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.

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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.”

 

The “Hero” We Deserve

“He’s the hero Gotham deserves, but not the one it needs …”
The Dark Knight. (2008).

 

The election of Donald Trump, Peter Beinart argued the other day in The Atlantic, was precisely “the kind of democratic catastrophe that the Constitution, and the Electoral College in particular, were in part designed to prevent.” It’s a fairly common sentiment, it seems, in some parts of the liberal press: Bob Cesca, of Salon, argued back in October that “the shrieking, wild-eyed, uncorked flailing that’s taking place among supporters of Donald Trump, both online and off” made an “abundantly self-evident” case for “the establishment of the Electoral College as a bulwark against destabilizing figures with the charisma to easily manipulate [sic] low-information voters.”  Such arguments often seem to think that their opponents are dewy-eyed idealists, their eyes clouded by Frank Capra movies: Cesca, for example, calls the view in favor of direct popular voting an argument for “popular whimsy.” In reality however it’s the supposedly-liberal argument in favor of the Electoral College that’s based on a misperception: what people like Beinart or Cesca don’t see is that the Electoral College is not a “bulwark” for preventing the election of candidates like Donald Trump—but in fact a machine for producing them. They don’t see it because they do not understand how the Electoral College is built on a flawed knowledge of probability—an argument in turn that, perhaps horrifically, suggests that the idea that powered Trump’s campaign, the thought that the American leadership class is dangerously out of touch with reality, is more or less right.

To see just how ignorant we all are concerning that knowledge, ask yourself this question (as Distinguished Research Scientist of the National Board of Medical Examiners Howard Wainer asked several years ago in the pages of American Scientist): what are the counties of the United States with the highest distribution of kidney cancer? As it happens, Wainer noted, they “tend to be very rural, Midwestern, Southern, or Western”—a finding that might make sense, say, in view of the fact that rural areas tend to be freer of the pollution that infects the largest cities. But, Wainer continued, consider also that the American counties with the lowest distribution of kidney cancer … “tend to be very rural, Midwestern, Southern, or Western”—a finding that might make sense, Wainer remarks, due to “the poverty of the rural lifestyle.” After all, people in rural counties very often don’t receive the best medical care, tend to eat worse, and tend to drink too much and use too much tobacco. But wait—one of these stories has to be wrong, they can’t both be right. Yet as Wainer goes on to write, they both are true: rural American counties have both the highest and the lowest incidences of kidney cancer. But how?

To solve the seeming-mystery, consider a hypothetical example taken from the Nobel Prize-winner Daniel Kahneman’s magisterial book, Thinking: Fast and Slow. “Imagine,” Kahneman says, “a large urn filled with marbles.” Some of these marbles are white, and some are red. Now imagine “two very patient marble counters” taking turns drawing from the urn: “Jack draws 4 marbles on each trial, Jill draws 7.” Every time one of them draws an unusual sample—that is, a sample of marbles that is either all-red or all-white—each records it. The question Kahneman then implicitly asks is: which marble counter will draw more all-white (or all-red) samples?

The answer is Jack—“by a factor of 8,” Kahneman notes: Jack is likely to draw a sample of only one color more than twelve percent of the time, while Jill is likely to draw such a sample less than two percent of the time. But it isn’t really necessary to know high-level mathematics to understand that because Jack is drawing fewer marbles at a time, it is more likely that he will draw all of one color or the other than Jill is. By drawing fewer marbles, Jack is simultaneously more exposed to extreme events—just as it is more likely that, as Wainer has observed, a “county with, say, 100 inhabitants that has no cancer deaths would be in the lowest category,” while conversely if that same county “has one cancer death it would be among the highest.” Because there are fewer people in rural American counties than urban ones, a rural county will have a more extreme rate of kidney cancer, either high or low, than an urban one—for the very same reason that Jack is more likely to have a set of all-white or all-red marbles. The sample size is smaller—and the smaller the sample size, the more likely it is that the sample will be an outlier.

So far, of course, I might be said to be merely repeating something everyone already knows—maybe you anticipated the point about Jack and Jill and the rural counties, or maybe you just don’t see how any of this has any bearing beyond the lesson that scientists ought to be careful when they are designing their experiments. As many Americans think these days, perhaps you think that science is one thing, and politics is something else—maybe because Americans have been taught for several generations now, by people as diverse as conservative philosopher Leo Strauss and liberal biologist Stephen Jay Gould, that the humanities are one thing and the sciences are another. (Which Geoffrey Harpham, formerly the director of the National Humanities Center, might not find surprising: Harpham has claimed that “the modern concept of the humanities” —that is, as something distinct from the sciences—“is truly native only to the United States.”) But consider another of Wainer’s examples: one drawn from, as it happens, the world of education.

“In the late 1990s,” Wainer writes, “the Bill and Melinda Gates Foundation began supporting small schools on a broad-ranging, intensive, national basis.” Other foundations supporting the movement for smaller schools included, Wainer reported, the Annenberg Foundation, the Carnegie Corporation, George Soro’s Open Society Institute, and the Pew Cheritable Trusts, as well as the U.S. Department of Education’s Smaller Learning Communities Program. These programs brought pressure—to the tune 1.7 billion dollars—on many American school systems to break up their larger schools (a pressure that, incidentally, succeeded in cities like Los Angeles, New York, Chicago, and Seattle, among others). The reason the Gates Foundation and its helpers cited for pressuring America’s educators was that, as Wainer writes, surveys showed that “among high-performing schools, there is an unrepresentatively large proportion of smaller schools.” That is, when researchers looked at American schools, they found the highest-achieving schools included a disproportionate number of small ones.

By now, you see where this is going. What all of these educational specialists didn’t consider—but Wainer’s subsequent research found, at least in Pennsylvania—was that small schools were also disproportionately represented among the lowest-achieving schools. The Gates Foundation (led, mind you, by Bill Gates) had simply failed to consider that of course small schools might be overrepresented among the best schools, simply because schools with smaller numbers of students are more likely to be extreme cases. (Something that, by the way, also may have consequences for that perennial goal of professional educators: the smaller class size.) Small schools tend to be represented at the extremes not for any particular reason, but just because that’s how math works.

The inherent humor of a group of educators (and Bill Gates) not understanding how to do basic mathematics is, admittedly, self-evident—and incidentally good reason not to take the testimony of “experts” at face value. But more significantly, it also demonstrates the very real problem here: if highly-educated people (along with college dropout Gates) cannot see the flaws in their own reasoning while discussing precisely the question of education, how much more vulnerable is everyone else to flaws in their thinking? To people like Bob Cesca or Peter Beinart (or David Frum; cf. “Noble Lie”), of course, the answer to this problem is to install more professionals, more experts, to protect us from our own ignorance: to erect, as Cesca urges, a “firewall[…] against ignorant populism.” (A wording that, one imagines, reflects Cesca’s mighty struggle to avoid the word “peasants.”) The difficulty with such reasoning, however, is that it ignores the fact that the Electoral College is an instance of the same sort of ignorance as that which bedeviled the Gates Foundation—or that you may have encountered in yourself when you considered the kidney cancer example above.

Just as rural American counties, that is, are more likely to have either lots of cases—or very few cases—of kidney cancer, so too must those very same sparsely-populated states be more likely to vote in an extreme fashion inconsistent with the rest of the country. For one, it’s a lot cheaper to convince the voters of Wyoming (the half a million or so of whom possess not only a congressman, but also two senators) than the voters of, say, Staten Island (who, despite being only slightly less in number than the inhabitants of Wyoming, have to share a single congressman with part of Brooklyn). Yet the existence of the Electoral College, according to Peter Beinart, demonstrates just how “prescient” the authors of the Constitution were: while Beinart says he “could never have imagined President Donald Trump,” he’s glad that the college is cleverly constructed so as to … well, so far as I can tell Beinart appears to be insinuating that the Electoral College somehow prevented Trump’s election—so, yeeaaaah. Anyway, for those of us still living in reality, suffice it to say that the kidney cancer example illustrates just how dividing one big election into fifty smaller ones inherently makes it more probable that some of those subsidiary elections will be outliers. Not for any particular reason, mind you, but simply because that’s how math works—as anyone not named Bill Gates seems intelligent enough to understand once it’s explained.

In any case, the Electoral College thusly does not make it less likely that an outlier candidate like Donald Trump is elected—but instead more likely that such a candidate would be elected. What Beinart and other cheerleaders for the Electoral College fail to understand (either due to ignorance or some other motive) is that the Electoral College is not a “bulwark” or “firewall” against the Donald Trumps of the world. In reality—a place that, Trump has often implied, those in power seem not to inhabit any more—the Electoral College did not prevent Donald Trump from becoming the president of the United States, but instead (just as everyone witnessed on Election Day), exactly the means by which the “short-fingered vulgarian” became the nation’s leader. Contrary to Beinart or Cesca, the Electoral College is not a “firewall” or some cybersecurity app—it is, instead, a roulette wheel, and a biased one at that.

Like a sucker can expect that, so long as she stays at the roulette wheel, she will eventually go bust, thusly so too can the United States expect, so long as the Electoral College exists, to get presidents like Donald Trump: “accidental” presidencies, after all, have been an occasional feature of presidential elections since at least 1824, when John Quincy Adams was elected despite the fact that Andrew Jackson had won the popular vote. If not even the watchdogs of the American leadership class—much less that class itself—can see the mathematical point of the argument against the Electoral College, that in and of itself is pretty good reason to think that, while the specifics of Donald Trump’s criticisms of the Establishment during the campaign might have been ridiculous, he wasn’t wrong to criticize it. Donald Trump then may not be the president-elect America needs—but he might just be the president people like Peter Beinart and Bob Cesca deserve.

 

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.

 

Noble Lie

With a crew and good captain well seasoned,
They left fully loaded for Cleveland.
—“The Wreck of the Edmund Fitzgerald.” 1976.

The comedian Bill Maher began the “panel” part of his show Real Time the other day—the last episode before the election—by noting that virtually every political expert had dismissed Donald Trump’s candidacy at every stage of the past year’s campaign. When Trump announced he was running, Maher observed, the pundits said “oh, he’s just saying that … because he just wants to promote his brand.” They said Trump wouldn’t win any voters, Maher noted—“then he won votes.” And then, Maher went on, they said he wouldn’t win any primaries—“then he won primaries.” And so on, until Trump became the Republican nominee. So much we know, but what was of interest about the show was the response one of Maher’s guests: David Frum, a Canadian who despite his immigrant origins became a speechwriter for George W. Bush, invented the phrase “axis of evil,” and has since joined the staff of the supposedly liberal magazine, The Atlantic. The interest of Frum’s response was not only how marvelously inane it was—but also how it had already been decisively refuted only hours earlier, by men playing a boy’s game on the Lake Erie shore.

Maybe I’m being cruel however: like most television shows, Real Time with Bill Maher is shot before it is aired, and this episode was released last Friday. Frum then may not have been aware, when he said what he said, that the Chicago Cubs won the World Series on Wednesday—and if he is like most people, Frum is furthermore unaware of the significance of that event, which goes (as I will demonstrate) far beyond matters baseball. Still, surely Frum must have been aware of how ridiculous what he said was, given that the conversation began with Maher reciting the failures of the pundit class—and Frum admitted to belonging to that class. “I was one of those pundits that you made fun of,” Frum confessed to Maher—yet despite that admission, Frum went on to make a breathtakingly pro-pundit argument.

Trump’s candidacy, Frum said, demonstrated the importance of the gatekeepers of the public interest—the editors of the national newspapers, for instance, or the anchors of the network news shows, or the mandarins of the political parties. Retailing a similar  argument to one made by, among others, Salon’s Bob Cesca—who contended in early October that “social media is the trough from which Trump feeds”—Frum proceeded to make the case that the Trump phenomena was only possible once apps like Facebook and Twitter enabled presidential candidates to bypass the traditional centers of power. To Frum, in other words, the proper response to the complete failure of the establishment (to defeat Trump) was to prop up the establishment (so as to defeat future Trumps). To protect against the failure of experts Frum earnestly argued—with no apparent sense of irony—that we ought to give more power to experts.

There is, I admit, a certain schadenfreude in witnessing a veteran of the Bush Administration tout the importance of experts, given that George W.’s regime was notable for, among other things, “systematically chang[ing] and supress[ing] … scientific reports about global warming” (according to the British Broadcasting Corporation)—and not even to discuss how Bush cadres torpedoed the advice of the professionals of the CIA vis á vis the weapons-buying habits of a certain Middle Eastern tyrant. But the larger issue, however, is that the very importance of “expert” knowledge has been undergoing a deep interrogation for decades now—and that the victory of the Chicago Cubs in this year’s World Series has brought much of that critique to the mainstream.

What I mean can be demonstrated by a story told by the physicist Freeman Dyson—a man who never won a Nobel Prize, nor even received a doctorate, but nevertheless was awarded a place at Princeton’s Institute of Advanced Study at the ripe age of thirty by none other than Robert Oppenheimer (the man in charge of the Manhattan Project) himself. Although Dyson has had a lot to say during his long life—and a lot worth listening to—on a wide range of subjects, from interstellar travel to Chinese domestic politics, of interest to me in connection to Frum’s remarks on Donald Trump is an article Dyson published in The New York Review of Books in 2011, about a man who did win the Nobel Prize: the Israeli psychologist Daniel Kahneman, who won the prize for economics in 2002. In that article, Dyson told a story about himself: specifically, what he did during World War II—an experience, it turns out, that leads by a circuitous path over the course of seven decades to the epic clash resolved by the shores of Lake Erie in the wee hours of 3 November.

Entitled “How to Dispel Your Illusions,” Dyson there tells the story of being a young statistician with the Royal Air Force’s Bomber Command in the spring of 1944—a force that suffered, according to the United Kingdom’s Bomber Command Museum, “a loss rate comparable only to the worst slaughter of the First World War trenches.” To combat this horror, Dyson was charged with discovering the common denominator between the bomber crews that survived until the end of their thirty-mission tour of duty (about 25% of all air crews). Since they were succeeding when three out of four of their comrades were failing, Dyson’s superiors assumed that those successful crews were doing something that their less-successful colleagues (who were mostly so much less successful that they were no longer among the living) were not.

Bomber Command, that is, had a theory about why some survived and some died: “As [an air crew] became more skillful and more closely bonded,” Dyson writes that everyone at Bomber Command thought, “their chances of survival would improve.” So Dyson, in order to discover what that something was, plunged in among the data of all the bombing missions the United Kingdom had run over Germany since the beginning of the war. If he could find it, maybe it could be taught to the others—and the war brought that much closer to an end. But despite all his searching, Dyson never found that magic ingredient.

It wasn’t that Dyson didn’t look hard enough for it: according to Dyson, he “did a careful analysis of the correlation between the experience of the crews and their loss rates, subdividing the data into many small packages so as to eliminate effects of weather and geography.” Yet, no matter how many different ways he looked at the data, he could not find evidence that the air crews that survived were any different than the ones shot down over Berlin or lost in the North Sea: “There was no effect of experience,” Dyson’s work found, “on loss rate.” Who lived and who died while attempting to burn Dresden or blow up Hamburg was not a matter of experience: “whether a crew lived or died,” Dyson writes, “was purely a matter of chance.” The surviving crews possessed no magical ingredient. They couldn’t—perhaps because there wasn’t one.

Still, despite the conclusiveness of Dyson’s results his studies had no effect on the operations of Bomber Command: “The crews continued to die, experienced and inexperienced alike, until Germany was overrun and the war finally ended.” While Dyson’s research suggested that dying in the stratosphere over Lübeck had no relation to skill, no one at the highest levels wanted to admit that the survivors weren’t experts—that they were instead just lucky. Perhaps, had the war continued, Dyson’s argument might eventually have won out—but the war ended, fortunately (or not) for the air crews of the Royal Air Force, before Bomber Command had to admit he was right.

All of that, of course, might appear to have little to do with the Chicago Cubs—until it’s recognized that the end of their century-long championship drought had everything to do with the eventual success of Dyson’s argument. Unlike Bomber Command, the Cubs have been at the forefront of what The Ringer’s Rany Jazayerli calls baseball’s “Great Analytics War”—and unlike the contest between Dyson and his superiors, that war has had a definite conclusion. The battle between what Jazayerli calls an “objective, data-driven view” and an older vision of baseball “ended at 48 minutes after midnight on November 3”—when the Cubs (led by a general manager who, like Dyson, trusted to statistical analysis) recorded the final out of the 2016 season.

That general manager is Theo Epstein—a man who was converted to Dyson’s “faith” at an early age. According to ESPN, Epstein, “when he was 12 … got his first Bill James historical abstract”—and as many now recognize, James pioneered applying the same basic approach Dyson used to think about how to bomb Frankfurt to winning baseball games. An obscure graduate of the University of Kansas, after graduation James took a job as a night security guard at the Stokely-Van Camp pork and beans cannery in Kansas City—and while isolated in what one imagines were the sultry (or wintry) Kansas City evenings of the 1970s, James had plenty of time to think about what interested him. That turned out to be somewhat like the problem Dyson had faced a generation earlier: where Dyson was concerned with how to win World War II, James was interested in what appeared to be the much-less portentous question of how to win the American League. James thereby invented an entire field—what’s now known as sabermetrics, or the statistical study of baseball—and in so doing, the tools James invented have become the keys to baseball’s kingdom. After all, Epstein—employed by a team owner who hired James as a consultant in 2003—not only used James’ work to end the Cubs’ errand in baseball’s wilderness but also, as all the world knows, constructed the Boston Red Sox championship teams of 2004 and 2007.

What James had done, of course, is shown how the supposed baseball “experts”—the ex-players and cronies that dominated front offices at the time—in fact knew very little about the game: they did not know, for example, that the most valuable single thing a batter can do is to get on base, or that stolen bases are, for the most part, a waste of time. (The risk of making an out, as per for example David Smith’s “Maury Wills and the Value of a Stolen Base,” is more significant than the benefit of gaining a base.) James’ insights had not merely furnished the weaponry used by Epstein; during the early 2000s another baseball team, the Oakland A’s, and their manager Billy Beane, had used James-inspired work to get to the playoffs four consecutive years (from 2000 to 2003), and won twenty consecutive games in 2002—a run famously chronicled by journalist Michael Lewis’ book, Moneyball: The Art of Winning an Unfair Game, which later became a Hollywood movie starring Brad Pitt. What isn’t much known, however, is that Lewis has noticed the intellectual connection between this work in the sport of baseball—and the work Dyson thought of as similar to his own work as a statistician for Bomber Command: the work of psychologist Kahneman and his now-deceased colleague, Amos Tversky.

The connection between James, Kahneman, and Tversky—an excellent name for a law firm—was first noticed, Lewis says, in a review of his Moneyball book by University of Chicago professors Cass Sunstein, of the law school, and Richard Thaler, an economist. When Lewis described the failures of the “old baseball men,” and conversely Beane’s success, the two professors observed that “Lewis is actually speaking here of a central finding in cognitive psychology”: the finding upon which Kahneman and Tversky based their careers. Whereas Billy Beane’s enemies on other baseball teams tended “to rely on simple rules of thumb, on traditions, on habits, on what other experts seem to believe,” Sunstein and Thaler pointed out that Beane relied on the same principle that Dyson found when examining the relative success of bomber pilots: “Statistics and simple arithmetic tell us more about ourselves than expert intuition.” While Bomber Command in other words relied on the word of their “expert” pilots, who perhaps might have said they survived a run over a ball-bearing plant because of some maneuver or other, baseball front offices relied for decades on ex-players who thought they had won some long-ago game on the basis of some clever piece of baserunning. Tversky and Kahneman’s work, however—like that of Beane and Dyson—suggested that much of what passes as “expert” judgment can be, for decades if not centuries, an edifice erected on sand.

That work has, as Lewis found after investigating the point when his attention was drawn to it by Sunstein and Thaler’s article, been replicated in several fields: in the work of the physician Atul Gawande, for instance, who, Lewis says, “has shown the dangers of doctors who place too much faith in their intuition.” The University of California, Berkeley finance professor Terry Odean “examined 10,000 individual brokerage accounts to see if stocks the brokers bought outperformed stocks they sold and found that the reverse was true.” And another doctor, Toronto’s Donald Redelmeier—who studied under Tversky—found “that an applicant was less likely to be admitted to medical school if he was interviewed on a rainy day.” In all of these cases (and this is not even to bring up the subject of, say, the financial crisis of 2007-08, a crisis arguably brought on precisely by the advice of “experts”), investigation has shown that “expert” opinion may not be what it is cracked up to be. It may in fact actually be worse than the judgment of laypeople.

If so, might I suggest, then David Frum’s “expert” suggestion about what to do to avoid a replay of the Trump candidacy—reinforce the rule of experts, a proposition that itself makes several questionable assumptions about the nature of the events of the past two years, if not decades—stops appearing to be a reasonable proposition. It begins, in fact, to appear rather more sinister: an attempt by those in Frum’s position in life—what we might call Eastern, Ivy League-types—to will themselves into believing that Trump’s candidacy is fueled by a redneck resistance to “reason,” along with good old-fashioned American racism and sexism. But what the Cubs’ victory might suggest is that what could actually be powering Trump is the recognition by the American people that many of the “cures” dispensed by the American political class are nothing more than snake oil proffered by cynical tools like David Frum. That snake oil doubles down on exactly the same “expert” policies (like freeing capital to wander the world, while increasingly shackling labor) that, debatably, is what led to the rise of Trump in the first place—a message that, presumably, must be welcome to Frum’s superiors at whatever the contemporary equivalent of Bomber Command is.

Still, despite the fact that the David Frums of the world continue to peddle their nonsense in polite society, even this descendant of South Side White Sox fans must allow that Theo Epstein’s victory has given cause for hope down here at the street-level of a Midwestern city that for has, for more years than the Cubs have been in existence, been the plaything of Eastern-elite labor and trade policies. It’s a hope that, it seems, now has a Ground Zero.

You can see it at the intersection of Clark and Addison.

Windy Orders

Time flies like an arrow; fruit flies like a banana.
Modern Saying


There’s a story told at Royal Troon, site of the “Postage Stamp” par-three hole, about the lady golfer, playing into an extreme wind, who was handed her driver by her caddie. After she hit the shot, as the ball fell helplessly short against the gale, she shouted reproachfully, “You underclubbed me!” It’s a story that has a certain resonance for me—perhaps obviously—but also, more immediately, due to my present work at a golf course in South Carolina, where I have repaired following the arrival of snow in Chicago. It’s easy enough to imagine something similar occurring at Chechessee Creek’s 16th hole—which, if it did, might not furnish the material for a modest laugh so much as, in concurrence with the golf course’s next hole, demonstrate something rather more profound. 
     Chechessee Creek, the golf course where I am spending this late fall, is a design of the Coore/Crenshaw operation, and it’s very well known that Ben Crenshaw, one of the principals of the firm, considers Chicago Golf Club to be the epitome of good course design. It’s reflected in a number of features of the course: the elevated greens, the various “dunes” strewn about for no apparent reason. But it’s also true that Chicago Golf is, despite its much greater age, by far the more daring of the two courses: it has blind shots and incredibly risky greens where putts can not only fall off the green, but go bounding down the fairway twenty yards or more. There are places where at times it is better to hit a putt off the green deliberately—because that is the only way to get the ball to stop near the hole. Chechessee Creek, for good or ill, has none of these features.
     What it does have, however, is a sense of what David Mihm, writer of the EpicGolf website, calls “pacing.” “Golf is a game,” he points out, “that is experienced chronologically”—that is, it isn’t just the quality of the holes that is important, but also their situation within the golf course as a whole. “By definition,” he says, “part of a hole’s greatness must depend on where it falls in the round.” 
     Chicago Golf Club has that quality of pacing in abundance, starting with the very first hole, Valley. By means of a trompe l’oeil the hole, in reality a 450 yard monster of a par four, appears to be a quite sedate, much-shorter hole. It’s only upon seeing his drive “disappear” (into the concealed vale that gives the hole its name) that the golfer realizes that his eye has misled him. It’s a trick, sure, that would be fantastic on any hole—but is particularly appropriate on the first, since it signals to the golfer immediately—on the first shot of the day—that this is a different kind of golf course, and that he cannot trust what he sees. 
     I would not say that Chechessee Creek exemplifies that notion to the same degree; it may not be too much to wonder whether South Carolina, or at least the Lowcountry, Tidewater parts of it, might not be too level of a countryside really to lend itself to golf. (“All over the world,” says Anita Harris, the geologist turned tour guide in John McPhee’s monumental Annals of the Former World, “when people make golf courses they are copying glacial landscapes.” South Carolina, needless to say, did not experience the devastations of an ice sheet during the last Ice Age, or any other time.) Still, there is one set of holes that does exhibit what Mihm is talking about—and perhaps something more besides. 
     The sixteenth hole at Chechessee is, as perhaps might be put together, a long par three hole; so long, in fact, that it isn’t unlikely that a short hitter might use a driver there. But, of course, there is the small matter of pride to contend with—few (male) golfers ever want to concede that they needed a driver on a “short” hole. It’s something I saw often working at Medinah, when coming to the thirteenth hole—almost inevitably, someone would not hit the correct club because he took as it an affront to suggest hitting a driver or even a three wood. Fair enough, one supposes; these days, the long par three might be close to becoming a design cliche (and in any case, all iconic courses I have seen have one: Olympia Fields, Chicago Golf, and Butler do, as does Riviera). 
     Just having a long par three isn’t enough, obviously, to satisfy Mihm’s criteria, and it isn’t that alone that makes Chechessee unique or even interesting. What makes the course go is the hole that follows the sixteenth, the seventeenth (duh). It’s an intriguing design in its own right, because it is an example of a “Leven” hole. According to A Disorderly Compendium of Golf (and what better source?), Leven holes are modeled on the 7th at the Leven Links, a hole that no longer exists. The idea of it is simple: it is a short hole with an enormous hazard on one side of the fairway; at Chechessee, the hazard is a long-grassed and swampy depression. Thus, the question posed is, how much of the hazard will you dare? Bailing out to the side leaves the player with a poor, often obstructed view of the green; at Chechessee, that function is furnished by an enormous pine tree.
     Yet that dilemma alone isn’t the real crux of the matter—what matters is that the seventeenth follows the sixteenth. After all, at the sixteenth the golfer is tempted, by his own ego, not to hit enough club. Conversely, at the seventeenth, the golfer is tempted to hit too much club. The quandary posed at each tee, in short, is precisely the mirror of the other: failing to reach for a driver on the sixteenth can cause the player to demand it on the seventeenth—with disastrous consequences in each case. And that is interesting enough merely in terms of golf, to be sure. But what is likely far more intriguing about it is that the placing of these holes could not be better situated to illustrate—nay, perform—what two psychologists said about how the human mind actually works.  
      The psychologists were Daniel Kahneman and Amos Tversky—Kahneman recently received the Nobel Prize for his work with Tversky, who couldn’t receive the award because he died in 1996. What their work did was to uncover, by means of various experiments, some of the hidden pathways of the human mind: the “cognitive shortcuts” taken by the brain. One of these discoveries was the fact that human beings are “loss averse”—or, as Jonah Lehrer put it not long ago in the New Yorker, that for human beings “losses hurt more than gains feel good.” Kahneman and Tversky called this idea “prospect theory.” 
     The effect has been measured in golf. In a paper entitled “Is Tiger Woods Loss Averse? Persistent Bias In the Face of Experience, Competition, and High Stakes” two Wharton professors found that, for PGA Tour golfers, “the agony of a bogey seems to outweigh the thrill of a birdie.” What their data (from the PGA Tour’s ShotLink system, which measures the distance of every shot hit on tour) demonstrated was that tour players “make their birdie putts approximately two percentage points less often than they make comparable par putts.” Somehow, when pros are faced with a par putt instead of a birdie putt—even though they might be identical putts—they make the former slightly more than the latter. What that translates into is one stroke left on the table per tournament—and that leaves $1.2 million per year in prize money being given away by the top twenty players.
     It’s a phenomenon that’s been found again and again in many disparate fields: investors hold on to too many low-risk bonds, for instance, while condos stay on the market far too long (because their owners won’t reduce their price even during economic downturns), and NFL coaches will take the “sure thing” of a field goal even when it might actually hurt their chances of winning the game. This last, while being about sports, has also another dimension of application to golf: the way in which what can be called “social expectations” guides human decision-making. That is, how our ideas about how others judge us plays a role in our decisions.
     In the case of the NFL, studies have shown that coaches far more likely to make the decision to kick the ball—to punt or attempt a field goal—than they are to attempt a first down or a touchdown. This is so even in situations (such as on the opponent’s 2 yard line) where, say, scoring a field goal actually leaves the opponent in a better position: if the team doesn’t get the touchdown or first down, the opponent is pinned against his own goal line, whereas a field goal means a kickoff that will likely result in the opponent starting at the twenty yard line at least. NFL coaches, in other words, aren’t making these decisions entirely rationally. To some, it suggests that they are attempting to act conventionally: that is, by doing what everyone else does, each coach can “hide” better.
     What that suggests is just why golfers, faced with the sixteenth hole, are averse to select what’s actually the right club. Each golfer is, in a sense, engaged in an arms race with every other golfer: by taking more club than another, that implicitly cedes something to the player taking less. This, despite the fact that rationally speaking selecting a different club than another golfer does nothing towards the final score of each. Taking less club becomes a kind of auction—or as we might term it, a bidding war—but one where the risk of “losing face” is seen as more significant than the final score. 
     The same process is, if it exists at all, also at work on the seventeenth hole. But this time there’s an additional piece of information playing out in the golfer’s mind: whatever happened on the last hole. One plausible scenario—I’ve seen it happen—is that the player doesn’t take enough club on the sixteenth, and comes up short of the hole. Having made that decision, and been wrong, the golfer determines on the next hole to make the “sensible” choice, and lays up away from the hazard—leaving a difficult second shot to a small green. But here’s the thing: the “carry” on the tee shot on seventeen, which I’ve withheld until now, is only about 210 yards—which is about the same as that of the sixteenth hole. In other words, the reality is that—evaluated dispassionately—golfers should probably hit about the same club on each hole. If they don’t, it’s probably due to a collision between “prospect theory” and “pacing”—which is to say that the Coore and Crenshaw design of Chechessee Creek is, all things considered, clubbed about right.