influence the presidential election.” Or as Hugh Williamson, a delegate from North Carolina, observed during the convention, if American presidents were elected by direct national vote the South would be shut out of electing a national executive because “her slaves will have no suffrage”—that is, because in a direct vote all that would matter is the number of voters, the Southern states would lose the advantage the three-fifths ratio gave them in the House. Hence, the existence of the Electoral College is directly tied to the prior decision to grant Southern slave states an advantage in Congress, and so the Electoral College is another in a string of institutional decisions made by convention delegates to protect domestic slavery.

Yet, assuming that Finkelman’s case for the racism of the Electoral College is true, how can decrying the racism of the American voter somehow inflict harm on the case for abolishing the Electoral College? The answer goes back to the very justifications of, not only presidential elections, but elections in general—the gradual discovery, during the eighteenth century Enlightenment, of what is today known as the Law of Large Numbers.

Putting the law in capital letters, I admit, tends to mystify it, but anyone who buys insurance already understands the substance of the concept. As New Yorker writer Malcolm Gladwell once explained insurance, “the safest and most efficient way to provide insurance” is “to spread the costs and risks of benefits over the biggest and most diverse group possible.” In other words, the more people participating in an insurance plan, the greater the possibility that the plan’s members will be protected. The Law of Large Numbers explains why that is.

That reason is the same as the reason that, as Peter Bernstein remarks in Against the Gods: The Remarkable Story of Risk, if we toss a coin enough times that “will correspondingly increase the probability that the ratio of heads thrown to total throws” will decrease. Or, the reason that—as physicist Leonard Mlodinow has pointed out—in order really to tell which baseball team is better than another a World Series would have to be at least 23 games long (if one team were much better than the other), and possibly as long as 269 games (between two closely-matched opponents). Only by playing so many games can random chance be confidently excluded: as Carl Bialik of FiveThirtyEight once pointed out, usually “in sports, the longer the contest, the greater the chance that the favorite prevails.” Or, as Israeli psychologists Daniel Kahneman and Amos Tversky put the point in 1971, “the law of large numbers guarantees that very large samples will indeed be representative”: it’s what scientists rely upon to know that, if they have performed enough experiments or poured over enough data, they know enough to exclude idiosyncratic results. The Law of Large Numbers asserts, in short, that the more times we repeat something, the closer we will approach its true value.

It’s for just that reason that many have noted the connection between science and democratic government: “Science and democracy are powerful partners,” as the website for the Union of Concerned Scientists has put it. What makes these two objects such “powerful” partners is that the Law of Large Numbers is what underlies the act of holding elections: as James Surowiecki put the point in his book, The Wisdom of Crowds, the theory of democracy is that “the larger the group, the more reliable its judgment will be.” Just as scientists think that, by replicating an experiment, they can more readily trust in its results, so too does a democratic government implicitly think that, by including more people in the decision-making process, the government can the more readily arrive at the “correct” solution: as James Madison put it in The Federalist No. 10, if you “take in a greater variety of parties and interests,” then “you make it less probable that a majority of the whole will have a common motive for invading the rights of other citizens.” Without such a belief, after all, there would be no reason not to trust, say, a ruling caste to make decisions for society—or even a single, perhaps orange-toned, individual. Without some concept of the Law of Large Numbers—some belief that increasing the numbers of trials, or increasing the number of inputs, will make for better results—there is no reason for democratic government at all.

That’s why, when people criticize the Electoral College, they are implicitly invoking the Law of Large Numbers. The Electoral College divides the pool of American voters into fifty smaller pools, but a national popular vote would collect all Americans into a single lump—a point that some defenders of the College sometimes seek to make into a virtue, instead of the vice it is. In the wake of the 2000 election, for example, Senator Mitch McConnell wrote that the “Electoral College served to center the post-election battles in Florida,” preventing the “vote recounts and court battles in nearly every state of the Union” that, McConnell assures us, would have occurred in the college’s absence. But as Timothy Noah pointed out in The New Republic in 2012, what McConnell’s argument “fails to realize is that when you’re assembling one big count rather than a lot of little ones it’s a lot less clear what’s to be gained from rigging any of the little ones.” If what matters is the popular vote, what happens in any one location doesn’t matter so much; hence, stealing votes in downstate Illinois won’t allow you to steal the entire state—just as, with enough samples or experiments run, the fact that the lab assistant was drowsy at the time she recorded one set of results won’t matter so much. Or why deliberately losing a single game in July hardly matters so much as tanking a game of the World Series.

Put in such a way, it’s hard to see how anyone without a vested stake in the construction of the present system could defend the Electoral College—yet, as I suspect we are about to see, the very people now ascribing Donald Trump’s victory to the racism of the American voter will soon be doing just that. The reason will be precisely the same reason that such advocates want to blame racism, rather than the ongoing thievery of economic elites, for the rejection of Clinton: because racism is a “cultural” phenomenon, and most left-wing critics of the United States now obtain credentials in “cultural,” rather than scientific, disciplines.

If, in other words, Donald Trump’s victory was due to a complex series of renegotiations of the global contract between capital and labor, then that would require experts in economic and other, similar, disciplines to explain it; if his victory was due to racism, however—racism being considered a cultural phenomenon—then that will call forth experts in “cultural” fields. Because those with “liberal” or “leftist” political leanings now tend to gather in “cultural” fields, those with those political leanings will (indeed, must) now attempt to shift the battleground towards their areas of expertise. That shift, I would wager, will in turn lead those who argue for “cultural” explanations for the rise of Trump against arguments for the elimination of the Electoral College.

The reason is not difficult to understand: it isn’t too much to say, in fact, that one way to define the study of the humanities is to say it comprises the disciplines that largely ignore, or even oppose, the Law of Large Numbers both as a practical matter and as a philosophic one. As literary scholar Franco Moretti, now of Stanford, observed in his Atlas of the European Novel, 1800-1900, just as “silver fork novels”—a genre published in England between the 1820s and the 1840s—do not “show ‘London,’ but only a small, monochrome portion of it,” so too does the average student of literature not really study her ostensible subject matter. “I work on west European narrative between 1790 and 1930, and already feel like a charlatan outside of Britain and France,” Moretti confesses in an essay entitled “Distant Reading”—and even then, he only works “on its canonical fraction, which is not even 1 percent of published literature.” As Joshua Rothman put the point in a New Yorker profile of Moretti a few years ago, Moretti instead insists that “if you really want to understand literature, you can’t just read a few books or poems over and over,” but instead “you have to work with hundreds or even thousands of texts at a time”—that is, he insists on the significance of the Law of Large Numbers in his field, an insistence whose very novelty demonstrates how literary study is a field that has historically resisted precisely that recognition.

In order to proceed, in other words, disciplines like literary study or art history—or even history itself—must argue for the representativeness of a given body of work: usually termed, at least in literary study, “the Canon.” Such disciplines are already, simply by their very nature, committed to the idea that it is not necessary to read all of what Moretti says is the “thirty thousand nineteenth-century British novels out there” in order to arrive at conclusions about the nineteenth-century British novel: in the first place, “no one really knows” how many there really are (there could easily be twice as many), and in the second “no one has read them [all], [and] no one ever will.” In order to get off the ground, such disciplines must necessarily deny the Law of Large Numbers: as Moretti says, “you invest so much in individual texts only if you think that very few of them really matter”—a belief with an obvious political corollary. Rejection of the Law of Large Numbers is thusly, as Moretti also observes, “an unconscious and invisible premiss” for most who study such fields—which is to say that although students of the humanities often make claims for the political utility of their work, they sometimes forget that the enabling presuppositions of their fields are inherently those of the pre-Enlightenment ancien régime.

Perhaps that’s why—as Joe Pinsker observed in a fascinating, but short, article for The Atlantic several years ago—studies of college students find that those “from lower-income families tend toward ‘useful’ majors, such as computer science, math, and physics,” while students “whose parents make more money flock to history, English, and the performing arts”: the baseline assumptions of those disciplines are, no matter the particular predilections of a given instructor, essentially aristocratic, not democratic. To put it most baldly, the disciplines of the humanities must reject the premise of the Law of Large Numbers, which says that as more examples are added, the closer we approach to the truth—a point that can be directly witnessed when, for instance, English professor Michael Bérubé of Pennsylvania State University observes that the “humanists at [his] end of the [academic] hallway roundly dismissed” Harvard biologist E.O. Wilson’s book, Consilience: The Unity of Knowledge for arguing that “all human knowledge can and eventually will be unified under the rubric of the natural sciences.” Rejecting the Law of Large Numbers is foundational to the very operation of the humanities: without making that rejection, they cannot exist.

In recent decades, of course, presumably Franco Moretti has not been the only professor of the humanities to realize that their disciplines stood on a collision course with the Law of Large Numbers—it may perhaps explain why disciplines like literature and others have, for years, been actively recruiting among members of minority groups. The institutional motivations of such hiring, in other words, ought to be readily apparent: by making such hires, departments of the humanities could insulate themselves from charges from the political left—while at the same time continuing the practices that, without such cover, might have appeared increasingly anachronistic in a democratic age. Minority hiring, that is, may not be so politically “progressive” as its defenders sometimes argue: it may, in fact, have prevented the intellectual reforms within the humanities urged by people like Franco Moretti for a generation or more. Of course, by joining such departments, members of minority groups also may have, consciously or not, tied their own fortunes to a philosophic rejection of concepts like the Law of Large Numbers—as African-American sportswriter Michael Wilbon, of ESPN fame, wrote this past May, black people supposedly have some kind of allergy to statistical analysis: “in ‘BlackWorld,’” Wilbon solemnly intoned, “never is heard an advanced analytical word.” I suspect then that many who claim to be on the political left will soon come out to defend the Electoral College. If that happens, then in one last cruel historical irony the final defenders of American slavery may end up being precisely those slavery meant to oppress.

Stormy Weather

They can see no reasons …
—“I Don’t Like Mondays” 
The Boomtown Rats.
The Fine Art of Surfacing. 1979.

 

“Since Tuesday night,” John Cassidy wrote in The New Yorker this week, “there has been a lot of handwringing about how the media, with all its fancy analytics, failed to foresee Donald Trump’s victory”: as the New York Times headline had it, “How Data Failed Us in Calling an Election.” The failure of Nate Silver and other statistical analysts in the lead-up to Election Day rehearses, once again, a seemingly-ancient argument between what are now known as the sciences and the humanities—an argument sometimes held to be as old as the moment when Herodotus (the “Father of History”) asserted that his object in telling the story of the Greco-Persian Wars of 2500 years ago was “to set forth the reasons why [the Greeks and Persians] wage war on each other.” In other words, Herodotus thought that, to investigate war, it was necessary to understand the motives of the people who fought it—just as Cassidy says the failure of the press to get it right about this election was, Cassidy says, “a failure of analysis, rather than of observation.” The argument both Herodotus and Cassidy are making is the seemingly unanswerable one that it is the interpretation of the evidence, rather than the evidence itself, that is significant—a position that seems inarguable so long as you aren’t in the Prussian Army, dodging Nazi bombs during the last year of the Second World War, or living in Malibu.

The reason why it seems inarguable, some might say, is because the argument both Herodotus and Cassidy are making is inescapable: obviously, given Herodotus’ participation, it is a very ancient one, and yet new versions are produced all the time. Consider for instance a debate conducted by English literature professor Michael Bérubé and philosopher John Searle some years ago, about a distinction between what Searle called “brute fact” and “social fact.” “Brute facts,” Bérubé wrote later, are “phenomena like Neptune, DNA, and the cosmic background radiation,” while the second kind are “items whose existence and meaning are obviously dependent entirely on human interpretation,” such as “touchdowns and twenty-dollar bills.” Like Searle, most people might like to say that “brute fact” is clearly more significant than “social fact,” in the sense that Neptune doesn’t care what we think about it, whereas touchdowns and twenty dollar bills are just as surely entirely dependent on what we think of them.

Not so fast, said Bérubé: “there’s a compelling sense,” the professor of literature argued, in which social facts are “prior to and even constitutive of” brute facts—if social facts are the means by which we obtain our knowledge of the outside world, then social facts could “be philosophically prior to and certainly more immediately available to us humans than the world of brute fact.” The only way we know about Neptune is because a number of human beings thought it was important enough to discover; Neptune doesn’t give a damn one way or the other.

“Is the distinction between social facts and brute facts,” Bérubé therefore asks, “a social fact or a brute fact?” (Boom! Mic drop.) That is, whatever the brute facts are, we can only interpret them in the light of social facts—which would seem to grant priority to those disciplines dealing with social facts, rather than those disciplines that deal with brute fact; Hillary Clinton, Bérubé might say, would have been better off hiring an English professor, rather than a statistician, to forecast the election. Yet, despite the smugness with which Bérubé delivers what he believes is a coup de grâce, it does not seem to occur to him that traffic between the two realms can also go the other way: while it may be possible to see how “social facts” subtly influence our ability to see “brute facts,” it’s also possible to see how “brute facts” subtly influence our ability to see “social facts.” It’s merely necessary to understand how the nineteenth-century Prussian Army treated its horses.

The book that treats that question about German military horsemanship is called The Law of Small Numbers, which was published in 1898 by one Ladislaus Bortkiewicz: a Pole who lived in the Russian Empire and yet conducted a study on data about the incidence of deaths caused by horse kicks in the nineteenth-century Prussian Army. Apparently, this was a cause of some concern to military leaders: they wanted to know whether, say, if an army corp that experienced several horse kick deaths in a year—an exceptional number of deaths from this category—was using bad techniques, or whether they happened to buy particularly ornery horses. Why, in short, did some corps have what looked like an epidemic of horse kick deaths in a given year, while others might go for years without a single death? What Bortkiewicz found answered those questions—though perhaps not in a fashion the army brass might have liked.

Bortkiewicz began by assembling data about the number of fatal horse kicks in fourteen Prussian army corps over twenty years, which he then combined into “corp years”: the number of years together with the number of corps. What he found—as E.J. Gumbel pus it in the International Encyclopedia of the Social Sciences—was that for “over half the corps-year combinations there were no deaths from horse kicks,” while “for the other combinations the number of deaths ranged up to four.” In most years, in other words, no one was killed in any given corps by a horse kick, while in some years someone was—and in terrible years four were. Deaths by horse kick, then, were uncommon, which meant they were hard to study: given that they happened so rarely, it was difficult to determine what caused them—which was why Bortkiewicz had to assemble so much data about them. By doing so, the Russian Pole hoped to be able to isolate a common factor among these deaths.

In the course of studying these deaths, Bortkiewicz ended up independently re-discovering something that a French mathematician, Simeon Denis Poisson, had already, in 1837, used in connection with discussing the verdicts of juries: an arrangement of data now known as the Poisson distribution. And as the mathematics department at the University of Massachusetts is happy to tell us (https://www.umass.edu/wsp/resources/poisson/), the Poisson distribution applies when four conditions are met: first, “the event is something that can be counted in whole numbers”; second, “occurrences are independent, so that one occurrence neither diminishes nor increases the chance of another”; third, “the average frequency of occurrence for the time period in question is known”; and finally “it is possible to count how many events have occurred.” If these things are known, it seems, the Poisson distribution will tell you how often the event in question will happen in the future—a pretty useful feature for, say, predicting the results of an election. But that what wasn’t was intriguing about Bortkiewicz’ study: what made it important enough to outlast the government that commissioned it was that Bortkiewicz found that the Poisson distribution “may be used in reverse”—a discovery ended up telling us about far more than the care of Prussian horses.

What “Bortkiewicz realized,” as Aatish Bhatia of Wired wrote some years ago, was “that he could use Poisson’s formula to work out how many deaths you could expect to see” if the deaths from horse kicks in the Prussian army were random. The key to the Poisson distribution, in other words, is the second component, “occurrences are independent, so that one occurrence neither diminishes nor increases the chance of another”: a Poisson distribution describes processes that are like the flip of a coin. As everyone knows, each flip of a coin is independent of the one that came before; hence, the record of successive flips is the record of a random process—a process that will leave its mark, Bortkiewicz understood.

A Poisson distribution maps a random process; therefore, if the process in question maps a Poisson distribution, then it must be a random process. A distribution that matches the results a Poisson distribution would predict must also be a process in which each occurrence is independent of those that came before. As the UMass mathematicians say, “if the data are lumpy, we look for what might be causing the lump,” while conversely, if  “the data fit the Poisson expectation closely, then there is no strong reason to believe that something other than random occurrence is at work.” Anything that follows a Poisson distribution is likely the result of a random process; hence, what Bortkiewicz had discovered was a tool to find randomness.

Take, for example, the case of German V-2 rocket attacks on London during the last years of World War II—the background, as it happens, to novelist Thomas Pynchon’s Gravity’s Rainbow. As Pynchon’s book relates, the flying missiles were falling in a pattern: some parts of London were hit multiple times, while others were spared. Some Londoners argued that this “clustering” demonstrated that the Germans must have discovered a way to guide these missiles—something that would have been highly, highly advanced for mid-twentieth century technology. (Even today, guided missiles are incredibly advanced: much less than ten percent of all the bombs dropped during the 1991 Gulf War, for instance, had “smart bomb” technology.) So what British scientist R. D. Clarke did was to look at the data for all the targets of V-2s that fell on London. What he found was that the results matched a Poisson distribution—the Germans did not possess super-advanced guidance systems. They were just lucky.

Daniel Kahneman, the Israeli psychologist, has a similar story: “‘During the Yom Kippur War, in 1973,’” Kahneman told New Yorker writer Atul Gawande, he was approached by the Israeli Air Force to investigate why, of two squads that took to the skies during the war, “‘one had lost four planes and the other had lost none.’” Kahneman told them not to waste their time, because a “difference of four lost planes could easily have occurred by chance.” Without knowing about Bortkiewicz, that is, the Israeli Air Force “would inevitably find some measurable differences between the squadrons and feel compelled to act on them”—differences that, in reality, mattered not at all. Presumably, Israel’s opponents were bound to hit some of Israel’s warplanes; it just so happened that they were clustered in one squadron and not the other.

Why though, should any of this matter in terms of the distinction between “brute” and “social” facts? Well, consider what Herodotus wrote more than two millennia ago: what matters, when studying war, is the reasons people had for fighting. After all, wars are some of the best examples of a “social fact” anywhere: wars only exist, Herodotus is claiming, because of what people think about them. But what if it could be shown that, actually, there’s a good case to be made for thinking of war as a “brute fact”—something more like DNA or Neptune than like money or a home run? As it happens, at least one person, following in Bortkiewicz’ footsteps, already has.

In November of 1941, the British meteorologist and statistician Lewis Fry Richardson published, in the journal Nature, a curious article entitled “Frequency of Occurrence of Wars and Other Quarrels.” Richardson, it seems, had had enough of the endless theorizing about war’s causes: whether it be due to, say, simple material greed, or religion, or differences between various cultures or races. (Take for instance the American Civil War: according to some Southerners, the war could be ascribed to the racial differences between Southern “Celtics” versus Northern “Anglo-Saxons”; according to William Seward, Abraham Lincoln’s Secretary of State, the war was due to the differences in economic systems between the two regions—while to Lincoln himself, perhaps characteristically, it was all due to slavery.) Rather than argue with the historians, Richardson decided to instead gather data: he compiled a list of real wars going back centuries, then attempted to analyze the data he had collected.

What Richardson found was, to say the least, highly damaging to Herodotus: as Brian Hayes puts it in a recent article in American Scientist about Richardson’s work, when Richardson compared a group of wars with similar amounts of casualties to a Poisson distribution, he found that the “match is very close.” The British scientist also “performed a similar analysis of the dates on which wars ended—the ‘outbreaks of peace’—with the same result.” Finally, he checked another data set concerning wars, this one compiled by the University of Chicago’s Quincy Wright—“and again found good agreement.” “Thus,” Hayes writes, “the data offer no reason to believe that wars are anything other than randomly distributed accidents.” Although Herodotus argued that the only way to study wars is to study the motivations of those who fought them, there may in reality be no more “reason” for the existence of war than the existence of a forest fire in Southern California.

Herodotus, to be sure, could not have seen that: the mathematics of his time were nowhere near sophisticated enough to run a Poisson distribution. Therefore, the Greek historian was eminently justified in thinking that wars must have “reasons”: he literally did not have the conceptual tools necessary to think that wars may not have reasons at all. That was an unavailable option. But through the work of Bortkiewizc and his successors, that has now become an option: indeed, the innovation of these statisticians has been to show that our default assumption ought to be what statisticians call the “null hypothesis,” which is defined by the Cambridge Dictionary of Statistics to be “the ‘no difference’ or ‘no association’ hypothesis.” Unlike Herodotus, who presumed that explanations must equal causes, we now assume that we ought to be first sure that there is anything to explain before trying to explain it.

In this case, then, it may be that the “brute fact” of the press’ Herodotian commitment to discovering “reasons” that explains why nobody in the public sphere predicted Donald Trump’s victory: because the press is already committed to the supremacy of analysis over observation, it could not perform the observations necessary to think Trump could win. Or, as Cassidy put it, when a reporter saw the statistical election model of choice “registering the chances of the election going a certain way at ninety per cent, or ninety-five per cent, it’s easy to dismiss the other outcome as a live possibility—particularly if you haven’t been schooled in how to think in probabilistic terms, which many people haven’t.” Just how powerful the assumption of the force of analysis over data can be is demonstrated by the fact that—even despite noting the widespread lack of probabilistic thinking—Cassidy still thinks it possible that “F.B.I. Director James Comey’s intervention ten days before the election,” in which Comey announced his staff was still investigating Clinton’s emails, “may have proved decisive.” In other words, despite knowing something about the impact of probability, Cassidy still thinks it possible that a letter from the F.B.I. director was somehow more important to the outcome of this past election than the evidence of their own lives were to million of Americans—or, say, the effect of a system in which the answer to the question where outweighs that of how many?

Probabilistic reasoning, of course, was unavailable to Herodotus, who lived two millennia before the mathematical tools necessary were even invented—which is to say that, while some like to claim that the war between interpretation and data is eternal, it might not be. Yet John Cassidy—and Michael Bérubé—don’t live before those tools were invented, and yet they persist in writing as if they do. While that’s fine, so far as it is their choice as private citizens, it ought to be quite a different thing insofar as it is their jobs as journalist and teacher, respectively—particularly in the case, as say in the 2016 election, when it is of importance to the continued health of the nation as a whole that there be a clear public understanding of events. Some people appear to think that continuing the quarrels of people whose habits of mind, today, would barely qualify them to teach Sunday school is something noble; in reality, it may just be a measure of how far we have yet to travel.

 

Double Down

There is a large difference between our view of the US as a net creditor with assets of about 600 billion US dollars and BEA’s view of the US as a net debtor with total net debt of 2.5 trillion. We call the difference between these two equally arbitrary estimates dark matter, because it corresponds to assets that we know exist, since they generate revenue but cannot be seen (or, better said, cannot be properly measured). The name is taken from a term used in physics to account for the fact that the world is more stable than you would think if it were held together only by the gravity emanating from visible matter. In our measure the US owns about 3.1 trillion of unaccounted net foreign assets. [Emp. added]
—Ricardo Hausmann and Frederico Sturzenegger.
“U.S. and Global Imbalances: Can Dark Matter Prevent a Big Bang?”
13 November 2005.

 

Last month Wikileaks, the journalistic-like platform, released a series of emails that included (according to the editorial board of The Washington Post) “purloined emailed excerpts” of Hillary Clinton’s “paid speeches to corporate audiences” from 2013 to 2015—the years in which Clinton withdrew from public life while building a war-chest for her presidential campaign. In one of those speeches, she expressed what the board of the Post calls “her much-maligned view that ‘you need both a public and a private position’”—a position that, the Post harumphs, “is playing as a confession of two-facedness but is actually a clumsy formulation of obvious truth”: namely, that politics cannot operate “unless legislators can deliberate and negotiate candidly, outside the glare of publicity.” To the Post, in other words, thinking that people ought to believe the same things privately as they loudly assert publicly is the sure sign of a näivete verging on imbecility; almost certainly, the Post’s comments draw a dividing line in American life between those who “get” that distinction and those who don’t. Yet, while the Post sees fit to present Clinton’s comments as a sign of her status as “a knowledgeable, balanced political veteran with sound policy instincts and a mature sense of how to sustain a decent, stable democracy,” in point of fact it demonstrates—far more than Donald Trump’s ridiculous campaign—just how far from a “decent, stable democracy” the United States has become: because as those who, nearly a thousand years ago, first set in motion the conceptual revolution that resulted in democracy understood, there is no thought or doctrine more destructive of democracy than the idea that there is a “public” and a “private” truth.

That’s a notion that, likely, is difficult for the Post’s audience to encompass. Presumably educated at the nation’s finest schools, the Post’s audience can see no issue with Clinton’s position because the way towards it has been prepared for decades: it is, in fact, one of the foundational doctrines of current American higher education. Anyone who has attended an American institution of higher learning over the past several decades, in other words, is going to learn a version of Clinton’s belief that truth can come in two (or more) varieties, because that is what intellectuals of both the political left and the political right have asserted for more than half a century.

The African-American novelist James Baldwin asserted, for example, in 1949 that “literature and sociology are not the same,” while in 1958 the conservative political scientist Leo Strauss dismissed “the ‘scientific’ approach to society” as ignoring “the moral distinctions by which we take our bearings as citizens and”—in a now-regrettable choice of words—“as men.” It’s become so unconscious a belief among the educated, in fact, that even some scientists themselves have adopted this view: the biologist Stephen Jay Gould, for instance, towards the end of his life argued that science and religion constituted what he called “non-overlapping magisteria,” while John Carmody, a physician turned writer for The Australian, more prosaically—and seemingly modestly—asserted not long ago that “science and religion, as we understand them, are different.” The motives of those arguing for such a separation are usually thought to be inherently positive: agreeing to such a distinction, in fact, is nearly a requirement for admittance to polite society these days—which is probably why the Post can assert that Clinton’s admissions are a sign of her fitness for the presidency, instead of being disqualifying.

To the Post’s readers, in short, Hillary Clinton’s doubleness is a sign of her “sophistication” and “responsibility.” It’s a sign that she’s “one of us”—she, presumably unlike the trailer trash interested in Donald Trump’s candidacy, understands the point Rashomon! (Though, Kurosawa’s film does not—because logically it cannot—necessarily imply the view of ambiguity it’s often suggested it does: if Rashomon makes the claim that reality is ultimately unknowable, how can we know that?) But those who think thusly betray their own lack of sophistication—because, in the long history of humanity, this isn’t the first time that someone has tried to sell a similar doctrine.

Toward the height of the Middle Ages the works of Aristotle became re-discovered in Europe, in part through contacts with Muslim thinkers like the twelfth-century Andalusian Ibn-Rushd—better known in Europe as “Averroes.” Aristotle’s works were extremely exciting to students used to a steady diet of Plato and the Church Fathers—precisely because at points they contradicted, or at least appeared to contradict, those same Church Fathers. (Which was also, as it happened, what interested Ibn-Rushd about Aristotle—though in his case, the Greek philosopher appeared to contradict Muslim, instead of Christian, sources.) That however left Aristotle enthusiasts with a problem: if they continued to read the Philosopher (Aristotle) and his Commentator (Averroes), they would embark on a collision course with the religious authorities.

In The Harmony of Religion and Philosophy, it seems, Averroes taught that “philosophy and revelation do not contradict each other, and are essentially different means of reaching the same truth”—a doctrine that his later Christian followers turned into what became known as the doctrine of “double truth.” According to a lecturer at the University of Paris in the thirteenth century named Siger of Brabant, for instance, “there existed a ‘double truth’: a factual or ‘hard’ truth that is reached through science and philosophy, and a ‘religious’ truth that is reached through religion.” To Brabant and his crowd, according to Encyclopedia Britannica, “religion and philosophy, as separate sources of knowledge, might arrive at contradictory truths without detriment to either.” (Which was not the same as Averroes’ point, however: the Andalusian scholar “taught that there is only one truth, but reached in two different ways, not two truths.”) Siger of Brabant, in other words, would have been quite familiar with Hillary Clinton’s distinction between the “public” and the “private.”

To some today, of course, that would merely point to how contemporary Siger of Brabant was, and how fuddy-duddy were his opponents—like Stephen Tempier, the bishop of Paris. As if he were some 1950s backwoods Baptist preacher denouncing Elvis or the Beatles, in 1277 Tempier denounced those who “hold that something is true according to philosophy but not according to the Catholic faith, as if there are two contrary truths.” Yet, while some might want to portray Brabant, thusly, as a forerunner to today’s tolerant societies, in reality it was Tempier’s insistence that truth comes in mono, not stereo, that (seemingly paradoxically) led to the relatively open society we at present enjoy.

People who today would make that identification, that is, might be uneasy if they knew that part of the reason Brabant believed his doctrine was his belief in “the superiority of philosophers to the common people,” or that Averroes himself warned “against teaching philosophical methods to the general populace.” Two truths, in other words, easily translated into two different kinds of people—and make no mistake, these doctrines did not imply that these two differing types were “separate but equal.” Instead, they were a means of asserting the superiority of the one type over the other. The doctrine of “double truth,” in other words, was not a forerunner to today’s easygoing societies.

To George Orwell, in fact, it was prerequisite for totalitarianism: Brabant’s theory of “double truth,” in other words, may be the origin of the concept of “doublethink” as used in Orwell’s 1984. In that 1948 novel, “doublethink” is defined as

To know and not to know, to be conscious of complete truthfulness while telling carefully constructed lies, to hold simultaneously two opinions which cancelled out, knowing them to be contradictory and believing in both of them, to use logic against logic, to repudiate morality while laying claim to it, to believe that democracy was impossible and that the Party was the guardian of democracy, to forget whatever it was necessary to forget, then to draw it back into memory again at the moment when it was needed, and then promptly to forget it again, and above all, to apply the same process to the process itself – that was the ultimate subtlety: consciously to induce unconsciousness, and then, once again, to become unconscious of the act of hypnosis you had just performed. Even to understand the word ‘doublethink’ involved the use of doublethink.

It was a point Orwell had been thinking about for some time: in a 1946 essay entitled “Politics and the English Language,” he had denounced “unscrupulous politicians, advertisers, religionists, and other doublespeakers of whatever stripe [who] continue to abuse language for manipulative purposes.” To Orwell, the doctrine of the “double truth” was just a means of sloughing off feelings of guilt or shame naturally produced by human beings engaged in such manipulations—a technique vital to totalitarian regimes.

Many in today’s universities, to be sure, have a deep distrust for Orwell: Louis Menand—who not only teaches at Harvard and writes for The New Yorker, but grew up in a Hudson Valley town named for his own great-grandfather—perhaps summed up the currently fashionable opinion of the English writer when he noted, in a drive-by slur, that Orwell was “a man who believed that to write honestly he needed to publish under a false name.” The British novelist Will Self, in turn, has attacked Orwell as the “Supreme Mediocrity”—and in particular takes issue with Orwell’s stand, in “Politics and the English Language,” in favor of the idea “that anything worth saying in English can be set down with perfect clarity such that it’s comprehensible to all averagely intelligent English readers.” It’s exactly that part of Orwell’s position that most threatens those of Self’s view.

Orwell’s assertion, Self says flatly, is simply “not true”—an assertion that Self explicitly ties to issues of minority representation. “Only homogeneous groups of people all speak and write identically,” Self writes against Orwell; in reality, Self says, “[p]eople from different heritages, ethnicities, classes and regions speak the same language differently, duh!” Orwell’s big argument against “doublethink”—and thusly, totalitarianism—is in other words just “talented dog-whistling calling [us] to chow down on a big bowl of conformity.” Thusly, “underlying” Orwell’s argument “are good old-fashioned prejudices against difference itself.” Orwell, in short, is a racist.

Maybe that’s true—but it may also be worth noting that the sort of “tolerance” advocated by people like Self can also be interpreted, and has been for centuries, as in the first place a direct assault on the principle of rationality, and in the second place an abandonment of millions of people. Such, at least, is how Thomas Aquinas would have received Self’s point. The Angelic Doctor, as the Church calls him, asserted that Averroeists like Brabant could be refuted on their own terms: the Averroeists said they believed, Aquinas remarked, that philosophy taught them that truth must be one, but faith taught them the opposite—a position that would lead those who held it to think “that faith avows what is false and impossible.” According to Aquinas, the doctrine of the “double truth” would imply that belief in religion was as much as admitting that religion was foolish—at which point you have admitted that there is only a single truth, and it isn’t a religious one. Hence, Aquinas’ point was that, despite what Orwell feared in 1984, it simply is not psychologically possible to hold two opposed beliefs in one’s head simultaneously: whenever someone is faced with a choice like that, that person will inevitably choose one side or the other.

In this, Aquinas was merely following his predecessors. To the ancients, this was known as the “law of non-contradiction”—one of the ancient world’s three fundamental laws of thought. “No one can believe that the same thing can (at the same time) be and not be,” as Aristotle himself put that law in the Metaphysics; nobody can (sincerely) believe one thing and its opposite at the same time. As the Persian, Avicenna—demonstrating that this law was hardly limited to Europeans—put it centuries later: “Anyone who denies the law of non-contradiction should be beaten and burned until he admits that to be beaten is not the same as not to be beaten, and to be burned is not the same as not to be burned.” Or finally, as Arthur Schopenhauer wrote centuries after that in The World as Will and Representation (using the heavy-handed vocabulary of German philosophers), “every two concept-spheres must be thought of as either united or as separated, but never as both at once; and therefore, although words are joined together which express the latter, these words assert a process of thought which cannot be carried out” (emp. added). If anyone says the contrary, these philosophers implied,  somebody’s selling something.

The point that Aristotle, Aquinas, Avicenna, and Orwell were making, in other words, is that the law of non-contradiction is essentially identical to rationality itself: a nearly foolproof method of performing the most basic of intellectual tasks—above all, telling honest and rational people from dishonest and duplicitous ones. And that, in turn, would lead to their second refutation of Self’s argument: by abandoning the law of non-contradiction, people like Brabant (or Self) were also effectively setting themselves above ordinary people. As one commenter on Aquinas writes, the Good Doctor’s insisted that if something is true, then “it must make sense and it must make sense in terms which are related to the ordinary, untheological ways in which human beings try to make sense of things”—as Orwell saw, that position is related to the law of noncontradiction, and both are related to the notion of democratic government, because telling which candidate is the better one is exactly the very foundation of that form of government. When Will Self attacks George Orwell for being in favor of comprehensibility, in other words, he isn’t attacking Orwell alone: he’s actually attacking Thomas Aquinas—and ultimately the very possibility of self-governance.

While the supporters of Hillary Clinton like to describe her opponent as a threat to democratic government, in other words, Donald Trump’s minor campaign arguably poses far less threat to American freedoms than hers does: from one point of view, Clinton’s accession to power actually threatens the basic conceptual apparatus without which there can be no democracy. Of course, given that during this presidential campaign virtually no attention has been paid, say, to the findings of social scientists (like Ricardo Hausmann and Federico Sturzenegger) and journalists (like those who reported on The Panama Papers) that while many conservatives bemoan such deficits as the U.S. budget or trade imbalances, in fact there is good reason to suspect that such gaps are actually the result of billions (or trillions) of dollars being hidden by wealthy Americans and corporations beyond the reach of the Internal Revenue Service (an agency whose budget has been gutted in recent decades by conservatives)—well, let’s just say that there’s good reason to suspect that Hillary Clinton’s campaign may not be what it appears to be.

After all—she said so.