The Formula

What pit bulls can teach us about profiling.


One afternoon last February, buy more about Guy Clairoux picked up his two-and-a half-year-old son, information pills Jayden, order from day care and walked him back to their house in the west end of Ottawa, Ontario. They were almost home. Jayden was straggling behind, and, as his father’s back was turned, a pit bull jumped over a back-yard fence and lunged at Jayden. “The dog had his head in its mouth and started to do this shake,” Clairoux’s wife, JoAnn Hartley, said later. As she watched in horror, two more pit bulls jumped over the fence, joining in the assault. She and Clairoux came running, and he punched the first of the dogs in the head, until it dropped Jayden, and then he threw the boy toward his mother. Hartley fell on her son, protecting him with her body. “JoAnn!” Clairoux cried out, as all three dogs descended on his wife. “Cover your neck, cover your neck.” A neighbor, sitting by her window, screamed for help. Her partner and a friend, Mario Gauthier, ran outside. A neighborhood boy grabbed his hockey stick and threw it to Gauthier. He began hitting one of the dogs over the head, until the stick broke. “They wouldn’t stop,” Gauthier said. “As soon as you’d stop, they’d attack again. I’ve never seen a dog go so crazy. They were like Tasmanian devils.” The police came. The dogs were pulled away, and the Clairouxes and one of the rescuers were taken to the hospital. Five days later, the Ontario legislature banned the ownership of pit bulls. “Just as we wouldn’t let a great white shark in a swimming pool,” the province’s attorney general, Michael Bryant, had said, “maybe we shouldn’t have these animals on the civilized streets.”

Pit bulls, descendants of the bulldogs used in the nineteenth century for bull baiting and dogfighting, have been bred for “gameness,” and thus a lowered inhibition to aggression. Most dogs fight as a last resort, when staring and growling fail. A pit bull is willing to fight with little or no provocation. Pit bulls seem to have a high tolerance for pain, making it possible for them to fight to the point of exhaustion. Whereas guard dogs like German shepherds usually attempt to restrain those they perceive to be threats by biting and holding, pit bulls try to inflict the maximum amount of damage on an opponent. They bite, hold, shake, and tear. They don’t growl or assume an aggressive facial expression as warning. They just attack. “They are often insensitive to behaviors that usually stop aggression,” one scientific review of the breed states. “For example, dogs not bred for fighting usually display defeat in combat by rolling over and exposing a light underside. On several occasions, pit bulls have been reported to disembowel dogs offering this signal of submission.” In epidemiological studies of dog bites, the pit bull is overrepresented among dogs known to have seriously injured or killed human beings, and, as a result, pit bulls have been banned or restricted in several Western European countries, China, and numerous cities and municipalities across North America. Pit bulls are dangerous.

Of course, not all pit bulls are dangerous. Most don’t bite anyone. Meanwhile, Dobermans and Great Danes and German shepherds and Rottweilers are frequent biters as well, and the dog that recently mauled a Frenchwoman so badly that she was given the world’s first face transplant was, of all things, a Labrador retriever. When we say that pit bulls are dangerous, we are making a generalization, just as insurance companies use generalizations when they charge young men more for car insurance than the rest of us (even though many young men are perfectly good drivers), and doctors use generalizations when they tell overweight middle-aged men to get their cholesterol checked (even though many overweight middle-aged men won’t experience heart trouble). Because we don’t know which dog will bite someone or who will have a heart attack or which drivers will get in an accident, we can make predictions only by generalizing. As the legal scholar Frederick Schauer has observed, “painting with a broad brush” is “an often inevitable and frequently desirable dimension of our decision-making lives.”

Another word for generalization, though, is “stereotype,” and stereotypes are usually not considered desirable dimensions of our decision-making lives. The process of moving from the specific to the general is both necessary and perilous. A doctor could, with some statistical support, generalize about men of a certain age and weight. But what if generalizing from other traits—such as high blood pressure, family history, and smoking—saved more lives? Behind each generalization is a choice of what factors to leave in and what factors to leave out, and those choices can prove surprisingly complicated. After the attack on Jayden Clairoux, the Ontario government chose to make a generalization about pit bulls. But it could also have chosen to generalize about powerful dogs, or about the kinds of people who own powerful dogs, or about small children, or about back-yard fences—or, indeed, about any number of other things to do with dogs and people and places. How do we know when we’ve made the right generalization?


In July of last year, following the transit bombings in London, the New York City Police Department announced that it would send officers into the subways to conduct random searches of passengers’ bags. On the face of it, doing random searches in the hunt for terrorists—as opposed to being guided by generalizations—seems like a silly idea. As a columnist in New York wrote at the time, “Not just ‘most’ but nearly every jihadi who has attacked a Western European or American target is a young Arab or Pakistani man. In other words, you can predict with a fair degree of certainty what an Al Qaeda terrorist looks like. Just as we have always known what Mafiosi look like—even as we understand that only an infinitesimal fraction of Italian-Americans are members of the mob.”

But wait: do we really know what mafiosi look like? In “The Godfather,” where most of us get our knowledge of the Mafia, the male members of the Corleone family were played by Marlon Brando, who was of Irish and French ancestry, James Caan, who is Jewish, and two Italian-Americans, Al Pacino and John Cazale. To go by “The Godfather,” mafiosi look like white men of European descent, which, as generalizations go, isn’t terribly helpful. Figuring out what an Islamic terrorist looks like isn’t any easier. Muslims are not like the Amish: they don’t come dressed in identifiable costumes. And they don’t look like basketball players; they don’t come in predictable shapes and sizes. Islam is a religion that spans the globe.

“We have a policy against racial profiling,” Raymond Kelly, New York City’s police commissioner, told me. “I put it in here in March of the first year I was here. It’s the wrong thing to do, and it’s also ineffective. If you look at the London bombings, you have three British citizens of Pakistani descent. You have Germaine Lindsay, who is Jamaican. You have the next crew, on July 21st, who are East African. You have a Chechen woman in Moscow in early 2004 who blows herself up in the subway station. So whom do you profile? Look at New York City. Forty per cent of New Yorkers are born outside the country. Look at the diversity here. Who am I supposed to profile?”

Kelly was pointing out what might be called profiling’s “category problem.” Generalizations involve matching a category of people to a behavior or trait—overweight middle-aged men to heart-attack risk, young men to bad driving. But, for that process to work, you have to be able both to define and to identify the category you are generalizing about. “You think that terrorists aren’t aware of how easy it is to be characterized by ethnicity?” Kelly went on. “Look at the 9/11 hijackers. They came here. They shaved. They went to topless bars. They wanted to blend in. They wanted to look like they were part of the American dream. These are not dumb people. Could a terrorist dress up as a Hasidic Jew and walk into the subway, and not be profiled? Yes. I think profiling is just nuts.”


Pit-bull bans involve a category problem, too, because pit bulls, as it happens, aren’t a single breed. The name refers to dogs belonging to a number of related breeds, such as the American Staffordshire terrier, the Staffordshire bull terrier, and the American pit bull terrier—all of which share a square and muscular body, a short snout, and a sleek, short-haired coat. Thus the Ontario ban prohibits not only these three breeds but any “dog that has an appearance and physical characteristics that are substantially similar” to theirs; the term of art is “pit bull-type” dogs. But what does that mean? Is a cross between an American pit bull terrier and a golden retriever a pit bull-type dog or a golden retriever-type dog? If thinking about muscular terriers as pit bulls is a generalization, then thinking about dangerous dogs as anything substantially similar to a pit bull is a generalization about a generalization. “The way a lot of these laws are written, pit bulls are whatever they say they are,” Lora Brashears, a kennel manager in Pennsylvania, says. “And for most people it just means big, nasty, scary dog that bites.”

The goal of pit-bull bans, obviously, isn’t to prohibit dogs that look like pit bulls. The pit-bull appearance is a proxy for the pit-bull temperament—for some trait that these dogs share. But “pit bullness” turns out to be elusive as well. The supposedly troublesome characteristics of the pit-bull type—its gameness, its determination, its insensitivity to pain—are chiefly directed toward other dogs. Pit bulls were not bred to fight humans. On the contrary: a dog that went after spectators, or its handler, or the trainer, or any of the other people involved in making a dogfighting dog a good dogfighter was usually put down. (The rule in the pit-bull world was “Man-eaters die.”)

A Georgia-based group called the American Temperament Test Society has put twenty-five thousand dogs through a ten-part standardized drill designed to assess a dog’s stability, shyness, aggressiveness, and friendliness in the company of people. A handler takes a dog on a six-foot lead and judges its reaction to stimuli such as gunshots, an umbrella opening, and a weirdly dressed stranger approaching in a threatening way. Eighty-four per cent of the pit bulls that have been given the test have passed, which ranks pit bulls ahead of beagles, Airedales, bearded collies, and all but one variety of dachshund. “We have tested somewhere around a thousand pit-bull-type dogs,” Carl Herkstroeter, the president of the A.T.T.S., says. “I’ve tested half of them. And of the number I’ve tested I have disqualified one pit bull because of aggressive tendencies. They have done extremely well. They have a good temperament. They are very good with children.” It can even be argued that the same traits that make the pit bull so aggressive toward other dogs are what make it so nice to humans. “There are a lot of pit bulls these days who are licensed therapy dogs,” the writer Vicki Hearne points out. “Their stability and resoluteness make them excellent for work with people who might not like a more bouncy, flibbertigibbet sort of dog. When pit bulls set out to provide comfort, they are as resolute as they are when they fight, but what they are resolute about is being gentle. And, because they are fearless, they can be gentle with anybody.”

Then which are the pit bulls that get into trouble? “The ones that the legislation is geared toward have aggressive tendencies that are either bred in by the breeder, trained in by the trainer, or reinforced in by the owner,” Herkstroeter says. A mean pit bull is a dog that has been turned mean, by selective breeding, by being cross-bred with a bigger, human-aggressive breed like German shepherds or Rottweilers, or by being conditioned in such a way that it begins to express hostility to human beings. A pit bull is dangerous to people, then, not to the extent that it expresses its essential pit bullness but to the extent that it deviates from it. A pit-bull ban is a generalization about a generalization about a trait that is not, in fact, general. That’s a category problem.


One of the puzzling things about New York City is that, after the enormous and well-publicized reductions in crime in the mid-nineteen-nineties, the crime rate has continued to fall. In the past two years, for instance, murder in New York has declined by almost ten per cent, rape by twelve per cent, and burglary by more than eighteen per cent. Just in the last year, auto theft went down 11.8 per cent. On a list of two hundred and forty cities in the United States with a population of a hundred thousand or more, New York City now ranks two hundred-and-twenty-second in crime, down near the bottom with Fontana, California, and Port St. Lucie, Florida. In the nineteen-nineties, the crime decrease was attributed to big obvious changes in city life and government—the decline of the drug trade, the gentrification of Brooklyn, the successful implementation of “broken windows” policing. But all those big changes happened a decade ago. Why is crime still falling?

The explanation may have to do with a shift in police tactics. The N.Y.P.D. has a computerized map showing, in real time, precisely where serious crimes are being reported, and at any moment the map typically shows a few dozen constantly shifting high-crime hot spots, some as small as two or three blocks square. What the N.Y.P.D. has done, under Commissioner Kelly, is to use the map to establish “impact zones,” and to direct newly graduated officers—who used to be distributed proportionally to precincts across the city—to these zones, in some cases doubling the number of officers in the immediate neighborhood. “We took two-thirds of our graduating class and linked them with experienced officers, and focussed on those areas,” Kelly said. “Well, what has happened is that over time we have averaged about a thirty-five-per-cent crime reduction in impact zones.”

For years, experts have maintained that the incidence of violent crime is “inelastic” relative to police presence—that people commit serious crimes because of poverty and psychopathology and cultural dysfunction, along with spontaneous motives and opportunities. The presence of a few extra officers down the block, it was thought, wouldn’t make much difference. But the N.Y.P.D. experience suggests otherwise. More police means that some crimes are prevented, others are more easily solved, and still others are displaced—pushed out of the troubled neighborhood—which Kelly says is a good thing, because it disrupts the patterns and practices and social networks that serve as the basis for lawbreaking. In other words, the relation between New York City (a category) and criminality (a trait) is unstable, and this kind of instability is another way in which our generalizations can be derailed.

Why, for instance, is it a useful rule of thumb that Kenyans are good distance runners? It’s not just that it’s statistically supportable today. It’s that it has been true for almost half a century, and that in Kenya the tradition of distance running is sufficiently rooted that something cataclysmic would have to happen to dislodge it. By contrast, the generalization that New York City is a crime-ridden place was once true and now, manifestly, isn’t. People who moved to sunny retirement communities like Port St. Lucie because they thought they were much safer than New York are suddenly in the position of having made the wrong bet.

The instability issue is a problem for profiling in law enforcement as well. The law professor David Cole once tallied up some of the traits that Drug Enforcement Administration agents have used over the years in making generalizations about suspected smugglers. Here is a sample:

Arrived late at night; arrived early in the morning; arrived in afternoon; one of the first to deplane; one of the last to deplane; deplaned in the middle; purchased ticket at the airport; made reservation on short notice; bought coach ticket; bought first-class ticket; used one-way ticket; used round-trip ticket; paid for ticket with cash; paid for ticket with small denomination currency; paid for ticket with large denomination currency; made local telephone calls after deplaning; made long distance telephone call after deplaning; pretended to make telephone call; traveled from New York to Los Angeles; traveled to Houston; carried no luggage; carried brand-new luggage; carried a small bag; carried a medium-sized bag; carried two bulky garment bags; carried two heavy suitcases; carried four pieces of luggage; overly protective of luggage; disassociated self from luggage; traveled alone; traveled with a companion; acted too nervous; acted too calm; made eye contact with officer; avoided making eye contact with officer; wore expensive clothing and jewelry; dressed casually; went to restroom after deplaning; walked rapidly through airport; walked slowly through airport; walked aimlessly through airport; left airport by taxi; left airport by limousine; left airport by private car; left airport by hotel courtesy van.

Some of these reasons for suspicion are plainly absurd, suggesting that there’s no particular rationale to the generalizations used by D.E.A. agents in stopping suspected drug smugglers. A way of making sense of the list, though, is to think of it as a catalogue of unstable traits. Smugglers may once have tended to buy one-way tickets in cash and carry two bulky suitcases. But they don’t have to. They can easily switch to round-trip tickets bought with a credit card, or a single carry-on bag, without losing their capacity to smuggle. There’s a second kind of instability here as well. Maybe the reason some of them switched from one-way tickets and two bulky suitcases was that law enforcement got wise to those habits, so the smugglers did the equivalent of what the jihadis seemed to have done in London, when they switched to East Africans because the scrutiny of young Arab and Pakistani men grew too intense. It doesn’t work to generalize about a relationship between a category and a trait when that relationship isn’t stable—or when the act of generalizing may itself change the basis of the generalization.

Before Kelly became the New York police commissioner, he served as the head of the U.S. Customs Service, and while he was there he overhauled the criteria that border-control officers use to identify and search suspected smugglers. There had been a list of forty-three suspicious traits. He replaced it with a list of six broad criteria. Is there something suspicious about their physical appearance? Are they nervous? Is there specific intelligence targeting this person? Does the drug-sniffing dog raise an alarm? Is there something amiss in their paperwork or explanations? Has contraband been found that implicates this person?

You’ll find nothing here about race or gender or ethnicity, and nothing here about expensive jewelry or deplaning at the middle or the end, or walking briskly or walking aimlessly. Kelly removed all the unstable generalizations, forcing customs officers to make generalizations about things that don’t change from one day or one month to the next. Some percentage of smugglers will always be nervous, will always get their story wrong, and will always be caught by the dogs. That’s why those kinds of inferences are more reliable than the ones based on whether smugglers are white or black, or carry one bag or two. After Kelly’s reforms, the number of searches conducted by the Customs Service dropped by about seventy-five per cent, but the number of successful seizures improved by twenty-five per cent. The officers went from making fairly lousy decisions about smugglers to making pretty good ones. “We made them more efficient and more effective at what they were doing,” Kelly said.


Does the notion of a pit-bull menace rest on a stable or an unstable generalization? The best data we have on breed dangerousness are fatal dog bites, which serve as a useful indicator of just how much havoc certain kinds of dogs are causing. Between the late nineteen-seventies and the late nineteen-nineties, more than twenty-five breeds were involved in fatal attacks in the United States. Pit-bull breeds led the pack, but the variability from year to year is considerable. For instance, in the period from 1981 to 1982 fatalities were caused by five pit bulls, three mixed breeds, two St. Bernards, two German-shepherd mixes, a pure-bred German shepherd, a husky type, a Doberman, a Chow Chow, a Great Dane, a wolf-dog hybrid, a husky mix, and a pit-bull mix—but no Rottweilers. In 1995 and 1996, the list included ten Rottweilers, four pit bulls, two German shepherds, two huskies, two Chow Chows, two wolf-dog hybrids, two shepherd mixes, a Rottweiler mix, a mixed breed, a Chow Chow mix, and a Great Dane. The kinds of dogs that kill people change over time, because the popularity of certain breeds changes over time. The one thing that doesn’t change is the total number of the people killed by dogs. When we have more problems with pit bulls, it’s not necessarily a sign that pit bulls are more dangerous than other dogs. It could just be a sign that pit bulls have become more numerous.

“I’ve seen virtually every breed involved in fatalities, including Pomeranians and everything else, except a beagle or a basset hound,” Randall Lockwood, a senior vice-president of the A.S.P.C.A. and one of the country’s leading dogbite experts, told me. “And there’s always one or two deaths attributable to malamutes or huskies, although you never hear people clamoring for a ban on those breeds. When I first started looking at fatal dog attacks, they largely involved dogs like German shepherds and shepherd mixes and St. Bernards—which is probably why Stephen King chose to make Cujo a St. Bernard, not a pit bull. I haven’t seen a fatality involving a Doberman for decades, whereas in the nineteen-seventies they were quite common. If you wanted a mean dog, back then, you got a Doberman. I don’t think I even saw my first pit-bull case until the middle to late nineteen-eighties, and I didn’t start seeing Rottweilers until I’d already looked at a few hundred fatal dog attacks. Now those dogs make up the preponderance of fatalities. The point is that it changes over time. It’s a reflection of what the dog of choice is among people who want to own an aggressive dog.”

There is no shortage of more stable generalizations about dangerous dogs, though. A 1991 study in Denver, for example, compared a hundred and seventy-eight dogs with a history of biting people with a random sample of a hundred and seventy-eight dogs with no history of biting. The breeds were scattered: German shepherds, Akitas, and Chow Chows were among those most heavily represented. (There were no pit bulls among the biting dogs in the study, because Denver banned pit bulls in 1989.) But a number of other, more stable factors stand out. The biters were 6.2 times as likely to be male than female, and 2.6 times as likely to be intact than neutered. The Denver study also found that biters were 2.8 times as likely to be chained as unchained. “About twenty per cent of the dogs involved in fatalities were chained at the time, and had a history of long-term chaining,” Lockwood said. “Now, are they chained because they are aggressive or aggressive because they are chained? It’s a bit of both. These are animals that have not had an opportunity to become socialized to people. They don’t necessarily even know that children are small human beings. They tend to see them as prey.”

In many cases, vicious dogs are hungry or in need of medical attention. Often, the dogs had a history of aggressive incidents, and, overwhelmingly, dog-bite victims were children (particularly small boys) who were physically vulnerable to attack and may also have unwittingly done things to provoke the dog, like teasing it, or bothering it while it was eating. The strongest connection of all, though, is between the trait of dog viciousness and certain kinds of dog owners. In about a quarter of fatal dog-bite cases, the dog owners were previously involved in illegal fighting. The dogs that bite people are, in many cases, socially isolated because their owners are socially isolated, and they are vicious because they have owners who want a vicious dog. The junk-yard German shepherd—which looks as if it would rip your throat out—and the German-shepherd guide dog are the same breed. But they are not the same dog, because they have owners with different intentions.

“A fatal dog attack is not just a dog bite by a big or aggressive dog,” Lockwood went on. “It is usually a perfect storm of bad human-canine interactions—the wrong dog, the wrong background, the wrong history in the hands of the wrong person in the wrong environmental situation. I’ve been involved in many legal cases involving fatal dog attacks, and, certainly, it’s my impression that these are generally cases where everyone is to blame. You’ve got the unsupervised three-year-old child wandering in the neighborhood killed by a starved, abused dog owned by the dogfighting boyfriend of some woman who doesn’t know where her child is. It’s not old Shep sleeping by the fire who suddenly goes bonkers. Usually there are all kinds of other warning signs.”


Jayden Clairoux was attacked by Jada, a pit-bull terrier, and her two pit-bull–bullmastiff puppies, Agua and Akasha. The dogs were owned by a twenty-one-year-old man named Shridev Café, who worked in construction and did odd jobs. Five weeks before the Clairoux attack, Café’s three dogs got loose and attacked a sixteen-year-old boy and his four-year-old half brother while they were ice skating. The boys beat back the animals with a snow shovel and escaped into a neighbor’s house. Café was fined, and he moved the dogs to his seventeen-year-old girlfriend’s house. This was not the first time that he ran into trouble last year; a few months later, he was charged with domestic assault, and, in another incident, involving a street brawl, with aggravated assault. “Shridev has personal issues,” Cheryl Smith, a canine-behavior specialist who consulted on the case, says. “He’s certainly not a very mature person.” Agua and Akasha were now about seven months old. The court order in the wake of the first attack required that they be muzzled when they were outside the home and kept in an enclosed yard. But Café did not muzzle them, because, he said later, he couldn’t afford muzzles, and apparently no one from the city ever came by to force him to comply. A few times, he talked about taking his dogs to obedience classes, but never did. The subject of neutering them also came up—particularly Agua, the male—but neutering cost a hundred dollars, which he evidently thought was too much money, and when the city temporarily confiscated his animals after the first attack it did not neuter them, either, because Ottawa does not have a policy of preëmptively neutering dogs that bite people.

On the day of the second attack, according to some accounts, a visitor came by the house of Café’s girlfriend, and the dogs got wound up. They were put outside, where the snowbanks were high enough so that the back-yard fence could be readily jumped. Jayden Clairoux stopped and stared at the dogs, saying, “Puppies, puppies.” His mother called out to his father. His father came running, which is the kind of thing that will rile up an aggressive dog. The dogs jumped the fence, and Agua took Jayden’s head in his mouth and started to shake. It was a textbook dog-biting case: unneutered, ill-trained, charged-up dogs, with a history of aggression and an irresponsible owner, somehow get loose, and set upon a small child. The dogs had already passed through the animal bureaucracy of Ottawa, and the city could easily have prevented the second attack with the right kind of generalization—a generalization based not on breed but on the known and meaningful connection between dangerous dogs and negligent owners. But that would have required someone to track down Shridev Café, and check to see whether he had bought muzzles, and someone to send the dogs to be neutered after the first attack, and an animal-control law that insured that those whose dogs attack small children forfeit their right to have a dog. It would have required, that is, a more exacting set of generalizations to be more exactingly applied. It’s always easier just to ban the breed.
Why problems like homelessness may be easier to solve than to manage.


Murray Barr was a bear of a man, doctor an ex-marine, view six feet tall and heavyset, and when he fell down—which he did nearly every day—it could take two or three grown men to pick him up. He had straight black hair and olive skin. On the street, they called him Smokey. He was missing most of his teeth. He had a wonderful smile. People loved Murray.

His chosen drink was vodka. Beer he called “horse piss.” On the streets of downtown Reno, where he lived, he could buy a two-hundred-and-fifty-millilitre bottle of cheap vodka for a dollar-fifty. If he was flush, he could go for the seven-hundred-and-fifty-millilitre bottle, and if he was broke he could always do what many of the other homeless people of Reno did, which is to walk through the casinos and finish off the half-empty glasses of liquor left at the gaming tables.

“If he was on a runner, we could pick him up several times a day,” Patrick O’Bryan, who is a bicycle cop in downtown Reno, said. “And he’s gone on some amazing runners. He would get picked up, get detoxed, then get back out a couple of hours later and start up again. A lot of the guys on the streets who’ve been drinking, they get so angry. They are so incredibly abrasive, so violent, so abusive. Murray was such a character and had such a great sense of humor that we somehow got past that. Even when he was abusive, we’d say, ‘Murray, you know you love us,’ and he’d say, ‘I know—and go back to swearing at us.”

“I’ve been a police officer for fifteen years,” O’Bryan’s partner, Steve Johns, said. “I picked up Murray my whole career. Literally.”

Johns and O’Bryan pleaded with Murray to quit drinking. A few years ago, he was assigned to a treatment program in which he was under the equivalent of house arrest, and he thrived. He got a job and worked hard. But then the program ended. “Once he graduated out, he had no one to report to, and he needed that,” O’Bryan said. “I don’t know whether it was his military background. I suspect that it was. He was a good cook. One time, he accumulated savings of over six thousand dollars. Showed up for work religiously. Did everything he was supposed to do. They said, ‘Congratulations,’ and put him back on the street. He spent that six thousand in a week or so.”

Often, he was too intoxicated for the drunk tank at the jail, and he’d get sent to the emergency room at either Saint Mary’s or Washoe Medical Center. Marla Johns, who was a social worker in the emergency room at Saint Mary’s, saw him several times a week. “The ambulance would bring him in. We would sober him up, so he would be sober enough to go to jail. And we would call the police to pick him up. In fact, that’s how I met my husband.” Marla Johns is married to Steve Johns.

“He was like the one constant in an environment that was ever changing,” she went on. “In he would come. He would grin that half-toothless grin. He called me ‘my angel.’ I would walk in the room, and he would smile and say, ‘Oh, my angel, I’m so happy to see you.’ We would joke back and forth, and I would beg him to quit drinking and he would laugh it off. And when time went by and he didn’t come in I would get worried and call the coroner’s office. When he was sober, we would find out, oh, he’s working someplace, and my husband and I would go and have dinner where he was working. When my husband and I were dating, and we were going to get married, he said, ‘Can I come to the wedding?’ And I almost felt like he should. My joke was ‘If you are sober you can come, because I can’t afford your bar bill.’ When we started a family, he would lay a hand on my pregnant belly and bless the child. He really was this kind of light.”

In the fall of 2003, the Reno Police Department started an initiative designed to limit panhandling in the downtown core. There were articles in the newspapers, and the police department came under harsh criticism on local talk radio. The crackdown on panhandling amounted to harassment, the critics said. The homeless weren’t an imposition on the city; they were just trying to get by. “One morning, I’m listening to one of the talk shows, and they’re just trashing the police department and going on about how unfair it is,” O’Bryan said. “And I thought, Wow, I’ve never seen any of these critics in one of the alleyways in the middle of the winter looking for bodies.” O’Bryan was angry. In downtown Reno, food for the homeless was plentiful: there was a Gospel kitchen and Catholic Services, and even the local McDonald’s fed the hungry. The panhandling was for liquor, and the liquor was anything but harmless. He and Johns spent at least half their time dealing with people like Murray; they were as much caseworkers as police officers. And they knew they weren’t the only ones involved. When someone passed out on the street, there was a “One down” call to the paramedics. There were four people in an ambulance, and the patient sometimes stayed at the hospital for days, because living on the streets in a state of almost constant intoxication was a reliable way of getting sick. None of that, surely, could be cheap.

O’Bryan and Johns called someone they knew at an ambulance service and then contacted the local hospitals. “We came up with three names that were some of our chronic inebriates in the downtown area, that got arrested the most often,” O’Bryan said. “We tracked those three individuals through just one of our two hospitals. One of the guys had been in jail previously, so he’d only been on the streets for six months. In those six months, he had accumulated a bill of a hundred thousand dollars—and that’s at the smaller of the two hospitals near downtown Reno. It’s pretty reasonable to assume that the other hospital had an even larger bill. Another individual came from Portland and had been in Reno for three months. In those three months, he had accumulated a bill for sixty-five thousand dollars. The third individual actually had some periods of being sober, and had accumulated a bill of fifty thousand.”

The first of those people was Murray Barr, and Johns and O’Bryan realized that if you totted up all his hospital bills for the ten years that he had been on the streets—as well as substance-abuse-treatment costs, doctors’ fees, and other expenses—Murray Barr probably ran up a medical bill as large as anyone in the state of Nevada.

“It cost us one million dollars not to do something about Murray,” O’Bryan said.


Fifteen years ago, after the Rodney King beating, the Los Angeles Police Department was in crisis. It was accused of racial insensitivity and ill discipline and violence, and the assumption was that those problems had spread broadly throughout the rank and file. In the language of statisticians, it was thought that L.A.P.D.’s troubles had a “normal” distribution—that if you graphed them the result would look like a bell curve, with a small number of officers at one end of the curve, a small number at the other end, and the bulk of the problem situated in the middle. The bell-curve assumption has become so much a part of our mental architecture that we tend to use it to organize experience automatically.

But when the L.A.P.D. was investigated by a special commission headed by Warren Christopher, a very different picture emerged. Between 1986 and 1990, allegations of excessive force or improper tactics were made against eighteen hundred of the eighty-five hundred officers in the L.A.P.D. The broad middle had scarcely been accused of anything. Furthermore, more than fourteen hundred officers had only one or two allegations made against them—and bear in mind that these were not proven charges, that they happened in a four-year period, and that allegations of excessive force are an inevitable feature of urban police work. (The N.Y.P.D. receives about three thousand such complaints a year.) A hundred and eighty-three officers, however, had four or more complaints against them, forty-four officers had six or more complaints, sixteen had eight or more, and one had sixteen complaints. If you were to graph the troubles of the L.A.P.D., it wouldn’t look like a bell curve. It would look more like a hockey stick. It would follow what statisticians call a “power law” distribution—where all the activity is not in the middle but at one extreme.

The Christopher Commission’s report repeatedly comes back to what it describes as the extreme concentration of problematic officers. One officer had been the subject of thirteen allegations of excessive use of force, five other complaints, twenty-eight “use of force reports” (that is, documented, internal accounts of inappropriate behavior), and one shooting. Another had six excessive-force complaints, nineteen other complaints, ten use-of-force reports, and three shootings. A third had twenty-seven use-of-force reports, and a fourth had thirty-five. Another had a file full of complaints for doing things like “striking an arrestee on the back of the neck with the butt of a shotgun for no apparent reason while the arrestee was kneeling and handcuffed,” beating up a thirteen-year-old juvenile, and throwing an arrestee from his chair and kicking him in the back and side of the head while he was handcuffed and lying on his stomach.

The report gives the strong impression that if you fired those forty-four cops the L.A.P.D. would suddenly become a pretty well-functioning police department. But the report also suggests that the problem is tougher than it seems, because those forty-four bad cops were so bad that the institutional mechanisms in place to get rid of bad apples clearly weren’t working. If you made the mistake of assuming that the department’s troubles fell into a normal distribution, you’d propose solutions that would raise the performance of the middle—like better training or better hiring—when the middle didn’t need help. For those hard-core few who did need help, meanwhile, the medicine that helped the middle wouldn’t be nearly strong enough.

In the nineteen-eighties, when homelessness first surfaced as a national issue, the assumption was that the problem fit a normal distribution: that the vast majority of the homeless were in the same state of semi-permanent distress. It was an assumption that bred despair: if there were so many homeless, with so many problems, what could be done to help them? Then, fifteen years ago, a young Boston College graduate student named Dennis Culhane lived in a shelter in Philadelphia for seven weeks as part of the research for his dissertation. A few months later he went back, and was surprised to discover that he couldn’t find any of the people he had recently spent so much time with. “It made me realize that most of these people were getting on with their own lives,” he said.

Culhane then put together a database—the first of its kind—to track who was coming in and out of the shelter system. What he discovered profoundly changed the way homelessness is understood. Homelessness doesn’t have a normal distribution, it turned out. It has a power-law distribution. “We found that eighty per cent of the homeless were in and out really quickly,” he said. “In Philadelphia, the most common length of time that someone is homeless is one day. And the second most common length is two days. And they never come back. Anyone who ever has to stay in a shelter involuntarily knows that all you think about is how to make sure you never come back.”

The next ten per cent were what Culhane calls episodic users. They would come for three weeks at a time, and return periodically, particularly in the winter. They were quite young, and they were often heavy drug users. It was the last ten per cent—the group at the farthest edge of the curve—that interested Culhane the most. They were the chronically homeless, who lived in the shelters, sometimes for years at a time. They were older. Many were mentally ill or physically disabled, and when we think about homelessness as a social problem—the people sleeping on the sidewalk, aggressively panhandling, lying drunk in doorways, huddled on subway grates and under bridges—it’s this group that we have in mind. In the early nineteen-nineties, Culhane’s database suggested that New York City had a quarter of a million people who were homeless at some point in the previous half decade —which was a surprisingly high number. But only about twenty-five hundred were chronically homeless.

It turns out, furthermore, that this group costs the health-care and social-services systems far more than anyone had ever anticipated. Culhane estimates that in New York at least sixty-two million dollars was being spent annually to shelter just those twenty-five hundred hard-core homeless. “It costs twenty-four thousand dollars a year for one of these shelter beds,” Culhane said. “We’re talking about a cot eighteen inches away from the next cot.” Boston Health Care for the Homeless Program, a leading service group for the homeless in Boston, recently tracked the medical expenses of a hundred and nineteen chronically homeless people. In the course of five years, thirty-three people died and seven more were sent to nursing homes, and the group still accounted for 18,834 emergency-room visits—at a minimum cost of a thousand dollars a visit. The University of California, San Diego Medical Center followed fifteen chronically homeless inebriates and found that over eighteen months those fifteen people were treated at the hospital’s emergency room four hundred and seventeen times, and ran up bills that averaged a hundred thousand dollars each. One person—San Diego’s counterpart to Murray Barr—came to the emergency room eighty-seven times.

“If it’s a medical admission, it’s likely to be the guys with the really complex pneumonia,” James Dunford, the city of San Diego’s emergency medical director and the author of the observational study, said. “They are drunk and they aspirate and get vomit in their lungs and develop a lung abscess, and they get hypothermia on top of that, because they’re out in the rain. They end up in the intensive-care unit with these very complicated medical infections. These are the guys who typically get hit by cars and buses and trucks. They often have a neurosurgical catastrophe as well. So they are very prone to just falling down and cracking their head and getting a subdural hematoma, which, if not drained, could kill them, and it’s the guy who falls down and hits his head who ends up costing you at least fifty thousand dollars. Meanwhile, they are going through alcoholic withdrawal and have devastating liver disease that only adds to their inability to fight infections. There is no end to the issues. We do this huge drill. We run up big lab fees, and the nurses want to quit, because they see the same guys come in over and over, and all we’re doing is making them capable of walking down the block.”

The homelessness problem is like the L.A.P.D.’s bad-cop problem. It’s a matter of a few hard cases, and that’s good news, because when a problem is that concentrated you can wrap your arms around it and think about solving it. The bad news is that those few hard cases are hard. They are falling-down drunks with liver disease and complex infections and mental illness. They need time and attention and lots of money. But enormous sums of money are already being spent on the chronically homeless, and Culhane saw that the kind of money it would take to solve the homeless problem could well be less than the kind of money it took to ignore it. Murray Barr used more health-care dollars, after all, than almost anyone in the state of Nevada. It would probably have been cheaper to give him a full-time nurse and his own apartment.

The leading exponent for the power-law theory of homelessness is Philip Mangano, who, since he was appointed by President Bush in 2002, has been the executive director of the U.S. Interagency Council on Homelessness, a group that oversees the programs of twenty federal agencies. Mangano is a slender man, with a mane of white hair and a magnetic presence, who got his start as an advocate for the homeless in Massachusetts. In the past two years, he has crisscrossed the United States, educating local mayors and city councils about the real shape of the homelessness curve. Simply running soup kitchens and shelters, he argues, allows the chronically homeless to remain chronically homeless. You build a shelter and a soup kitchen if you think that homelessness is a problem with a broad and unmanageable middle. But if it’s a problem at the fringe it can be solved. So far, Mangano has convinced more than two hundred cities to radically reëvaluate their policy for dealing with the homeless.

“I was in St. Louis recently,” Mangano said, back in June, when he dropped by New York on his way to Boise, Idaho. “I spoke with people doing services there. They had a very difficult group of people they couldn’t reach no matter what they offered. So I said, Take some of your money and rent some apartments and go out to those people, and literally go out there with the key and say to them, ‘This is the key to an apartment. If you come with me right now I am going to give it to you, and you are going to have that apartment.’ And so they did. And one by one those people were coming in. Our intent is to take homeless policy from the old idea of funding programs that serve homeless people endlessly and invest in results that actually end homelessness.”

Mangano is a history buff, a man who sometimes falls asleep listening to old Malcolm X speeches, and who peppers his remarks with references to the civil-rights movement and the Berlin Wall and, most of all, the fight against slavery. “I am an abolitionist,” he says. “My office in Boston was opposite the monument to the 54th Regiment on the Boston Common, up the street from the Park Street Church, where William Lloyd Garrison called for immediate abolition, and around the corner from where Frederick Douglass gave that famous speech at the Tremont Temple. It is very much ingrained in me that you do not manage a social wrong. You should be ending it.”


The old Y.M.C.A. in downtown Denver is on Sixteenth Street, just east of the central business district. The main building is a handsome six-story stone structure that was erected in 1906, and next door is an annex that was added in the nineteen-fifties. On the ground floor there is a gym and exercise rooms. On the upper floors there are several hundred apartments—brightly painted one-bedrooms, efficiencies, and S.R.O.-style rooms with microwaves and refrigerators and central airconditioning—and for the past several years those apartments have been owned and managed by the Colorado Coalition for the Homeless.

Even by big-city standards, Denver has a serious homelessness problem. The winters are relatively mild, and the summers aren’t nearly as hot as those of neighboring New Mexico or Utah, which has made the city a magnet for the indigent. By the city’s estimates, it has roughly a thousand chronically homeless people, of whom three hundred spend their time downtown, along the central Sixteenth Street shopping corridor or in nearby Civic Center Park. Many of the merchants downtown worry that the presence of the homeless is scaring away customers. A few blocks north, near the hospital, a modest, low-slung detox center handles twenty-eight thousand admissions a year, many of them homeless people who have passed out on the streets, either from liquor or—as is increasingly the case—from mouthwash. “Dr. ——Dr. Tich, they call it—is the brand of mouthwash they use,” says Roxane White, the manager of the city’s social services. “You can imagine what that does to your gut.”

Eighteen months ago, the city signed up with Mangano. With a mixture of federal and local funds, the C.C.H. inaugurated a new program that has so far enrolled a hundred and six people. It is aimed at the Murray Barrs of Denver, the people costing the system the most. C.C.H. went after the people who had been on the streets the longest, who had a criminal record, who had a problem with substance abuse or mental illness. “We have one individual in her early sixties, but looking at her you’d think she’s eighty,” Rachel Post, the director of substance treatment at the C.C.H., said. (Post changed some details about her clients in order to protect their identity.) “She’s a chronic alcoholic. A typical day for her is she gets up and tries to find whatever ‘s going to drink that day. She falls down a lot. There’s another person who came in during the first week. He was on methadone maintenance. He’d had psychiatric treatment. He was incarcerated for eleven years, and lived on the streets for three years after that, and, if that’s not enough, he had a hole in his heart.”

The recruitment strategy was as simple as the one that Mangano had laid out in St. Louis: Would you like a free apartment? The enrollees got either an efficiency at the Y.M.C.A. or an apartment rented for them in a building somewhere else in the city, provided they agreed to work within the rules of the program. In the basement of the Y, where the racquetball courts used to be, the coalition built a command center, staffed with ten caseworkers. Five days a week, between eight-thirty and ten in the morning, the caseworkers meet and painstakingly review the status of everyone in the program. On the wall around the conference table are several large white boards, with lists of doctor’s appointments and court dates and medication schedules. “We need a staffing ratio of one to ten to make it work,” Post said. “You go out there and you find people and assess how ‘re doing in their residence. Sometimes we’re in contact with someone every day. Ideally, we want to be in contact every couple of days. We’ve got about fifteen people we’re really worried about now.”

The cost of services comes to about ten thousand dollars per homeless client per year. An efficiency apartment in Denver averages $376 a month, or just over forty-five hundred a year, which means that you can house and care for a chronically homeless person for at most fifteen thousand dollars, or about a third of what he or she would cost on the street. The idea is that once the people in the program get stabilized they will find jobs, and start to pick up more and more of their own rent, which would bring someone’s annual cost to the program closer to six thousand dollars. As of today, seventy-five supportive housing slots have already been added, and the city’s homeless plan calls for eight hundred more over the next ten years.

The reality, of course, is hardly that neat and tidy. The idea that the very sickest and most troubled of the homeless can be stabilized and eventually employed is only a hope. Some of them plainly won’t be able to get there: these are, after all, hard cases. “We’ve got one man, he’s in his twenties,” Post said. “Already, he has cirrhosis of the liver. One time he blew a blood alcohol of .49, which is enough to kill most people. The first place we had he brought over all his friends, and they partied and trashed the place and broke a window. Then we gave him another apartment, and he did the same thing.”

Post said that the man had been sober for several months. But he could relapse at some point and perhaps trash another apartment, and they’d have to figure out what to do with him next. Post had just been on a conference call with some people in New York City who run a similar program, and they talked about whether giving clients so many chances simply encourages them to behave irresponsibly. For some people, it probably does. But what was the alternative? If this young man was put back on the streets, he would cost the system even more money. The current philosophy of welfare holds that government assistance should be temporary and conditional, to avoid creating dependency. But someone who blows .49 on a Breathalyzer and has cirrhosis of the liver at the age of twenty-seven doesn’t respond to incentives and sanctions in the usual way. “The most complicated people to work with are those who have been homeless for so long that going back to the streets just isn’t scary to them,” Post said. “The summer comes along and they say, ‘I don’t need to follow your rules.’ ” Power-law homelessness policy has to do the opposite of normal-distribution social policy. It should create dependency: you want people who have been outside the system to come inside and rebuild their lives under the supervision of those ten caseworkers in the basement of the Y.M.C.A.

That is what is so perplexing about power-law homeless policy. From an economic perspective the approach makes perfect sense. But from a moral perspective it doesn’t seem fair. Thousands of people in the Denver area no doubt live day to day, work two or three jobs, and are eminently deserving of a helping hand—and no one offers them the key to a new apartment. Yet that’s just what the guy screaming obscenities and swigging Dr. Tich gets. When the welfare mom’s time on public assistance runs out, we cut her off. Yet when the homeless man trashes his apartment we give him another. Social benefits are supposed to have some kind of moral justification. We give them to widows and disabled veterans and poor mothers with small children. Giving the homeless guy passed out on the sidewalk an apartment has a different rationale. It’s simply about efficiency.

We also believe that the distribution of social benefits should not be arbitrary. We don’t give only to some poor mothers, or to a random handful of disabled veterans. We give to everyone who meets a formal criterion, and the moral credibility of government assistance derives, in part, from this universality. But the Denver homelessness program doesn’t help every chronically homeless person in Denver. There is a waiting list of six hundred for the supportive-housing program; it will be years before all those people get apartments, and some may never get one. There isn’t enough money to go around, and to try to help everyone a little bit—to observe the principle of universality—isn’t as cost-effective as helping a few people a lot. Being fair, in this case, means providing shelters and soup kitchens, and shelters and soup kitchens don’t solve the problem of homelessness. Our usual moral intuitions are little use, then, when it comes to a few hard cases. Power-law problems leave us with an unpleasant choice. We can be true to our principles or we can fix the problem. We cannot do both.


A few miles northwest of the old Y.M.C.A. in downtown Denver, on the Speer Boulevard off-ramp from I-25, there is a big electronic sign by the side of the road, connected to a device that remotely measures the emissions of the vehicles driving past. When a car with properly functioning pollution-control equipment passes, the sign flashes “Good.” When a car passes that is well over the acceptable limits, the sign flashes “Poor.” If you stand at the Speer Boulevard exit and watch the sign for any length of time, you’ll find that virtually every car scores “Good.” An Audi A4 —”Good.” A Buick Century—”Good.” A Toyota Corolla—”Good.” A Ford Taurus—”Good.” A Saab 9-5—”Good,” and on and on, until after twenty minutes or so, some beat-up old Ford Escort or tricked-out Porsche drives by and the sign flashes “Poor.” The picture of the smog problem you get from watching the Speer Boulevard sign and the picture of the homelessness problem you get from listening in on the morning staff meetings at the Y.M.C.A. are pretty much the same. Auto emissions follow a power-law distribution, and the air-pollution example offers another look at why we struggle so much with problems centered on a few hard cases.

Most cars, especially new ones, are extraordinarily clean. A 2004 Subaru in good working order has an exhaust stream that’s just .06 per cent carbon monoxide, which is negligible. But on almost any highway, for whatever reason—age, ill repair, deliberate tampering by the owner—a small number of cars can have carbon-monoxide levels in excess of ten per cent, which is almost two hundred times higher. In Denver, five per cent of the vehicles on the road produce fifty-five per cent of the automobile pollution.

“Let’s say a car is fifteen years old,” Donald Stedman says. Stedman is a chemist and automobile-emissions specialist at the University of Denver. His laboratory put up the sign on Speer Avenue. “Obviously, the older a car is the more likely it is to become broken. It’s the same as human beings. And by broken we mean any number of mechanical malfunctions—the computer’s not working anymore, fuel injection is stuck open, the catalyst ‘s not unusual that these failure modes result in high emissions. We have at least one car in our database which was emitting seventy grams of hydrocarbon per mile, which means that you could almost drive a Honda Civic on the exhaust fumes from that car. It’s not just old cars. It’s new cars with high mileage, like taxis. One of the most successful and least publicized control measures was done by a district attorney in L.A. back in the nineties. He went to LAX and discovered that all of the Bell Cabs were gross emitters. One of those cabs emitted more than its own weight of pollution every year.”

In Stedman’s view, the current system of smog checks makes little sense. A million motorists in Denver have to go to an emissions center every year—take time from work, wait in line, pay fifteen or twenty-five dollars—for a test that more than ninety per cent of them don’t need. “Not everybody gets tested for breast cancer,” Stedman says. “Not everybody takes an AIDS test.” On-site smog checks, furthermore, do a pretty bad job of finding and fixing the few outliers. Car enthusiasts—with high-powered, high-polluting sports cars—have been known to drop a clean engine into their car on the day they get it tested. Others register their car in a faraway town without emissions testing or arrive at the test site “hot”—having just come off hard driving on the freeway—which is a good way to make a dirty engine appear to be clean. Still others randomly pass the test when they shouldn’t, because dirty engines are highly variable and sometimes burn cleanly for short durations. There is little evidence, Stedman says, that the city’s regime of inspections makes any difference in air quality.

He proposes mobile testing instead. Twenty years ago, he invented a device the size of a suitcase that uses infrared light to instantly measure and then analyze the emissions of cars as they drive by on the highway. The Speer Avenue sign is attached to one of Stedman’s devices. He says that cities should put half a dozen or so of his devices in vans, park them on freeway off-ramps around the city, and have a police car poised to pull over anyone who fails the test. A half-dozen vans could test thirty thousand cars a day. For the same twenty-five million dollars that Denver’s motorists now spend on on-site testing, Stedman estimates, the city could identify and fix twenty-five thousand truly dirty vehicles every year, and within a few years cut automobile emissions in the Denver metropolitan area by somewhere between thirty-five and forty per cent. The city could stop managing its smog problem and start ending it.

Why don’t we all adopt the Stedman method? There’s no moral impediment here. We’re used to the police pulling people over for having a blown headlight or a broken side mirror, and it wouldn’t be difficult to have them add pollution-control devices to their list. Yet it does run counter to an instinctive social preference for thinking of pollution as a problem to which we all contribute equally. We have developed institutions that move reassuringly quickly and forcefully on collective problems. Congress passes a law. The Environmental Protection Agency promulgates a regulation. The auto industry makes its cars a little cleaner, and—presto—the air gets better. But Stedman doesn’t much care about what happens in Washington and Detroit. The challenge of controlling air pollution isn’t so much about the laws as it is about compliance with them. It’s a policing problem, rather than a policy problem, and there is something ultimately unsatisfying about his proposed solution. He wants to end air pollution in Denver with a half-dozen vans outfitted with a contraption about the size of a suitcase. Can such a big problem have such a small-bore solution?

That’s what made the findings of the Christopher Commission so unsatisfying. We put together blue-ribbon panels when we’re faced with problems that seem too large for the normal mechanisms of bureaucratic repair. We want sweeping reforms. But what was the commission’s most memorable observation? It was the story of an officer with a known history of doing things like beating up handcuffed suspects who nonetheless received a performance review from his superior stating that he “usually conducts himself in a manner that inspires respect for the law and instills public confidence.” This is what you say about an officer when you haven’t actually read his file, and the implication of the Christopher Commission’s report was that the L.A.P.D. might help solve its problem simply by getting its police captains to read the files of their officers. The L.A.P.D.’s problem was a matter not of policy but of compliance. The department needed to adhere to the rules it already had in place, and that’s not what a public hungry for institutional transformation wants to hear. Solving problems that have power-law distributions doesn’t just violate our moral intuitions; it violates our political intuitions as well. It’s hard not to conclude, in the end, that the reason we treated the homeless as one hopeless undifferentiated group for so long is not simply that we didn’t know better. It’s that we didn’t want to know better. It was easier the old way.

Power-law solutions have little appeal to the right, because they involve special treatment for people who do not deserve special treatment; and they have little appeal to the left, because their emphasis on efficiency over fairness suggests the cold number-crunching of Chicago-school cost-benefit analysis. Even the promise of millions of dollars in savings or cleaner air or better police departments cannot entirely compensate for such discomfort. In Denver, John Hickenlooper, the city’s enormously popular mayor, has worked on the homelessness issue tirelessly during the past couple of years. He spent more time on the subject in his annual State of the City address this past summer than on any other topic. He gave the speech, with deliberate symbolism, in the city’s downtown Civic Center Park, where homeless people gather every day with their shopping carts and garbage bags. He has gone on local talk radio on many occasions to discuss what the city is doing about the issue. He has commissioned studies to show what a drain on the city’s resources the homeless population has become. But, he says, “there are still people who stop me going into the supermarket and say, ‘I can’t believe you’re going to help those homeless people, those bums.'”


Early one morning a year ago, Marla Johns got a call from her husband, Steve. He was at work. “He called and woke me up,” Johns remembers. “He was choked up and crying on the phone. And I thought that something had happened with another police officer. I said, ‘Oh, my gosh, what happened?’ He said, ‘Murray died last night.’ ” He died of intestinal bleeding. At the police department that morning, some of the officers gave Murray a moment of silence.

“There are not many days that go by that I don’t have a thought of him,” she went on. “Christmas comes— and I used to buy him a Christmas present. Make sure he had warm gloves and a blanket and a coat. There was this mutual respect. There was a time when another intoxicated patient jumped off the gurney and was coming at me, and Murray jumped off his gurney and shook his fist and said, ‘Don’t you touch my angel.’ You know, when he was monitored by the system he did fabulously. He would be on house arrest and he would get a job and he would save money and go to work every day, and he wouldn’t drink. He would do all the things he was supposed to do. There are some people who can be very successful members of society if someone monitors them. Murray needed someone to be in charge of him.”

But, of course, Reno didn’t have a place where Murray could be given the structure he needed. Someone must have decided that it cost too much.

“I told my husband that I would claim his body if no one else did,” she said. “I would not have him in an unmarked grave.”
A sociologist offers an anatomy of explanations.


Little Timothy is playing with his older brother Geoffrey, price when he comes running to his mother.

“Mommy, Mommy, ” he starts in. “I was playing with my truck, and then Geoffrey came and he said it was his turn to play with the truck even though it’s my truck and then he pushed me.”

“Timothy!” his mother says, silencing him. “Don’t be a tattletale.”

Timothy has heard that phrase—”Don’t be a tattletale”—countless times, and it always stops him short. He has offered his mother an eyewitness account of a crime. His mother, furthermore, in no way disputes the truth of his story. Yet what does she do? She rejects it in favor of a simplistic social formula: Don’t be a tattletale. It makes no sense. Timothy’s mother would never use such a formula to trump a story if she were talking to his father. On the contrary, his mother and father tattle to each other about Geoffrey all the time. And, if Timothy were to tattle on Geoffrey to his best friend, Bruce, Bruce wouldn’t reject the story in favor of a formula, either. Narratives are the basis of Timothy’s friendship with Bruce. They explain not just effects but causes. They matter—except in this instance, of a story told by Timothy to Mommy about Geoffrey, in which Mommy is suddenly indifferent to stories altogether. What is this don’t-be-a-tattletale business about?

In “Why?” (Princeton; $24.95), the Columbia University scholar Charles Tilly sets out to make sense of our reasons for giving reasons. In the tradition of the legendary sociologist Erving Goffman, Tilly seeks to decode the structure of everyday social interaction, and the result is a book that forces readers to reëxamine everything from the way they talk to their children to the way they argue about politics.

In Tilly’s view, we rely on four general categories of reasons. The first is what he calls conventions—conventionally accepted explanations. Tilly would call “Don’t be a ” a convention. The second is stories, and what distinguishes a story (“I was playing with my truck, and then Geoffrey came in . . .”) is a very specific account of cause and effect. Tilly cites the sociologist Francesca Polletta’s interviews with people who were active in the civil-rights sit-ins of the nineteen-sixties. Polletta repeatedly heard stories that stressed the spontaneity of the protests, leaving out the role of civil-rights organizations, teachers, and churches. That’s what stories do. As Tilly writes, they circumscribe time and space, limit the number of actors and actions, situate all causes “in the consciousness of the actors,” and elevate the personal over the institutional.

Then there are codes, which are high-level conventions, formulas that invoke sometimes recondite procedural rules and categories. If a loan officer turns you down for a mortgage, the reason he gives has to do with your inability to conform to a prescribed standard of creditworthiness. Finally, there are technical accounts: stories informed by specialized knowledge and authority. An academic history of civil-rights sit-ins wouldn’t leave out the role of institutions, and it probably wouldn’t focus on a few actors and actions; it would aim at giving patient and expert attention to every sort of nuance and detail.

Tilly argues that we make two common errors when it comes to understanding reasons. The first is to assume that some kinds of reasons are always better than others—that there is a hierarchy of reasons, with conventions (the least sophisticated) at the bottom and technical accounts at the top. That’s wrong, Tilly says: each type of reason has its own role.

Tilly’s second point flows from the first, and it’s that the reasons people give aren’t a function of their character—that is, there aren’t people who always favor technical accounts and people who always favor stories. Rather, reasons arise out of situations and roles. Imagine, he says, the following possible responses to one person’s knocking some books off the desk of another:

  1. Sorry, buddy. I’m just plain awkward.
  2. I’m sorry. I didn’t see your book.
  3. Nuts! I did it again.
  4. Why did you put that book there?
  5. I told you to stack up your books neatly.

The lesson is not that the kind of person who uses reason No. 1 or No. 2 is polite and the kind of person who uses reason No. 4 or No. 5 is a jerk. The point is that any of us might use any of those five reasons depending on our relation to the person whose books we knocked over. Reason-giving, Tilly says, reflects, establishes, repairs, and negotiates relationships. The husband who uses a story to explain his unhappiness to his wife—”Ever since I got my new job, I feel like I’ve just been so busy that I haven’t had time for us”—is attempting to salvage the relationship. But when he wants out of the marriage, he’ll say, “It’s not you—it’s me.” He switches to a convention. As his wife realizes, it’s not the content of what he has said that matters. It’s his shift from the kind of reason-giving that signals commitment to the kind that signals disengagement. Marriages thrive on stories. They die on conventions.

Consider the orgy of reason-giving that followed Vice-President Dick Cheney’s quail-hunting accident involving his friend Harry Whittington. Allies of the Vice-President insisted that the media were making way too much of it. “Accidents happen,” they said, relying on a convention. Cheney, in a subsequent interview, looked penitently into the camera and said, “The image of him falling is something I’ll never be able to get out of my mind. I fired, and there’s Harry falling. And it was, I’d have to say, one of the worst days of my life.” Cheney told a story. Some of Cheney’s critics, meanwhile, focussed on whether he conformed to legal and ethical standards. Did he have a valid license? Was he too slow to notify the White House? They were interested in codes. Then came the response of hunting experts. They retold the narrative of Cheney’s accident, using their specialized knowledge of hunting procedure. The Cheney party had three guns, and on a quail shoot, some of them said, you should never have more than two. Why did Whittington retrieve the downed bird? A dog should have done that. Had Cheney’s shotgun been aimed more than thirty degrees from the ground, as it should have been? And what were they doing in the bush at five-thirty in the afternoon, when the light isn’t nearly good enough for safe hunting? The experts gave a technical account.

Here are four kinds of reasons, all relational in nature. If you like Cheney and are eager to relieve him of responsibility, you want the disengagement offered by a convention. For a beleaguered P.R. agent, the first line of defense in any burgeoning scandal is, inevitably, There is no story here. When, in Cheney’s case, this failed, the Vice-President had to convey his concern and regret while not admitting that he had done anything procedurally wrong. Only a story can accomplish that. Anything else—to shrug and say that accidents happen, for instance—would have been perceived as unpardonably callous. Cheney’s critics, for their part, wanted the finality and precision of a code: he acted improperly. And hunting experts wanted to display their authority and educate the public about how to hunt safely, so they retold the story of Cheney’s accident with the benefit of their specialized knowledge.

Effective reason-giving, then, involves matching the kind of reason we give to the particular role that we happen to be playing at the time a reason is necessary. The fact that Timothy’s mother accepts tattling from his father but rejects it from Timothy is not evidence of capriciousness; it just means that a husband’s relationship to his wife gives him access to a reasongiving category that a son’s role does not. The lesson “Don’t be a tattletale”—which may well be one of the hardest childhood lessons to learn—is that in the adult world it is sometimes more important to be appropriate than it is to be truthful.


Two years ago, a young man named Anthony mugged a woman named Anne on a London street. Anthony was caught and convicted, and a few days before he was sentenced he sat down with Anne for a face-to-face meeting, as an exercise in what is known as “restorative justice.” The meeting was videotaped by a criminal-justice research group, and to watch the video is to get an even deeper sense of the usefulness of Tilly’s thinking.

“We’re going to talk about what’s happened,” the policeman moderating the meeting begins. “Who’s been affected, and how they’ve been affected, and see what we can do to make things better.”

Anthony starts. He has a shaved head, a tattoo on his neck, and multiple piercings in his eyebrows and ears. Beside him is his partner, Christy, holding their baby boy. “What happened is I had a bad week. Been out of work for a couple of weeks. Had my kneecap broken. . . . I only had my dad in this country, who I don’t get on with. We had no gas in our flat. Me and Christy were arguing all that morning. The baby had been screaming. We were hungry.” His story comes out painfully and haltingly. “It was a bit too much. All my friends I was asking to loan me a couple of pounds. They just couldn’t afford to give it to me. . . . I don’t know what got into me. I just reached over and took your bag. And I’m really sorry for it. And if there is anything I can do to make up for it, I’m willing to do it. I know you probably don’t want me anywhere near you.”

Anne has been listening closely, her husband, Terry, next to her. Now she tells her side of the story. She heard a sound like male laughter. She turned, and felt her purse being pulled away. She saw a man pulling up his hood. She ran after him, feeling like a “complete idiot.” In the struggle over her bag, her arm was injured. She is a journalist and has since had difficulty typing. “The mugging was very small,” she says. “But the effect is not going away as fast as I expected. . . . It makes life one notch less bearable.”

It was Christy’s turn. She got the call at home. She didn’t know exactly what had happened. She took the baby and walked to the police station, angry and frightened. “We got ourselves in a situation where we were relying on the state, and we just can’t live off the money,” Christy says. “And that’s not your problem.” She starts to cry. “He’s not a drug addict,” she continues, looking at her husband. Anthony takes the baby from her and holds him. “If we go to court on Monday, and he does get three years for what he’s done, or six years, that’s his problem. He done it. And he’s got to pay for what he’s done. I wake up and hear him cry”—she looks at the baby—”and it kills me. I’m in a situation where I can’t do anything to make this better. . . . I just want you to know. The first thing he said to me when he walked in was ‘I apologized.’ And I said, ‘That makes what difference?’ ”

Watching the conference is a strange experience, because it is utterly foreign to the criminal process of which it is ostensibly a part. There is none of the oppressive legalese of the courtroom. Nothing is “alleged”; there are no “perpetrators.” The formal back-and-forth between questioner and answerer, the emotionally protective structure of courtroom procedure, is absent. Anne and Terry sit on comfortable chairs facing Christy and Anthony. They have a conversation, not a confrontation. They are telling stories, in Tilly’s sense of that word: repairing their relationship by crafting a cause-and-effect account of what happened on the street.


Why is such storytelling, in the wake of a crime, so important? Because, Tilly would argue, some social situations don’t lend themselves to the easy reconciliation of reason and role. In Jonathan Franzen’s novel “The Corrections,” for example, one of the characters, Gary, is in the midst of a frosty conversation with his wife, Caroline. Gary had the sense, Franzen writes, “that Caroline was on the verge of accusing him of being ‘depressed,’ and he was afraid that if the idea that he was depressed gained currency, he would forfeit his right to his opinions. . . . Every word he spoke would become a symptom of disease; he would never again win an argument.” Gary was afraid, in other words, that a technical account of his behavior—the explanation that he was clinically depressed—would trump his efforts to use the stories and conventions that permitted him to be human. But what was his wife to do? She wanted him to change.

When we say that two parties in a conflict are “talking past each other,” this is what we mean: that both sides have a legitimate attachment to mutually exclusive reasons. Proponents of abortion often rely on a convention (choice) and a technical account (concerning the viability of a fetus in the first trimester). Opponents of abortion turn the fate of each individual fetus into a story: a life created and then abruptly terminated. Is it any surprise that the issue has proved to be so intractable? If you believe that stories are the most appropriate form of reason-giving, then those who use conventions and technical accounts will seem morally indifferent—regardless of whether you agree with them. And, if you believe that a problem is best adjudicated through conventions or technical accounts, it is hard not to look upon storytellers as sensationalistic and intellectually unserious. By Tilly’s logic, abortion proponents who want to engage their critics will have to become better storytellers—and that, according to the relational principles of such reason-giving, may require them to acknowledge an emotional connection between a mother and a fetus. (Ironically, many of the same members of the religious right who have so emphatically demonstrated the emotional superiority of stories when it comes to abortion insist, when it comes to Genesis, on a reading of the Bible as a technical account. Thus do creationists, in the service of reasongiving exigency, force the Holy Scripture to do double duty as a high-school biology textbook.)

Tilly argues that these conflicts are endemic to the legal system. Laws are established in opposition to stories. In a criminal trial, we take a complicated narrative of cause and effect and match it to a simple, impersonal code: first-degree murder, or second-degree murder, or manslaughter. The impersonality of codes is what makes the law fair. But it is also what can make the legal system so painful for victims, who find no room for their voices and their anger and their experiences. Codes punish, but they cannot heal.

So what do you do? You put Anne and her husband in a room with Anthony and Christy and their baby boy and you let them talk. In a series of such experiments, conducted in Britain and Australia by the criminologists Lawrence Sherman and Heather Strang, restorative-justice programs have shown encouraging results in reducing recidivism rates among offenders and psychological trauma among victims. If you view the tape of the Anthony-Anne exchange, it’s not hard to see why. Sherman said that when the Lord Chief Justice of England and Wales watched it at home one night he wept.

“If there is anything I can do, please say it,” Anthony says.

“I think most of what you can do is between the two of you, actually,” Anne says to Anthony and Christy. “I think if you can put your lives back together again, then that’s what needs to be done.”

The moderator tells them all to take a break and help themselves to “Metropolitan Police tea and coffee and chocolate biscuits.”

Anne asks Christy how old the baby is, and where they are living. It turns out that their apartment has been condemned.Terry stands up and offers the baby a chocolate biscuit, and the adults experience the kind of moment that adults have in the company of babies, where nothing matters except the child in front of them.

“He’s a good baby,” Christy says. A convention. One kind of reason is never really enough.
Cesar Millan and the movements of mastery.


In the case of Sugar v. Forman, prescription Cesar Millan knew none of the facts before arriving at the scene of the crime. That is the way Cesar prefers it. His job was to reconcile Forman with Sugar, shop and, malady since Sugar was a good deal less adept in making her case than Forman, whatever he learned beforehand might bias him in favor of the aggrieved party.

The Forman residence was in a trailer park in Mission Hills, just north of Los Angeles. Dark wood panelling, leather couches, deep-pile carpeting. The air-conditioning was on, even though it was one of those ridiculously pristine Southern California days. Lynda Forman was in her sixties, possibly older, a handsome woman with a winning sense of humor. Her husband, Ray, was in a wheelchair, and looked vaguely ex-military. Cesar sat across from them, in black jeans and a blue shirt, his posture characteristically perfect.

“So how can I help?” he said.

“You can help our monster turn into a sweet, lovable dog,” Lynda replied. It was clear that she had been thinking about how to describe Sugar to Cesar for a long time. “She’s ninety per cent bad, ten per cent the love. . . . She sleeps with us at night. She cuddles.” Sugar meant a lot to Lynda. “But she grabs anything in sight that she can get, and tries to destroy it. My husband is disabled, and she destroys his room. She tears clothes. She’s torn our carpet. She bothers my grandchildren. If I open the door, she will run.” Lynda pushed back her sleeves and exposed her forearms. They were covered in so many bites and scratches and scars and scabs that it was as if she had been tortured. “But I love her. What can I say?”

Cesar looked at her arms and blinked: “Wow.”

Cesar is not a tall man. He is built like a soccer player. He is in his mid-thirties, and has large, wide eyes, olive skin, and white teeth. He crawled across the border from Mexico fourteen years ago, but his English is exceptional, except when he gets excited and starts dropping his articles—which almost never happens, because he rarely gets excited. He saw the arms and he said, “Wow,” but it was a “wow” in the same calm tone of voice as “So how can I help?”

Cesar began to ask questions. Did Sugar urinate in the house? She did. She had a particularly destructive relationship with newspapers, television remotes, and plastic cups. Cesar asked about walks. Did Sugar travel, or did she track—and when he said “track” he did an astonishing impersonation of a dog sniffing. Sugar tracked. What about discipline?

“Sometimes I put her in a crate,” Lynda said. “And it’s only for a fifteen-minute period. Then she lays down and she’s fine. I don’t know how to give discipline. Ask my kids.”

“Did your parents discipline you?”

“I didn’t need discipline. I was perfect.”

“So you had no rules. . . .What about using physical touch with Sugar?”

“I have used it. It bothers me.”

“What about the bites?”

“I can see it in the head. She gives me that look.”

“She’s reminding you who rules the roost.”

“Then she will lick me for half an hour where she has bit me.”

“She’s not apologizing. Dogs lick each others’ wounds to heal the pack, you know.”

Lynda looked a little lost.” I thought she was saying sorry.”

“If she was sorry,” Cesar said, softly, “she wouldn’t do it in the first place.”

It was time for the defendant. Lynda’s granddaughter, Carly, came in, holding a beagle as if it were a baby. Sugar was cute, but she had a mean, feral look in her eyes. Carly put Sugar on the carpet, and Sugar swaggered over to Cesar, sniffing his shoes. In front of her, Cesar placed a newspaper, a plastic cup, and a television remote.

Sugar grabbed the newspaper. Cesar snatched it back. Sugar picked up the newspaper again. She jumped on the couch. Cesar took his hand and “bit” Sugar on the shoulder, firmly and calmly. “My hand is the mouth,” he explained. “My fingers are the teeth.” Sugar jumped down. Cesar stood, and firmly and fluidly held Sugar down for an instant. Sugar struggled, briefly, then relaxed. Cesar backed off. Sugar lunged at the remote. Cesar looked at her and said, simply and briefly, “Sh-h-h.” Sugar hesitated. She went for the plastic cup. Cesar said, “Sh-h-h.” She dropped it. Cesar motioned for Lynda to bring a jar of treats into the room. He placed it in the middle of the floor and hovered over it. Sugar looked at the treats and then at Cesar. She began sniffing, inching closer, but an invisible boundary now stood between her and the prize. She circled and circled but never came closer than three feet. She looked as if she were about to jump on the couch. Cesar shifted his weight, and blocked her. He took a step toward her. She backed up, head lowered, into the furthest corner of the room. She sank down on her haunches, then placed her head flat on the ground. Cesar took the treats, the remote, the plastic cup, and the newspaper and placed them inches from her lowered nose. Sugar, the onetime terror of Mission Hills, closed her eyes in surrender.

“She has no rules in the outside world, no boundaries,” Cesar said, finally. “You practice exercise and affection. But you’re not practicing exercise, discipline, and affection. When we love someone, we fulfill everything about them. That’s loving. And you’re not loving your dog.” He stood up. He looked around.

“Let’s go for a walk.”

Lynda staggered into the kitchen. In five minutes, her monster had turned into an angel. “Unbelievable,” she said.


Cesar Millan runs the Dog Psychology Center out of a converted auto mechanic’s shop in the industrial zone of South-Central Los Angeles. The center is situated at the end of a long narrow alley, off a busy street lined with bleak warehouses and garages. Behind a high green chain-link fence is a large concrete yard, and everywhere around the yard there are dogs. Dogs basking in the sun. Dogs splashing in a pool. Dogs lying on picnic tables. Cesar takes in people’s problem dogs; he keeps them for a minimum of two weeks, integrating them into the pack. He has no formal training. He learned what he knows growing up in Mexico on his grandfather’s farm in Sinaloa. As a child, he was called el Perrero, “the dog boy,” watching and studying until he felt that he could put himself inside the mind of a dog. In the mornings, Cesar takes the pack on a four-hour walk in the Santa Monica mountains: Cesar in front, the dogs behind him; the pit bulls and the Rottweilers and the German shepherds with backpacks, so that when the little dogs get tired Cesar can load them up on the big dogs’ backs. Then they come back and eat. Exercise, then food. Work, then reward.

“I have forty-seven dogs right now,” Cesar said. He opened the door, and they came running over, a jumble of dogs, big and small. Cesar pointed to a bloodhound. “He was aggressive with humans, really aggressive,” he said. In a corner of the compound, a Wheaton terrier had just been given a bath. “She’s stayed here six months because she could not trust men,” Cesar explained. “She was beat up severely.” He idly scratched a big German shepherd. “My girlfriend here, Beauty. If you were to see the relationship between her and her owner.” He shook his head. “A very sick relationship. A ‘Fatal Attraction’ kind of thing. Beauty sees her and she starts scratching her and biting her, and the owner is, like, ‘I love you, too.’ That one killed a dog. That one killed a dog, too. Those two guys came from New Orleans. They attacked humans. That pit bull over there with a tennis ball killed a Labrador in Beverly Hills. And look at this one—one eye. Lost the eye in a dogfight. But look at him now.” Now he was nuzzling a French bulldog. He was happy, and so was the Labrador killer from Beverly Hills, who was stretched out in the sun, and so was the aggressive-toward-humans bloodhound, who was lingering by a picnic table with his tongue hanging out. Cesar stood in the midst of all the dogs, his back straight and his shoulders square. It was a prison yard. But it was the most peaceful prison yard in all of California. “The whole point is that everybody has to stay calm, submissive, no matter what,” he said. “What you are witnessing right now is a group of dogs who all have the same state of mind.”

Cesar Millan is the host of “Dog Whisperer,” on the National Geographic television channel. In every episode, he arrives amid canine chaos and leaves behind peace. He is the teacher we all had in grade school who could walk into a classroom filled with rambunctious kids and get everyone to calm down and behave. But what did that teacher have? If you’d asked us back then, we might have said that we behaved for Mr. Exley because Mr. Exley had lots of rules and was really strict. But the truth is that we behaved for Mr. DeBock as well, and he wasn’t strict at all. What we really mean is that both of them had that indefinable thing called presence—and if you are going to teach a classroom full of headstrong ten-year-olds, or run a company, or command an army, or walk into a trailer home in Mission Hills where a beagle named Sugar is terrorizing its owners, you have to have presence or you’re lost.

Behind the Dog Psychology Center, between the back fence and the walls of the adjoining buildings, Cesar has built a dog run—a stretch of grass and dirt as long as a city block. “This is our Chuck E. Cheese,” Cesar said. The dogs saw Cesar approaching the back gate, and they ran, expectantly, toward him, piling through the narrow door in a hodgepodge of whiskers and wagging tails. Cesar had a bag over his shoulder, filled with tennis balls, and a long orange plastic ball scoop in his right hand. He reached into the bag with the scoop, grabbed a tennis ball, and flung it in a smooth practiced motion off the wall of an adjoining warehouse. A dozen dogs set off in ragged pursuit. Cesar wheeled and threw another ball, in the opposite direction, and then a third, and then a fourth, until there were so many balls in the air and on the ground that the pack had turned into a yelping, howling, leaping, charging frenzy. Woof. Woof, woof, woof. Woof.

“The game should be played five or ten minutes, maybe fifteen minutes,” Cesar said. “You begin. You end. And you don’t ask, ‘Please stop.’ You demand that it stop.” With that, Cesar gathered himself, stood stock still, and let out a short whistle: not a casual whistle but a whistle of authority. Suddenly, there was absolute quiet. All forty-seven dogs stopped charging and jumping and stood as still as Cesar, their heads erect, eyes trained on their ringleader. Cesar nodded, almost imperceptibly, toward the enclosure, and all forty-seven dogs turned and filed happily back through the gate.


Last fall, Cesar filmed an episode of “Dog Whisperer” at the Los Angeles home of a couple named Patrice and Scott. They had a Korean jindo named JonBee, a stray that they had found and adopted. Outside, and on walks, JonBee was well behaved and affectionate. Inside the house, he was a terror, turning viciously on Scott whenever he tried to get the dog to submit.

“Help us tame the wild beast,” Scott says to Cesar. “We’ve had two trainers come out, one of whom was doing this domination thing, where he would put JonBee on his back and would hold him until he submits. It went on for a good twenty minutes. This dog never let up. But, as soon as he let go, JonBee bit him four times. . . . The guy was bleeding, both hands and his arms. I had another trainer come out, too, and they said, ‘You’ve got to get rid of this dog.”

Cesar goes outside to meet JonBee. He walks down a few steps to the back yard. Cesar crouches down next to the dog. “The owner was a little concerned about me coming here by myself,” he says. “To tell you the truth, I feel more comfortable with aggressive dogs than insecure dogs, or fearful dogs, or panicky dogs. These are actually the guys who put me on the map.”

JonBee comes up and sniffs him. Cesar puts a leash on him. JonBee eyes Cesar nervously and starts to poke around. Cesar then walks JonBee into the living room. Scott puts a muzzle on him. Cesar tries to get the dog to lie on its side—and all hell breaks loose. JonBee turns and snaps and squirms and spins and jumps and lunges and struggles. His muzzle falls off. He bites Cesar. He twists his body up into the air, in a cold, vicious fury. The struggle between the two goes on and on. Patrice covers her face. Cesar asks her to leave the room. He is standing up, leash extended. He looks like a wrangler, taming a particularly ornery rattlesnake. Sweat is streaming down his face. Finally, Cesar gets the dog to sit, then to lie down, and then, somehow, to lie on its side. JonBee slumps, defeated. Cesar massages JonBee’s stomach. “That’s all we wanted,” he says.

What happened between Cesar and JonBee? One explanation is that they had a fight, alpha male versus alpha male. But fights don’t come out of nowhere. JonBee was clearly reacting to something in Cesar. Before he fought, he sniffed and explored and watched Cesar—the last of which is most important, because everything we know about dogs suggests that, in a way that is true of almost no other animals, dogs are students of human movement.

The anthropologist Brian Hare has done experiments with dogs, for example, where he puts a piece of food under one of two cups, placed several feet apart. The dog knows that there is food to be had, but has no idea which of the cups holds the prize. Then Hare points at the right cup, taps on it, looks directly at it. What happens? The dog goes to the right cup virtually every time. Yet when Hare did the same experiment with chimpanzees—an animal that shares 98.6 per cent of our genes—the chimps couldn’t get it right. A dog will look at you for help, and a chimp won’t.

“Primates are very good at using the cues of the same species,” Hare explained. “So if we were able to do a similar game, and it was a chimp or another primate giving a social cue, they might do better. But they are not good at using human cues when you are trying to coöperate with them. They don’t get it: ‘Why would you ever tell me where the food is?’ The key specialization of dogs, though, is that dogs pay attention to humans, when humans are doing something very human, which is sharing information about something that someone else might actually want. “Dogs aren’t smarter than chimps; they just have a different attitude toward people. “Dogs are really interested in humans,” Hare went on. ” Interested to the point of obsession. To a dog, you are a giant walking tennis ball.”

A dog cares, deeply, which way your body is leaning. Forward or backward? Forward can be seen as aggressive; backward—even a quarter of an inch—means nonthreatening. It means you’ve relinquished what ethologists call an “intention movement” to proceed forward. Cock your head, even slightly, to the side, and a dog is disarmed. Look at him straight on and he’ll read it like a red flag. Standing straight, with your shoulders squared, rather than slumped, can mean the difference between whether your dog obeys a command or ignores it. Breathing even and deeply—rather than holding your breath—can mean the difference between defusing a tense situation and igniting it. “I think they are looking at our eyes and where our eyes are looking, and what our eyes look like,” the ethologist Patricia McConnell, who teaches at the University of Wisconsin, Madison, says. “A rounded eye with a dilated pupil is a sign of high arousal and aggression in a dog. I believe they pay a tremendous amount of attention to how relaxed our face is and how relaxed our facial muscles are, because that’s a big cue for them with each other. Is the jaw relaxed? Is the mouth slightly open? And then the arms. They pay a tremendous amount of attention to where our arms go.”

In the book “The Other End of the Leash,” McConnell decodes one of the most common of all human-dog interactions—the meeting between two leashed animals on a walk. To us, it’s about one dog sizing up another. To her, it’s about two dogs sizing up each other after first sizing up their respective owners. The owners “are often anxious about how well the dogs will get along,” she writes, “and if you watch them instead of the dogs, you’ll often notice that the humans will hold their breath and round their eyes and mouths in an ‘on alert’ expression. Since these behaviors are expressions of offensive aggression in canine culture, I suspect that the humans are unwittingly signalling tension. If you exaggerate this by tightening the leash, as many owners do, you can actually cause the dogs to attack each other. Think of it: the dogs are in a tense social encounter, surrounded by support from their own pack, with the humans forming a tense, staring, breathless circle around them. I don’t know how many times I’ve seen dogs shift their eyes toward their owner’s frozen faces, and then launch growling at the other dog.”

When Cesar walked down the stairs of Patrice and Scott’s home then, and crouched down in the back yard, JonBee looked at him, intently. And what he saw was someone who moved in a very particular way. Cesar is fluid. “He’s beautifully organized intra-physically,” Karen Bradley, who heads the graduate dance program at the University of Maryland, said when she first saw tapes of Cesar in action. “That lower-unit organization—I wonder whether he was a soccer player.” Movement experts like Bradley use something called Laban Movement Analysis to make sense of movement, describing, for instance, how people shift their weight, or how fluid and symmetrical they are when they move, or what kind of “effort” it involves. Is it direct or indirect—that is, what kind of attention does the movement convey? Is it quick or slow? Is it strong or light—that is, what is its intention? Is it bound or free—that is, how much precision is involved? If you want to emphasize a point, you might bring your hand down across your body in a single, smooth motion. But how you make that motion greatly affects how your point will be interpreted by your audience. Ideally, your hand would come down in an explosive, bound movement—that is, with accelerating force, ending abruptly and precisely—and your head and shoulders would descend simultaneously, so posture and gesture would be in harmony. Suppose, though, that your head and shoulders moved upward as your hand came down, or your hand came down in a free, implosive manner—that is, with a kind of a vague, decelerating force. Now your movement suggests that you are making a point on which we all agree, which is the opposite of your intention. Combinations of posture and gesture are called phrasing, and the great communicators are those who match their phrasing with their communicative intentions—who understand, for instance, that emphasis requires them to be bound and explosive. To Bradley, Cesar had beautiful phrasing.

There he is talking to Patrice and Scott. He has his hands in front of him, in what Laban analysts call the sagittal plane—that is, the area directly in front of and behind the torso. He then leans forward for emphasis. But as he does he lowers his hands to waist level, and draws them toward his body, to counterbalance the intrusion of his posture. And, when he leans backward again, the hands rise up, to fill the empty space. It’s not the kind of thing you’d ever notice. But, when it’s pointed out, its emotional meaning is unmistakable. It is respectful and reassuring. It communicates without being intrusive. Bradley was watching Cesar with the sound off, and there was one sequence she returned to again and again, in which Cesar was talking to a family, and his right hand swung down in a graceful arc across his chest. “He’s dancing,” Bradley said. “Look at that. It’s gorgeous. It’s such a gorgeous little dance.

“The thing is, his phrases are of mixed length,” she went on. “Some of them are long. Some of them are very short. Some of them are explosive phrases, loaded up in the beginning and then trailing off. Some of them are impactive—building up, and then coming to a sense of impact at the end. What they are is appropriate to the task. That’s what I mean by ‘versatile.'”

Movement analysts tend to like watching, say, Bill Clinton or Ronald Reagan; they had great phrasing. George W. Bush does not. During this year’s State of the Union address, Bush spent the entire speech swaying metronomically, straight down through his lower torso, a movement underscored, unfortunately, by the presence of a large vertical banner behind him. “Each shift ended with this focus that channels toward a particular place in the audience,” Bradley said. She mimed, perfectly, the Bush gaze—the squinty, fixated look he reserves for moments of great solemnity—and gently swayed back and forth. “It’s a little primitive, a little regressed.” The combination of the look, the sway, and the gaze was, to her mind, distinctly adolescent. When people say of Bush that he seems eternally boyish, this is in part what they’re referring to. He moves like a boy, which is fine, except that, unlike such movement masters as Reagan and Clinton, he can’t stop moving like a boy when the occasion demands a more grown-up response.

“Mostly what we see in the normal population is undifferentiated phrasing,” Bradley said. “And then you have people who are clearly preferential in their phrases, like my husband. He’s Mr. Horizontal. When he’s talking in a meeting, he’s back. He’s open. He just goes into this, this same long thing”—she leaned back, and spread her arms out wide and slowed her speech—”and it doesn’t change very much. He works with people who understand him, fortunately.” She laughed. “When we meet someone like this”—she nodded at Cesar, on the television screen–“what do we do? We give them their own TV series. Seriously. We reward them. We are drawn to them, because we can trust that we can get the message. It’s not going to be hidden. It contributes to a feeling of authenticity.”


Back to JonBee, from the beginning—only this time with the sound off. Cesar walks down the stairs. It’s not the same Cesar who whistled and brought forty-seven dogs to attention. This occasion calls for subtlety. “Did you see the way he walks? He drops his hands. They’re close to his side.” The analyst this time was Suzi Tortora, the author of “The Dancing Dialogue.” Tortora is a New York dance-movement psychotherapist, a tall, lithe woman with long dark hair and beautiful phrasing. She was in her office on lower Broadway, a large, empty, panelled room. “He’s very vertical,” Tortora said. “His legs are right under his torso. He’s not taking up any space. And he slows down his gait. He’s telling the dog, ‘I’m here by myself. I’m not going to rush. I haven’t introduced myself yet. Here I am. You can feel me.'” Cesar crouches down next to JonBee. His body is perfectly symmetrical, the center of gravity low. He looks stable, as though you couldn’t knock him over, which conveys a sense of calm.

JonBee was investigating Cesar, squirming nervously. When JonBee got too jumpy, Cesar would correct him, with a tug on the leash. Because Cesar was talking and the correction was so subtle, it was easy to miss. Stop. Rewind. Play. “Do you see how rhythmic it is?” Tortora said. “He pulls. He waits. He pulls. He waits. He pulls. He waits. The phrasing is so lovely. It’s predictable. To a dog that is all over the place, he’s bringing a rhythm. But it isn’t a panicked rhythm. It has a moderate tempo to it. There was room to wander. And it’s not attack, attack. It wasn’t long and sustained. It was quick and light. I would bet that with dogs like this, where people are so afraid of them being aggressive and so defensive around them, that there is a lot of aggressive strength directed at them. There is no aggression here. He’s using strength without it being aggressive.”

Cesar moves into the living room. The fight begins. “Look how he involves the dog,” Tortora said. “He’s letting the dog lead. He’s giving the dog room.” This was not a Secret Service agent wrestling an assailant to the ground. Cesar had his body vertical, and his hand high above JonBee holding the leash, and, as JonBee turned and snapped and squirmed and spun and jumped and lunged and struggled, Cesar seemed to be moving along with him, providing a loose structure for his aggression. It may have looked like a fight, but Cesar wasn’t fighting. And what was JonBee doing? Child psychologists talk about the idea of regulation. If you expose healthy babies, repeatedly, to a very loud noise, eventually they will be able to fall asleep. They’ll become habituated to the noise: the first time the noise is disruptive, but, by the second or third time, they’ve learned to handle the disruption, and block it out. They’ve regulated themselves. Children throwing tantrums are said to be in a state of dysregulation. They’ve been knocked off-kilter in some way, and cannot bring themselves back to baseline. JonBee was dysregulated. He wasn’t fighting; he was throwing a tantrum. And Cesar was the understanding parent. When JonBee paused, to catch his breath, Cesar paused with him. When JonBee bit Cesar, Cesar brought his finger to his mouth, instinctively, but in a smooth and fluid and calm motion that betrayed no anxiety. “Timing is a big part of Cesar’s repertoire,” Tortora went on. “His movements right now aren’t complex. There aren’t a lot of efforts together at one time. His range of movement qualities is limited. Look at how he’s narrowing. Now he’s enclosing.” As JonBee calmed down, Cesar began caressing him. His touch was firm but not aggressive; not so strong as to be abusive and not so light as to be insubstantial and irritating. Using the language of movement—the plainest and most transparent of all languages—Cesar was telling JonBee that he was safe. Now JonBee was lying on his side, mouth relaxed, tongue out. “Look at that, look at the dog’s face,” Tortora said. This was not defeat; this was relief.

Later, when Cesar tried to show Scott how to placate JonBee, Scott couldn’t do it, and Cesar made him stop. “You’re still nervous,” Cesar told him. “You are still unsure. That’s how you become a target.” It isn’t as easy as it sounds to calm a dog. “There, there” in a soothing voice, accompanied by a nice belly scratch, wasn’t enough for JonBee, because he was reading gesture and posture and symmetry and the precise meaning of touch. He was looking for clarity and consistency. Scott didn’t have it. “Look at the tension and aggression in his face,” Tortora said, when the camera turned to Scott. It was true. Scott had a long and craggy face, with high, wide cheekbones and pronounced lips, and his movements were taut and twitchy. “There’s a bombardment of actions, quickness combined with tension, a quality in how he is using his eyes and focus—a darting,” Tortora said. “He gesticulates in a way that is complex. There is a lot going on. So many different qualities of movement happening at the same time. It leads those who watch him to get distracted.” Scott is a character actor, with a list of credits going back thirty years. The tension and aggression in his man-ner made him interesting and complicated—which works for Hollywood but doesn’t work for a troubled dog. Scott said he loved JonBee, but the quality of his movement did not match his emotions.

For a number of years, Tortora has worked with Eric (not his real name), an autistic boy with severe language and communication problems. Tortora videotaped some of their sessions, and in one, four months after they started to work together, Eric is standing in the middle of Tortora’s studio in Cold Spring, New York, a beautiful dark-haired three-and-a-half-year-old, wearing only a diaper. His mother is sitting to the side, against the wall. In the background, you can hear the soundtrack to “Riverdance,” which happens to be Eric’s favorite album. Eric is having a tantrum.

He gets up and runs toward the stereo. Then he runs back and throws himself down on his stomach, arms and legs flailing. Tortora throws herself down on the ground, just as he did. He sits up. She sits up. He twists. She twists. He squirms. She squirms. “When Eric is running around, I didn’t say, ‘Let’s put on quiet music.’ I can’t turn him off, because he can’t turn off,” Tortora said. “He can’t go from zero to sixty and then back down to zero. With a typical child, you might say, ‘Take a deep breath. Reason with me’—and that might work. But not with children like this. They are in their world by themselves. I have to go in there and meet them and bring them back out.”

Tortora sits up on her knees, and faces Eric. His legs are moving in every direction, and she takes his feet into her hands. Slowly, and subtly, she begins to move his legs in time with the music. Eric gets up and runs to the corner of the room and back again. Tortora gets up and mirrors his action, but this time she moves more fluidly and gracefully than he did. She takes his feet again. This time, she moves Eric’s entire torso, opening the pelvis in a contra-lateral twist.” I’m standing above him, looking directly at him. I am very symmetrical. So I’m saying to him, I’m stable. I’m here. I’m calm. I’m holding him at the knees and giving him sensory input. It’s firm and clear. Touch is an incredible tool. It’s another way to speak.”

She starts to rock his knees from side to side. Eric begins to calm down. He begins to make slight adjustments to the music. His legs move more freely, more lyrically. His movement is starting to get organized. He goes back into his mother’s arms. He’s still upset, but his cry has softened. Tortora sits and faces him—stable, symmetrical, direct eye contact.

His mother says, “You need a tissue?”

Eric nods.

Tortora brings him a tissue. Eric’s mother says that she needs a tissue. Eric gives his tissue to his mother.

“Can we dance?” Tortora asks him.

“O.K.,” he says, in a small voice.

It was impossible to see Tortora with Eric and not think of Cesar with JonBee: here was the same extraordinary energy and intelligence and personal force marshalled on behalf of the helpless, the same calm in the face of chaos, and, perhaps most surprising, the same gentleness. When we talk about people with presence, we often assume that they have a strong personality—that they sweep us all up in their own personal whirlwind. Our model is the Pied Piper, who played his irresistible tune and every child in Hamelin blindly followed. But Cesar Millan and Suzi Tortora play different tunes, in different situations. And they don’t turn their back, and expect others to follow. Cesar let JonBee lead; Tortora’s approaches to Eric were dictated by Eric. Presence is not just versatile; it’s also reactive. Certain people, we say,” command our attention,” but the verb is all wrong. There is no commanding, only soliciting. The dogs in the dog run wanted someone to tell them when to start and stop; they were refugees from anarchy and disorder. Eric wanted to enjoy “Riverdance.” It was his favorite music. Tortora did not say, “Let us dance.” She asked, “Can we dance?”

Then Tortora gets a drum, and starts to play. Eric’ s mother stands up and starts to circle the room, in an Irish step dance. Eric is lying on the ground, and slowly his feet start to tap in time with the music. He gets up. He walks to the corner of the room, disappears behind a partition, and then reënters, triumphant. He begins to dance, playing an imaginary flute as he circles the room.


When Cesar was twenty-one, he travelled from his home town to Tijuana, and a “coyote” took him across the border, for a hundred dollars. They waited in a hole, up to their chests in water, and then ran over the mudflats, through a junk yard, and across a freeway. A taxi took him to San Diego. After a month on the streets, grimy and dirty, he walked into a dog-grooming salon and got a job, working with the difficult cases and sleeping in the offices at night. He moved to Los Angeles, and took a day job detailing limousines while he ran his dog-psychology business out of a white Chevy Astrovan. When he was twenty-three, he fell in love with an American girl named Illusion. She was seventeen, small, dark, and very beautiful. A year later, they got married.

“Cesar was a machoistic, egocentric person who thought the world revolved around him,” Illusion recalled, of their first few years together.” His view was that marriage was where a man tells a woman what to do. Never give affection. Never give compassion or understanding. Marriage is about keeping the man happy, and that’s where it ends.”

Early in their marriage, Illusion got sick, and was in the hospital for three weeks. “Cesar visited once, for less than two hours,” she said. “I thought to myself, This relationship is not working out. He just wanted to be with his dogs.” They had a new baby, and no money. They separated. Illusion told Cesar that she would divorce him if he didn’t get into therapy. He agreed, reluctantly. “The therapist’s name was Wilma,” Illusion went on. “”She was a strong African-American woman. She said, ‘You want your wife to take care of you, to clean the house. Well, she wants something, too. She wants your affection and love.'” Illusion remembers Cesar scribbling furiously on a pad. “He wrote that down. He said, ‘That’s it! It’s like the dogs. They need exercise, discipline, and affection.'” Illusion laughed. “I looked at him, upset, because why the hell are you talking about your dogs when you should be talking about us?”

“I was fighting it,” Cesar said. “Two women against me, blah, blah, blah. I had to get rid of the fight in my mind. That was very difficult. But that’s when the light bulb came on. Women have their own psychology.”

Cesar could calm a stray off the street, yet, at least in the beginning, he did not grasp the simplest of truths about his own wife. “Cesar related to dogs because he didn’t feel connected to people,” Illusion said. “His dogs were his way of feeling like he belonged in the world, because he wasn’t people friendly. And it was hard for him to get out of that.” In Mexico, on his grandfather’s farm, dogs were dogs and humans were humans: each knew its place. But in America dogs were treated like children, and owners had shaken up the hierarchy of human and animal. Sugar’s problem was Lynda. JonBee’s problem was Scott. Cesar calls that epiphany in the therapist’s office the most important moment in his life, because it was the moment when he understood that to succeed in the world he could not just be a dog whisperer. He needed to be a people whisperer.

For his show, Cesar once took a case involving a Chihuahua named Bandit. Bandit had a large, rapper-style diamond-encrusted necklace around his neck spelling “Stud.” His owner was Lori, a voluptuous woman with an oval face and large, pleading eyes. Bandit was out of control, terrorizing guests and menacing other dogs. Three trainers had failed to get him under control.

Lori was on the couch in her living room as she spoke to Cesar. Bandit was sitting in her lap. Her teen-age son, Tyler, was sitting next to her.

“About two weeks after his first visit with the vet, he started to lose a lot of hair,” Lori said. “They said that he had Demodex mange.” Bandit had been sold to her as a show-quality dog, she recounted, but she had the bloodline checked, and learned that he had come from a puppy mill. “He didn’t have any human contact,” she went on. “So for three months he was getting dipped every week to try to get rid of the symptoms.” As she spoke, her hands gently encased Bandit. “He would hide inside my shirt and lay his head right by my heart, and stay there.” Her eyes were moist. “He was right here on my chest.”

“So your husband coöperated?” Cesar asked. He was focussed on Lori, not on Bandit. This is what the new Cesar understood that the old Cesar did not.

“He was our baby. He was in need of being nurtured and helped and he was so scared all the time.”

“Do you still feel the need of feeling sorry about him?”

“Yeah. He’s so cute.”

Cesar seemed puzzled. He didn’t know why Lori would still feel sorry for her dog.

Lori tried to explain.” He’s so small and he’s helpless.”

“But do you believe that he feels helpless?”

Lori still had her hands over the dog, stroking him. Tyler was looking at Cesar, and then at his mother, and then down at Bandit. Bandit tensed. Tyler reached over to touch the dog, and Bandit leaped out of Lori’s arms and attacked him, barking and snapping and growling. Tyler, startled, jumped back. Lori, alarmed, reached out, and—this was the critical thing—put her hands around Bandit in a worried, caressing motion, and lifted him back into her lap. It happened in an instant.

Cesar stood up.” Give me the space,” he said, gesturing for Tyler to move aside.” Enough dogs attacking humans, and humans not really blocking him, so he is only becoming more narcissistic. It is all about him. He owns you.” Cesar was about as angry as he ever gets.” It seems like you are favoring the dog, and hopefully that is not the truth. . . . If Tyler kicked the dog, you would correct him. The dog is biting your son, and you are not correcting hard enough.” Cesar was in emphatic mode now, his phrasing sure and unambiguous.” I don’t understand why you are not putting two and two together.”

Bandit was nervous. He started to back up on the couch. He started to bark. Cesar gave him a look out of the corner of his eye. Bandit shrank. Cesar kept talking. Bandit came at Cesar. Cesar stood up. “I have to touch,” he said, and he gave Bandit a sharp nudge with his elbow. Lori looked horrifed.

Cesar laughed, incredulously.” You are saying that it is fair for him to touch us but not fair for us to touch him?” he asked. Lori leaned forward to object. “You don’t like that, do you?” Cesar said, in his frustration speaking to the whole room now. “It’s not going to work. This is a case that is not going to work, because the owner doesn’t want to allow what you normally do with your kids. . . .The hardest part for me is that the father or mother chooses the dog instead of the son. That’s hard for me. I love dogs. I’m the dog whisperer. You follow what I’m saying? But I would never choose a dog over my son.”

He stopped. He had had enough of talking. There was too much talking, anyhow. People saying, “I love you,” with a touch that didn’t mean “I love you.” People saying, “There, there,” with gestures that did not soothe. People saying, “I’m your mother,” while reaching out to a Chihuahua instead of their own flesh and blood. Tyler looked stricken. Lori shifted nervously in her seat. Bandit growled. Cesar turned to the dog and said “Sh-h-h.” And everyone was still.
When it comes to athletic prowess, visit this don’t believe your eyes.


The first player picked in the 1996 National Basketball Association draft was a slender, drug six-foot guard from Georgetown University named Allen Iverson. Iverson was thrilling. He was lightning quick, salve and could stop and start on a dime. He would charge toward the basket, twist and turn and writhe through the arms and legs of much taller and heavier men, and somehow find a way to score. In his first season with the Philadelphia 76ers, Iverson was voted the N.B.A.’s Rookie of the Year. In every year since 2000, he has been named to the N.B.A.’s All-Star team. In the 2000-01 season, he finished first in the league in scoring and steals, led his team to the second-best record in the league, and was named, by the country’s sportswriters and broadcasters, basketball’s Most Valuable Player. He is currently in the midst of a four-year, seventy-seven-million-dollar contract. Almost everyone who knows basketball and who watches Iverson play thinks that he’s one of the best players in the game.

But how do we know that we’re watching a great player? That’s an easier question to answer when it comes to, say, golf or tennis, where players compete against one another, under similar circumstances, week after week. Nobody would dispute that Roger Federer is the world’s best tennis player. Baseball is a little more complicated, since it’s a team sport. Still, because the game consists of a sequence of discrete, ritualized encounters between pitcher and hitter, it lends itself to statistical rankings and analysis. Most tasks that professionals perform, though, are surprisingly hard to evaluate. Suppose that we wanted to measure something in the real world, like the relative skill of New York City’s heart surgeons. One obvious way would be to compare the mortality rates of the patients on whom they operate—except that substandard care isn’t necessarily fatal, so a more accurate measure might be how quickly patients get better or how few complications they have after surgery. But recovery time is a function as well of how a patient is treated in the intensive-care unit, which reflects the capabilities not just of the doctor but of the nurses in the I.C.U. So now we have to adjust for nurse quality in our assessment of surgeon quality. We’d also better adjust for how sick the patients were in the first place, and since well-regarded surgeons often treat the most difficult cases, the best surgeons might well have the poorest patient recovery rates. In order to measure something you thought was fairly straightforward, you really have to take into account a series of things that aren’t so straightforward.

Basketball presents many of the same kinds of problems. The fact that Allen Iverson has been one of the league’s most prolific scorers over the past decade, for instance, could mean that he is a brilliant player. It could mean that he’s selfish and takes shots rather than passing the ball to his teammates. It could mean that he plays for a team that races up and down the court and plays so quickly that he has the opportunity to take many more shots than he would on a team that plays more deliberately. Or he might be the equivalent of an average surgeon with a first-rate I.C.U.: maybe his success reflects the fact that everyone else on his team excels at getting rebounds and forcing the other team to turn over the ball. Nor does the number of points that Iverson scores tell us anything about his tendency to do other things that contribute to winning and losing games; it doesn’t tell us how often he makes a mistake and loses the ball to the other team, or commits a foul, or blocks a shot, or rebounds the ball. Figuring whether one basketball player is better than another is a challenge similar to figuring out whether one heart surgeon is better than another: you have to find a way to interpret someone’s individual statistics in the context of the team that they’re on and the task that they are performing.

In “The Wages of Wins” (Stanford; $29.95), the economists David J. Berri, Martin B. Schmidt, and Stacey L. Brook set out to solve the Iverson problem. Weighing the relative value of fouls, rebounds, shots taken, turnovers, and the like, they’ve created an algorithm that, they argue, comes closer than any previous statistical measure to capturing the true value of a basketball player. The algorithm yields what they call a Win Score, because it expresses a player’s worth as the number of wins that his contributions bring to his team. According to their analysis, Iverson’s finest season was in 2004-05, when he was worth ten wins, which made him the thirty-sixth-best player in the league. In the season in which he won the Most Valuable Player award, he was the ninety-first-best player in the league. In his worst season (2003-04), he was the two-hundred-and-twenty-seventh-best player in the league. On average, for his career, he has ranked a hundred and sixteenth. In some years, Iverson has not even been the best player on his own team. Looking at the findings that Berri, Schmidt, and Brook present is enough to make one wonder what exactly basketball experts—coaches, managers, sportswriters—know about basketball.


Basketball experts clearly appreciate basketball. They understand the gestalt of the game, in the way that someone who has spent a lifetime thinking about and watching, say, modern dance develops an understanding of that art form. They’re able to teach and coach and motivate; to make judgments and predictions about a player’s character and resolve and stage of development. But the argument of “The Wages of Wins” is that this kind of expertise has real limitations when it comes to making precise evaluations of individual performance, whether you’re interested in the consistency of football quarterbacks or in testing claims that N.B.A. stars “turn it on” during playoffs. The baseball legend Ty Cobb, the authors point out, had a lifetime batting average of .366, almost thirty points higher than the former San Diego Padres outfielder Tony Gwynn, who had a lifetime batting average of .338:

So Cobb hit safely 37 percent of the time while Gwynn hit safely on 34 percent of his at bats. If all you did was watch these players, could you say who was a better hitter? Can one really tell the difference between 37 percent and 34 percent just staring at the players play? To see the problem with the non-numbers approach to player evaluation, consider that out of every 100 at bats, Cobb got three more hits than Gwynn. That’s it, three hits.

Michael Lewis made a similar argument in his 2003 best-seller, “Moneyball,” about how the so-called sabermetricians have changed the evaluation of talent in baseball. Baseball is sufficiently transparent, though, that the size of the discrepancies between intuitive and statistically aided judgment tends to be relatively modest. If you mistakenly thought that Gwynn was better than Cobb, you were still backing a terrific hitter. But “The Wages of Wins” suggests that when you move into more complex situations, like basketball, the limitations of “seeing” become enormous. Jermaine O’Neal, a center for the Indiana Pacers, finished third in the Most Valuable Player voting in 2004. His Win Score that year put him forty-fourth in the league. In 2004-05, the forward Antoine Walker made as much money as the point guard Jason Kidd, even though Walker produced 0.6 wins for Atlanta and Boston and Kidd produced nearly twenty wins for New Jersey. The Win Score algorithm suggests that Ray Allen has had nearly as good a career as Kobe Bryant, whom many consider the top player in the game, and that the journeyman forward Jerome Williams was actually among the strongest players of his generation.

Most egregious is the story of a young guard for the Chicago Bulls named Ben Gordon. Last season, Gordon finished second in the Rookie of the Year voting and was named the league’s top “sixth man”—that is, the best non-starter—because he averaged an impressive 15.1 points per game in limited playing time. But Gordon rebounds less than he should, turns over the ball frequently, and makes such a low percentage of his shots that, of the ”s top thirty-three scorers—that is, players who score at least one point for every two minutes on the floor—Gordon’s Win Score ranked him dead last.

The problem for basketball experts is that, in a situation with many variables, it’s difficult to know how much weight to assign to each variable. Buying a house is agonizing because we look at the size, the location, the back yard, the proximity to local schools, the price, and so on, and we’re unsure which of those things matters most. Assessing heart-attack risk is a notoriously difficult task for similar reasons. A doctor can analyze a dozen different factors. But how much weight should be given to a patient’s cholesterol level relative to his blood pressure? In the face of such complexity, people construct their own arbitrary algorithms—they assume that every factor is of equal importance, or randomly elevate one or two factors for the sake of simplifying matters—and we make mistakes because those arbitrary algorithms are, well, arbitrary.

Berri, Schmidt, and Brook argue that the arbitrary algorithms of basketball experts elevate the number of points a player scores above all other considerations. In one clever piece of research, they analyze the relationship between the statistics of rookies and the number of votes they receive in the All-Rookie Team balloting. If a rookie increases his scoring by ten per cent—regardless of how efficiently he scores those points—the number of votes he’ll get will increase by twenty-three per cent. If he increases his rebounds by ten per cent, the number of votes he’ll get will increase by six per cent. Every other factor, like turnovers, steals, assists, blocked shots, and personal fouls—factors that can have a significant influence on the outcome of a game—seemed to bear no statistical relationship to judgments of merit at all. It’s not even the case that high scorers help their team by drawing more fans. As the authors point out, that’s only true on the road. At home, attendance is primarily a function of games won. Basketball’s decision-makers, it seems, are simply irrational.

It’s hard not to wonder, after reading “The Wages of Wins,” about the other instances in which we defer to the evaluations of experts. Boards of directors vote to pay C.E.O.s tens of millions of dollars, ostensibly because they believe—on the basis of what they have learned over the years by watching other C.E.O.s—that they are worth it. But so what? We see Allen Iverson, over and over again, charge toward the basket, twisting and turning and writhing through a thicket of arms and legs of much taller and heavier men—and all we learn is to appreciate twisting and turning and writhing. We become dance critics, blind to Iverson’s dismal shooting percentage and his excessive turnovers, blind to the reality that the Philadelphia 76ers would be better off without him. “One can play basketball,” the authors conclude. “One can watch basketball. One can both play and watch basketball for a thousand years. If you do not systematically track what the players do, and then uncover the statistical relationship between these actions and wins, you will never know why teams win and why they lose.”
What’s behind Ireland’s economic miracle—and G.M.’s financial crisis?


The years just after the Second World War were a time of great industrial upheaval in the United States. Strikes were commonplace. Workers moved from one company to another. Runaway inflation was eroding the value of wages. In the uncertain nineteen-forties, decease in the wake of the Depression and the war, order workers wanted security, cialis 40mg and in 1949 the head of the Toledo, Ohio, local of the United Auto Workers, Richard Gosser, came up with a proposal. The workers of Toledo needed pensions. But, he said, the pension plan should be regional, spread across the many small auto-parts makers, electrical-appliance manufacturers, and plastics shops in the Toledo area. That way, if workers switched jobs they could take their pension credits with them, and if a company went bankrupt its workers’ retirement would be safe. Every company in the area, Gosser proposed, should pay ten cents an hour, per worker, into a centralized fund.

The business owners of Toledo reacted immediately. “They were terrified,” says Jennifer Klein, a labor historian at Yale University, who has written about the Toledo case. “They organized a trade association to stop the plan. In the business press, they actually said, ‘This idea might be efficient and rational. But it’s too dangerous.’ Some of the larger employers stepped forward and said, ‘We’ll offer you a company pension. Forget about that whole other idea.’ They took on the costs of setting up an individual company pension, at great expense, in order to head off what they saw as too much organized power for workers in the region.”

A year later, the same issue came up in Detroit. The president of General Motors at the time was Charles E. Wilson, known as Engine Charlie. Wilson was one of the highest-paid corporate executives in America, earning $586,100 (and paying, incidentally, $430,350 in taxes). He was in contract talks with Walter Reuther, the national president of the U.A.W. The two men had already agreed on a cost-of-living allowance. Now Wilson went one step further, and, for the first time, offered every G.M. employee health-care benefits and a pension.

Reuther had his doubts. He lived in a northwest Detroit bungalow, and drove a 1940 Chevrolet. His salary was ten thousand dollars a year. He was the son of a Debsian Socialist, worked for the Socialist Party during his college days, and went to the Soviet Union in the nineteen-thirties to teach peasants how to be auto machinists. His inclination was to fight for changes that benefitted every worker, not just those lucky enough to be employed by General Motors. In the nineteen-thirties, unions had launched a number of health-care plans, many of which cut across individual company and industry lines. In the nineteen-forties, they argued for expanding Social Security. In 1945, when President Truman first proposed national health insurance, they cheered. In 1947, when Ford offered its workers a pension, the union voted it down. The labor movement believed that the safest and most efficient way to provide insurance against ill health or old age was to spread the costs and risks of benefits over the biggest and most diverse group possible. Walter Reuther, as Nelson Lichtenstein argues in his definitive biography, believed that risk ought to be broadly collectivized. Charlie Wilson, on the other hand, felt the way the business leaders of Toledo did: that collectivization was a threat to the free market and to the autonomy of business owners. In his view, companies themselves ought to assume the risks of providing insurance.

America’s private pension system is now in crisis. Over the past few years, American taxpayers have been put at risk of assuming tens of billions of dollars of pension liabilities from once profitable companies. Hundreds of thousands of retired steelworkers and airline employees have seen health-care benefits that were promised to them by their employers vanish. General Motors, the country’s largest automaker, is between forty and fifty billion dollars behind in the money it needs to fulfill its health-care and pension promises. This crisis is sometimes portrayed as the result of corporate America’s excessive generosity in making promises to its workers. But when it comes to retirement, health, disability, and unemployment benefits there is nothing exceptional about the United States: it is average among industrialized countries—more generous than Australia, Canada, Ireland, and Italy, just behind Finland and the United Kingdom, and on a par with the Netherlands and Denmark. The difference is that in most countries the government, or large groups of companies, provides pensions and health insurance. The United States, by contrast, has over the past fifty years followed the lead of Charlie Wilson and the bosses of Toledo and made individual companies responsible for the care of their retirees. It is this fact, as much as any other, that explains the current crisis. In 1950, Charlie Wilson was wrong, and Walter Reuther was right.


The key to understanding the pension business is something called the “dependency ratio,” and dependency ratios are best understood in the context of countries. In the past two decades, for instance, Ireland has gone from being one of the most economically backward countries in Western Europe to being one of the strongest: its growth rate has been roughly double that of the rest of Europe. There is no shortage of conventional explanations. Ireland joined the European Union. It opened up its markets. It invested well in education and economic infrastructure. It’s a politically stable country with a sophisticated, mobile workforce.

But, as the Harvard economists David Bloom and David Canning suggest in their study of the “Celtic Tiger,” of greater importance may have been a singular demographic fact. In 1979, restrictions on contraception that had been in place since Ireland’s founding were lifted, and the birth rate began to fall. In 1970, the average Irishwoman had 3.9 children. By the mid-nineteen-nineties, that number was less than two. As a result, when the Irish children born in the nineteen-sixties hit the workforce, there weren’t a lot of children in the generation just behind them. Ireland was suddenly free of the enormous social cost of supporting and educating and caring for a large dependent population. It was like a family of four in which, all of a sudden, the elder child is old enough to take care of her little brother and the mother can rejoin the workforce. Overnight, that family doubles its number of breadwinners and becomes much better off.

This relation between the number of people who aren’t of working age and the number of people who are is captured in the dependency ratio. In Ireland during the sixties, when contraception was illegal, there were ten people who were too old or too young to work for every fourteen people in a position to earn a paycheck. That meant that the country was spending a large percentage of its resources on caring for the young and the old. Last year, Ireland’s dependency ratio hit an all-time low: for every ten dependents, it had twenty-two people of working age. That change coincides precisely with the country’s extraordinary economic surge.

Demographers estimate that declines in dependency ratios are responsible for about a third of the East Asian economic miracle of the postwar era; this is a part of the world that, in the course of twenty-five years, saw its dependency ratio decline thirty-five per cent. Dependency ratios may also help answer the much-debated question of whether India or China has a brighter economic future. Right now, China is in the midst of what Joseph Chamie, the former director of the United Nations’ population division, calls the “sweet spot.” In the nineteen-sixties, China brought down its birth rate dramatically; those children are now grown up and in the workforce, and there is no similarly sized class of dependents behind them. India, on the other hand, reduced its birth rate much more slowly and has yet to hit the sweet spot. Its best years are ahead.

The logic of dependency ratios, of course, works equally powerfully in reverse. If your economy benefits by having a big bulge of working-age people, then your economy will have a harder time of it when that bulge generation retires, and there are relatively few workers to take their place. For China, the next few decades will be more difficult. “China will peak with a 1-to-2.6 dependency ratio between 2010 and 2015,” Bloom says. “But then it’s back to a little over 1-to-1.5 by 2050. That’s a pretty dramatic change. Thirty per cent of the Chinese population will be over sixty by 2050. That’s four hundred and thirty-two million people.” Demographers sometimes say that China is in a race to get rich before it gets old.

Economists have long paid attention to population growth, making the argument that the number of people in a country is either a good thing (spurring innovation) or a bad thing (depleting scarce resources). But an analysis of dependency ratios tells us that what’s critical is not just the growth of a population but its structure. “The introduction of demographics has reduced the need for the argument that there was something exceptional about East Asia or idiosyncratic to Africa,” Bloom and Canning write, in their study of the Irish economic miracle. “Once age-structure dynamics are introduced into an economic growth model, these regions are much closer to obeying common principles of economic growth.”

This is an important point. People have talked endlessly of Africa’s political and social and economic shortcomings and simultaneously of some magical cultural ingredient possessed by South Korea and Japan and Taiwan that has brought them success. But the truth is that sub-Saharan Africa has been mired in a debilitating 1-to-1 ratio for decades, and that proportion of dependency would frustrate and complicate economic development anywhere. Asia, meanwhile, has seen its demographic load lighten overwhelmingly in the past thirty years. Getting to a 1-to-2.5 ratio doesn’t make economic success inevitable. But, given a reasonably functional economic and political infrastructure, it certainly makes it a lot easier.

This demographic logic also applies to companies, since any employer that offers pensions and benefits to its employees has to deal with the consequences of its nonworker-to-worker ratio, just as a country does. An employer that promised, back in the nineteen-fifties, to pay for its employees’ health care when they were retired didn’t set aside the money for that while they were working. It just paid the bills as they came in: money generated by current workers was used to pay for the costs of taking care of past workers. Pensions worked roughly the same way. On the day a company set up a pension plan, it was immediately on the hook for all the years of service accumulated by employees up to that point: the worker who was sixty-four when the pension was started got a pension when he retired at sixty-five, even though he had been in the system only a year. That debt is called a “past service” obligation, and in some cases in the nineteen-forties and fifties the past-service obligations facing employers were huge. At Ford, the amount reportedly came to two hundred million dollars, or just under three thousand dollars per employee. At Bethlehem Steel, it came to four thousand dollars per worker.

Companies were required to put aside a little extra money every year to make up for that debt, with the hope of someday—twenty or thirty years down the line—becoming fully funded. In practice, though, that was difficult. Suppose that a company agrees to give its workers a pension of fifty dollars a month for every year of service. Several years later, after a round of contract negotiations, that multiple is raised to sixty dollars a month. That increase applies retroactively: now that company has a brand-new past-service obligation equal to another ten dollars for every month served by its wage employees. Or suppose the stock market goes into decline or interest rates fall, and the company discovers that its pension plan has less money than it had expected. Now it’s behind again: it has to go back to using the money generated by current workers in order to take care of the costs of past workers. “You start off in the hole,” Steven Sass, a pension expert at Boston College, “And the problem in these plans is that it’s very difficult to dig your way out.”

Charlie Wilson’s promise to his workers, then, contained an audacious assumption about G.M.’s dependency ratio: that the company would always have enough active workers to cover the costs of its retired workers—that it would always be like Ireland, and never like sub-Saharan Africa. Wilson’s promise, in other words, was actually a gamble. Is it any wonder that the prospect of private pensions made people like Walter Reuther so nervous?

The most influential management theorist of the twentieth century was Peter Drucker, who, in 1950, wrote an extraordinarily prescient article for Harper’s entitled “The Mirage of Pensions.” It ought to be reprinted for every steelworker, airline mechanic, and autoworker who is worried about his retirement. Drucker simply couldn’t see how the pension plans on the table at companies like G.M. could ever work. “For such a plan to give real security, the financial strength of the company and its economic success must be reasonably secure for the next forty years,” Drucker wrote. “But is there any one company or any one industry whose future can be predicted with certainty for even ten years ahead?” He concluded, “The recent pension plans thus offer no more security against the big bad wolf of old age than the little piggy’s house of straw.”


In the mid-nineteen-fifties, the largest steel mill in the world was at Sparrows Point, just east of Baltimore, on the Chesapeake Bay. It was owned by Bethlehem Steel, one of the nation’s grandest industrial enterprises. The steel for the Golden Gate Bridge came from Sparrows Point, as did the cables for the George Washington Bridge, and the materials for countless guns and planes and ships that helped win both world wars. Sparrows Point, a so-called integrated mill, used a method of making steel that dated back to the nineteenth century. Coke and iron, the raw materials, were combined in a blast furnace to make liquid pig iron. The pig iron was poured into a vast oven, known as an open-hearth furnace, to make molten steel. The steel was poured into pots to make ingots. The ingots were cooled, reheated, and fed into a half-mile-long rolling mill and turned into semi-finished shapes, which eventually became girders for the construction industry or wafer-thin sheets for beer cans or galvanized panels for the automobile industry. Open-hearth steelmaking was expensive and time-consuming. It required great amounts of energy, water, and space. Sparrows Point stretched four miles from one end to the other. Most important, it required lots and lots of people. Sparrows Point, at its height, employed tens of thousands of them. As Mark Reutter demonstrates in “Making Steel,” his comprehensive history of Sparrows Point, it was not just a steel mill. It was a city.

In 1956, Eugene Grace, the head of Bethlehem Steel, was the country’s best- paid executive. Eleven of the country’s eighteen top-earning executives that year, in fact, worked for Bethlehem Steel. In 1955, when the American Iron and Steel Institute had its annual meeting, at the Waldorf-Astoria, in New York, the No. 2 at Bethlehem Steel, Arthur Homer, made a bold forecast: domestic demand for steel, he said, would increase by fifty per cent over the next fifteen years. “As someone has said, the American people are wanters,” he told the audience of twelve hundred industry executives. “Their wants are going to require a great deal of steel.”

But Big Steel didn’t get bigger. It got smaller. Imports began to take a larger and larger share of the American steel market. The growing use of aluminum, concrete, and plastic cut deeply into the demand for steel. And the steelmaking process changed. Instead of laboriously making steel from scratch, with coke and iron ore, factories increasingly just melted down scrap metal. The open-hearth furnace was replaced with the basic oxygen furnace, which could make the same amount of steel in about a tenth of the time. Steelmakers switched to continuous casting, which meant that you skipped the ingot phase altogether and poured your steel products directly out of the furnace. As a result, steelmakers like Bethlehem were no longer hiring young workers to replace the people who retired. They were laying people off by the thousands. But every time they laid off another employee they turned a money-making steelworker into a money-losing retiree—and their dependency ratio got a little worse. According to Reutter, Bethlehem had a hundred and sixty-four thousand workers in 1957. By the mid-to-late-nineteen-eighties, it was down to thirty-five thousand workers, and employment at Sparrows Point had fallen to seventy-nine hundred. In 2001, Bethlehem, just shy of its hundredth birthday, declared bankruptcy. It had twelve thousand active employees and ninety thousand retirees and their spouses drawing benefits. It had reached what might be a record-setting dependency ratio of 7.5 pensioners for every worker.

What happened to Bethlehem, of course, is what happened throughout American industry in the postwar period. Technology led to great advances in productivity, so that when the bulge of workers hired in the middle of the century retired and began drawing pensions, there was no one replacing them in the workforce. General Motors today makes more cars and trucks than it did in the early nineteen-sixties, but it does so with about a third of the employees. In 1962, G.M. had four hundred and sixty-four thousand U.S. employees and was paying benefits to forty thousand retirees and their spouses, for a dependency ratio of one pensioner to 11.6 employees. Last year, it had a hundred and forty-one thousand workers and paid benefits to four hundred and fifty-three thousand retirees, for a dependency ratio of 3.2 to 1.

Looking at General Motors and the old-line steel companies in demographic terms substantially changes the way we understand their problems. It is a commonplace assumption, for instance, that they were undone by overly generous union contracts. But, when dependency ratios start getting up into the 3-to-1 to 7-to-1 range, the issue is not so much what you are paying each dependent as how many dependents you are paying. “There is this notion that there is a Cadillac being provided to all these retirees,” Ron Bloom, a senior official at the United Steelworkers, says. “It’s not true. The truth is seventy-five-year-old widows living on less than three hundred dollars to four hundred dollars a month. It’s just that there’s a lot of them.”

A second common assumption is that fading industrial giants like G.M. and Bethlehem are victims of their own managerial incompetence. In various ways, they undoubtedly are. But, with respect to the staggering burden of benefit obligations, what got them in trouble isn’t what they did wrong; it is what they did right. They got in trouble in the nineteen-nineties because they were around in the nineteen-fifties—and survived to pay for the retirement of the workers they hired forty years ago. They got in trouble because they innovated, and became more efficient in their use of labor.

“We are making as much steel as we made thirty years ago with twenty-five per cent of the workforce,” Michael Locker, a steel-industry consultant, says. “And it is a much higher quality of steel, too. There is simply no comparison. That change recasts the industry and it recasts the workforce. You get this enormous bulge. It’s abnormal. It’s not predicted, and it’s not funded. Is that the fault of the steelworkers? Is that the fault of the companies?”

Here, surely, is the absurdity of a system in which individual employers are responsible for providing their own employee benefits. It penalizes companies for doing what they ought to do. General Motors, by American standards, has an old workforce: its average worker is much older than, say, the average worker at Google. That has an immediate effect: health-care costs are a linear function of age. The average cost of health insurance for an employee between the ages of thirty-five and thirty-nine is $3,759 a year, and for someone between the ages of sixty and sixty-four it is $7,622. This goes a long way toward explaining why G.M. has an estimated sixty-two billion dollars in health-care liabilities. The current arrangement discourages employers from hiring or retaining older workers. But don’t we want companies to retain older workers—to hire on the basis of ability and not age? In fact, a system in which companies shoulder their own benefits is ultimately a system that penalizes companies for offering any benefits at all. Many employers have simply decided to let their workers fend for themselves. Given what has so publicly and disastrously happened to companies like General Motors, can you blame them?

Or consider the continuous round of discounts and rebates that General Motors—a company that lost $8.6 billion last year—has been offering to customers. If you bought a Chevy Tahoe this summer, G.M. would give you zero-per-cent financing, or six thousand dollars cash back. Surely, if you are losing money on every car you sell, as G.M. is, cutting car prices still further in order to boost sales doesn’t make any sense. It’s like the old Borsht-belt joke about the haberdasher who lost money on every hat he made but figured he’d make up the difference on volume. The economically rational thing for G.M. to do would be to restructure, and sell fewer cars at a higher profit margin—and that’s what G.M. tried to do this summer, announcing plans to shutter plants and buy out the contracts of thirty-five thousand workers. But buyouts, which turn active workers into pensioners, only worsen the company’s dependency ratio. Last year, G.M. covered the costs of its four hundred and fifty-three thousand retirees and their dependents with the revenue from 4.5 million cars and trucks. How is G.M. better off covering the costs of four hundred and eighty-eighty thousand dependents with the revenue from, say, 4.2 million cars and trucks? This is the impossible predicament facing the company’s C.E.O., Rick Wagoner. Demographic logic requires him to sell more cars and hire more workers; financial logic requires him to sell fewer cars and hire fewer workers.

Under the circumstances, one of the great mysteries of contemporary American politics is why Wagoner isn’t the nation’s leading proponent of universal health care and expanded social welfare. That’s the only way out of G.M.’s dilemma. But, from Wagoner’s reticence on the issue, you’d think that it was still 1950, or that Wagoner believes he’s the Prime Minister of Ireland. “One thing I’ve learned is that corporate America has got much more class solidarity than we do—meaning union people,” the U.S.W.’s Ron Bloom says. “They really are afraid of getting thrown out of their country clubs, even though their objective ought to be maximizing value for their shareholders.”

David Bloom, the Harvard economist, once did a calculation in which he combined the dependency ratios of Africa and Western Europe. He found that they fit together almost perfectly; that is, Africa has plenty of young people and not a lot of older people and Western Europe has plenty of old people and not a lot of young people, and if you combine the two you have an even distribution of old and young. “It makes you think that if there is more international migration, that could smooth things out,” Bloom said.

Of course, you can’t take the populations of different countries and different cultures and simply merge them, no matter how much demographic sense that might make. But you can do that with companies within an economy. If the retiree obligations of Bethlehem Steel had been pooled with those of the much younger industries that supplanted steel—aluminum, say, or plastic—Bethlehem Steel might have made it. If you combined the obligations of G.M., with its four hundred and fifty-three thousand retirees, and the American manufacturing operations of Toyota, with a mere two hundred and fifty-eight retirees, Toyota could help G.M. shoulder its burden, and thirty or forty years from now—when those G.M. retirees are dead and Toyota’s now youthful workforce has turned gray—G.M. could return the favor. For that matter, if you pooled the obligations of every employer in the country, no company would go bankrupt just because it happened to employ older people, or it happened to have been around for a while, or it happened to have made the transformation from open-hearth furnaces and ingot-making to basic oxygen furnaces and continuous casting. This is what Walter Reuther and the other union heads understood more than fifty years ago: that in the free-market system it makes little sense for the burdens of insurance to be borne by one company. If the risks of providing for health care and old-age pensions are shared by all of us, then companies can succeed or fail based on what they do and not on the number of their retirees.


When Bethlehem Steel filed for bankruptcy, it owed about four billion dollars to its pension plan, and had another three billion dollars in unmet health-care obligations. Two years later, in 2003, the pension fund was terminated and handed over to the federal government’s Pension Benefit Guaranty Corporation. The assets of the company—Sparrows Point and a handful of other steel mills in the Midwest—were sold to the New York-based investor Wilbur Ross.

Ross acted quickly. He set up a small trust fund to help defray Bethlehem’s unmet retiree health-care costs, cut a deal with the union to streamline work rules, put in place a new 401(k) savings plan—and then started over. The new Bethlehem Steel had a dependency ratio of 0 to 1. Within about six months, it was profitable. The main problem with the American steel business wasn’t the steel business, Ross showed. It was all the things that had nothing to do with the steel business.

Not long ago, Ross sat in his sparse midtown office and explained what he had learned from his rescue of Bethlehem. Ross is in his sixties, a Yale- and Harvard-educated patrician with small rectangular glasses and impeccable manners. Outside his office, by the elevator, was a large sculpture of a bull, papered over from head to hoof with stock tables.

“When we showed up to the Bethlehem board to approve the deal, they had an army of people there,” Ross said. “The whole board was there, the whole senior management was there, people from Credit Suisse and Greenhill were there. They must have had about fifty or sixty people there for a deal that was already done. So my partner and I—just the two of us—show up, and they say, ‘Well, we should wait for the rest of your team.’ And we said, ‘There is no rest of the team, there is just the two of us.’ It said the whole thing right there.”

Ross isn’t a fan of old-style pensions, because they make it impossible to run a company efficiently. “When a company gets in trouble and restructures,” he said, those underfunded pension funds “will eat it alive.” And how much sense does employer-provided health insurance make? Bethlehem made promises to its employees, years ago, to give them medical insurance in exchange for their labor, and when the company ran into trouble those promises simply evaporated. “Every country against which we compete has universal health care,” he said. “That means we probably face a fifteen-per-cent cost disadvantage versus foreigners for no other reason than historical accident. . . . The randomness of our system is just not going to work.”

This is what Walter Reuther believed. He went along with Wilson’s scheme in 1950 because he thought that agreeing with Wilson was the surest way of getting Wilson and the other captains of industry to agree with him. “Reuther and his brain trust had a theory of capitalism,” Nelson Lichtenstein, the Reuther biographer, says. “It was: If we force G.M. to pay extra, we can create an incentive for G.M. to join our side.” Reuther believed, in other words, that when American corporations reached the point where they couldn’t make their business more efficient without making it less profitable, when their dependency ratios soared to unimaginable heights, when they got tens of billions behind in their health-care obligations, when the cost of carrying thou-sands of retirees forced them to stare bankruptcy in the face, they would come around to the idea that the markets work best when the burdens of benefits are broadly shared. It has taken half a century, but the world may finally be catching up with Walter Reuther.

In 1925, no rx a young American physicist was doing graduate work at Cambridge University, try in England. He was depressed. He was fighting with his mother and had just broken up with his girlfriend. His strength was in theoretical physics, information pills but he was being forced to sit in a laboratory making thin films of beryllium. In the fall of that year, he dosed an apple with noxious chemicals from the lab and put it on the desk of his tutor, Patrick Blackett. Blackett, luckily, didn’t eat the apple. But school officials found out what happened, and arrived at a punishment: the student was to be put on probation and ordered to go to London for regular sessions with a psychiatrist.

Probation? These days, we routinely suspend or expel high-school students for doing infinitely less harmful things, like fighting or drinking or taking drugs—that is, for doing the kinds of things that teen-agers do. This past summer, Rhett Bomar, the starting quarterback for the University of Oklahoma Sooners, was cut from the team when he was found to have been “overpaid” (receiving wages for more hours than he worked, with the apparent complicity of his boss) at his job at a car dealership. Even in Oklahoma, people seemed to think that kicking someone off a football team for having cut a few corners on his job made perfect sense. This is the age of zero tolerance. Rules are rules. Students have to be held accountable for their actions. Institutions must signal their expectations firmly and unambiguously: every school principal and every college president, these days, reads from exactly the same script. What, then, of a student who gives his teacher a poisoned apple? Surely he ought to be expelled from school and sent before a judge.

Suppose you cared about the student, though, and had some idea of his situation and his potential. Would you feel the same way? You might. Trying to poison your tutor is no small infraction. Then again, you might decide, as the dons at Cambridge clearly did, that what had happened called for a measure of leniency. They knew that the student had never done anything like this before, and that he wasn’t well. And they knew that to file charges would almost certainly ruin his career. Cambridge wasn’t sure that the benefits of enforcing the law, in this case, were greater than the benefits of allowing the offender an unimpeded future.

Schools, historically, have been home to this kind of discretionary justice. You let the principal or the teacher decide what to do about cheating because you know that every case of cheating is different—and, more to the point, that every cheater is different. Jimmy is incorrigible, and needs the shock of expulsion. But Bobby just needs a talking to, because he’s a decent kid, and Mary and Jane cheated because the teacher foolishly stepped out of the classroom in the middle of the test, and the temptation was simply too much. A Tennessee study found that after zero-tolerance programs were adopted by the state’s public schools the frequency of targeted offenses soared: the firm and unambiguous punishments weren’t deterring bad behavior at all. Is that really a surprise? If you’re a teen-ager, the announcement that an act will be sternly punished doesn’t always sink in, and it isn’t always obvious when you’re doing the thing you aren’t supposed to be doing. Why? Because you’re a teen-ager.

Somewhere along the way—perhaps in response to Columbine—we forgot the value of discretion in disciplining the young. “Ultimately, they have to make right decisions,” the Oklahoma football coach, Bob Stoops, said of his players, after jettisoning his quarterback. “When they do not, the consequences are serious.” Open and shut: he sounded as if he were talking about a senior executive of Enron, rather than a college sophomore whose primary obligation at Oklahoma was to throw a football in the direction of young men in helmets. You might think that if the University of Oklahoma was so touchy about its quarterback being “overpaid” it ought to have kept closer track of his work habits with an on-campus job. But making a fetish of personal accountability conveniently removes the need for institutional accountability. (We court-martial the grunts who abuse prisoners, not the commanding officers who let the abuse happen.) To acknowledge that the causes of our actions are complex and muddy seems permissive, and permissiveness is the hallmark of an ideology now firmly in disgrace. That conservative patron saint Whittaker Chambers once defined liberalism as Christ without the Crucifixion. But punishment without the possibility of redemption is worse: it is the Crucifixion without Christ.

As for the student whose career Cambridge saved? He left at the end of the academic year and went to study at the University of Göttingen, where he made important contributions to quantum theory. Later, after a brilliant academic career, he was entrusted with leading one of the most critical and morally charged projects in the history of science. His name was Robert Oppenheimer.
What if you built a machine to predict hit movies?


One sunny afternoon not long ago, adiposity Dick Copaken sat in a booth at Daniel, mind one of those hushed, exclusive restaurants on Manhattan’s Upper East Side where the waiters glide spectrally from table to table. He was wearing a starched button-down shirt and a blue blazer. Every strand of his thinning hair was in place, and he spoke calmly and slowly, his large pink Charlie Brown head bobbing along evenly as he did. Copaken spent many years as a partner at the white-shoe Washington, D.C., firm Covington & Burling, and he has a lawyer’s gravitas. One of his best friends calls him, admiringly, “relentless.” He likes to tell stories. Yet he is not, strictly, a storyteller, because storytellers are people who know when to leave things out, and Copaken never leaves anything out: each detail is adduced, considered, and laid on the table—and then adjusted and readjusted so that the corners of the new fact are flush with the corners of the fact that preceded it. This is especially true when Copaken is talking about things that he really cares about, such as questions of international law or his grandchildren or, most of all, the movies.

Dick Copaken loves the movies. His friend Richard Light, a statistician at Harvard, remembers summer vacations on Cape Cod with the Copakens, when Copaken would take his children and the Light children to the movies every day. “Fourteen nights out of fourteen,” Light said. “Dick would say at seven o’clock, ‘Hey, who’s up for the movies?’ And, all by himself, he would take the six kids to the movies. The kids had the time of their lives. And Dick would come back and give, with a completely straight face, a rigorous analysis of how each movie was put together, and the direction and the special effects and the animation.” This is a man who has seen two or three movies a week for the past fifty years, who has filed hundreds of plots and characters and scenes away in his mind, and at Daniel he was talking about a movie that touched him as much as any he’d ever seen.

“Nobody’s heard of it,” he said, and he clearly regarded this fact as a minor tragedy. “It’s called ‘Dear Frankie.’ I watched it on a Virgin Atlantic flight because it was the only movie they had that I hadn’t already seen. I had very low expectations. But I was blown away.” He began, in his lawyer-like manner, to lay out the plot. It takes place in Scotland. A woman has fled an abusive relationship with her infant son and is living in a port town. The boy, now nine, is deaf, and misses the father he has never known. His mother has told him that his father is a sailor on a ship that rarely comes to shore, and has suggested that he write his father letters. These she intercepts, and replies to, writing as if she were the father. One day, the boy finds out that what he thinks is his father’s ship is coming to shore. The mother has to find a man to stand in for the father. She does. The two fall in love. Unexpectedly, the real father reëmerges. He’s dying, and demands to see his son. The mother panics. Then the little boy reveals his secret: he knew about his mother’s ruse all along.

“I was in tears over this movie,” Copaken said. “You know, sometimes when you see a movie in the air you’re in such an out-of-body mood that things get exaggerated. So when I got home I sat down and saw it another time. I was bawling again, even though I knew what was coming.” Copaken shook his head, and then looked away. His cheeks were flushed. His voice was suddenly thick. There he was, a buttoned-down corporate lawyer, in a hushed restaurant where there is practically a sign on the wall forbidding displays of human emotion—and he was crying, a third time. “That absolutely hits me,” he said, his face still turned away. “He knew all along what the mother was doing.” He stopped to collect himself. “I can’t even retell the damn story without getting emotional.”

He tried to explain why he was crying. There was the little boy, first of all. He was just about the same age as Copaken’s grandson Jacob. So maybe that was part of it. Perhaps, as well, he was reacting to the idea of an absent parent. His own parents, Albert and Silvia, ran a modest community-law practice in Kansas City, and would shut down their office whenever Copaken or his brother had any kind of school activity or performance. In the Copaken world, it was an iron law that parents had to be present. He told a story about representing the Marshall Islands in negotiations with the U.S. government during the Cold War. A missile-testing range on the island was considered to be strategically critical. The case was enormously complex—involving something like fifty federal agencies and five countries—and, just as the negotiations were scheduled to begin, Copaken learned of a conflict: his eldest daughter was performing the lead role in a sixth-grade production of “The Wiz.” “I made an instant decision,” Copaken said. He told the President of the Marshall Islands that his daughter had to come first. Half an hour passed. “I get a frantic call from the State Department, very high levels: ‘Dick, I got a call from the President of the Marshall Islands. What’s going on?’ I told him. He said, ‘Dick, are you putting in jeopardy the national security of the United States for a sixth-grade production?’ ” In the end, the negotiations were suspended while Copaken flew home from Hawaii. “The point is,” Copaken said, “that absence at crucial moments has been a worry to me, and maybe this movie just grabbed at that issue.”

He stopped, seemingly dissatisfied. Was that really why he’d cried? Hollywood is awash in stories of bad fathers and abandoned children, and Copaken doesn’t cry in fancy restaurants every time he thinks of one of them. When he tried to remember the last time he cried at the movies, he was stumped. So he must have been responding to something else, too—some detail, some unconscious emotional trigger in the combination of the mother and the boy and the Scottish seaside town and the ship and the hired surrogate and the dying father. To say that he cried at “Dear Frankie” because of that lonely fatherless boy was as inadequate as saying that people cried at the death of Princess Diana because she was a beautiful princess. Surely it mattered as well that she was killed in the company of her lover, a man distrusted by the Royal Family. ”t this “Romeo and Juliet”? And surely it mattered that she died in a tunnel, and that the tunnel was in Paris, and that she was chased by motorbikes, and that she was blond and her lover was dark—because each one of those additional narrative details has complicated emotional associations, and it is the subtle combination of all these associations that makes us laugh or choke up when we remember a certain movie, every single time, even when we’re sitting in a fancy restaurant.

Of course, the optimal combination of all those elements is a mystery. That’s why it’s so hard to make a really memorable movie, and why we reward so richly the few people who can. But suppose you really, really loved the movies, and suppose you were a relentless type, and suppose you used all of the skills you’d learned during the course of your career at the highest rungs of the law to put together an international team of story experts. Do you think you could figure it out?


The most famous dictum about Hollywood belongs to the screenwriter William Goldman. “Nobody knows anything,” Goldman wrote in “Adventures in the Screen Trade” a couple of decades ago. “Not one person in the entire motion picture field knows for a certainty what’s going to work. Every time out it’s a guess.” One of the highest-grossing movies in history, “”Raiders of the Lost Ark,” was offered to every studio in Hollywood, Goldman writes, and every one of them turned it down except Paramount: “Why did Paramount say yes? Because nobody knows anything. And why did all the other studios say no? Because nobody knows anything. And why did Universal, the mightiest studio of all, pass on Star Wars? . . . Because nobody, nobody—not now, not ever—knows the least goddamn thing about what is or isn’t going to work at the box office.”

What Goldman was saying was a version of something that has long been argued about art: that there is no way of getting beyond one’s own impressions to arrive at some larger, objective truth. There are no rules to art, only the infinite variety of subjective experience. “Beauty is no quality in things themselves,” the eighteenth-century Scottish philosopher David Hume wrote. “It exists merely in the mind which contemplates them; and each mind perceives a different beauty.” Hume might as well have said that nobody knows anything.

But Hume had a Scottish counterpart, Lord Kames, and Lord Kames was equally convinced that traits like beauty, sublimity, and grandeur were indeed reducible to a rational system of rules and precepts. He devised principles of congruity, propriety, and perspicuity: an elevated subject, for instance, must be expressed in elevated language; sound and signification should be in concordance; a woman was most attractive when in distress; depicted misfortunes must never occur by chance. He genuinely thought that the superiority of Virgil’s hexameters to Horace’s could be demonstrated with Euclidean precision, and for every Hume, it seems, there has always been a Kames—someone arguing that if nobody knows anything it is only because nobody’s looking hard enough.

In a small New York loft, just below Union Square, for example, there is a tech startup called Platinum Blue that consults for companies in the music business. Record executives have tended to be Humean: though they can tell you how they feel when they listen to a song, they don’t believe anyone can know with confidence whether a song is going to be a hit, and, historically, fewer than twenty per cent of the songs picked as hits by music executives have fulfilled those expectations. Platinum Blue thinks it can do better. It has a proprietary computer program that uses “spectral deconvolution software” to measure the mathematical relationships among all of a song’s structural components: melody, harmony, beat, tempo, rhythm, octave, pitch, chord progression, cadence, sonic brilliance, frequency, and so on. On the basis of that analysis, the firm believes it can predict whether a song is likely to become a hit with eighty-per-cent accuracy. Platinum Blue is staunchly Kamesian, and, if you have a field dominated by those who say there are no rules, it is almost inevitable that someone will come along and say that there are. The head of Platinum Blue is a man named Mike McCready, and the service he is providing for the music business is an exact model of what Dick Copaken would like to do for the movie business.

McCready is in his thirties, baldish and laconic, with rectangular hipster glasses. His offices are in a large, open room, with a row of windows looking east, across the rooftops of downtown Manhattan. In the middle of the room is a conference table, and one morning recently McCready sat down and opened his laptop to demonstrate the Platinum Blue technology. On his screen was a cluster of thousands of white dots, resembling a cloud. This was a “map” of the songs his group had run through its software: each dot represented a single song, and each song was positioned in the cloud according to its particular mathematical signature. “You could have one piano sonata by Beethoven at this end and another one here,” McCready said, pointing at the opposite end, “as long as they have completely different chord progressions and completely different melodic structures.”

McCready then hit a button on his computer, which had the effect of eliminating all the songs that had not made the Billboard Top 30 in the past five years. The screen went from an undifferentiated cloud to sixty discrete clusters. This is what the universe of hit songs from the past five years looks like structurally; hits come out of a small, predictable, and highly conserved set of mathematical patterns. “We take a new CD far in advance of its release date,” McCready said. “We analyze all twelve tracks. Then we overlay them on top of the already existing hit clusters, and what we can tell a record company is which of those songs conform to the mathematical pattern of past hits. Now, that doesn’t mean that they will be hits. But what we are saying is that, almost certainly, songs that fall outside these clusters will not be —regardless of how much they sound and feel like hit songs, and regardless of how positive your call-out research or focus-group research is.” Four years ago, when McCready was working with a similar version of the program at a firm in Barcelona, he ran thirty just-released albums, chosen at random, through his system. One stood out. The computer said that nine of the fourteen songs on the album had clear hit potential—which was unheard of. Nobody in his group knew much about the artist or had even listened to the record before, but the numbers said the album was going to be big, and McCready and his crew were of the belief that numbers do not lie. “Right around that time, a local newspaper came by and asked us what we were doing,” McCready said. “We explained the hit-prediction thing, and that we were really turned on to a record by this artist called Norah Jones.” The record was “Come Away with Me.” It went on to sell twenty million copies and win eight Grammy awards.


The strength of McCready’s analysis is its precision. This past spring, for instance, he analyzed “Crazy,” by Gnarls Barkley. The computer calculated, first of all, the song’s Hit Grade—that is, how close it was to the center of any of those sixty hit clusters. Its Hit Grade was 755, on a scale where anything above 700 is exceptional. The computer also found that “Crazy” belonged to the same hit cluster as Dido’s “Thank You,” James Blunt’s “You’re Beautiful,” and Ashanti’s “Baby,” as well as older hits like “Let Me Be There,” by Olivia Newton-John, and “One Sweet Day,” by Mariah Carey, so that listeners who liked any of those songs would probably like “Crazy,” too. Finally, the computer gave “Crazy” a Periodicity Grade—which refers to the fact that, at any given time, only twelve to fifteen hit clusters are “active,” because from month to month the particular mathematical patterns that excite music listeners will shift around. “Crazy” ‘s periodicity score was 658—which suggested a very good fit with current tastes. The data said, in other words, that “Crazy” was almost certainly going to be huge—and, sure enough, it was.

If “Crazy” hadn’t scored so high, though, the Platinum Blue people would have given the song’s producers broad suggestions for fixing it. McCready said, “We can tell a producer, ‘These are the elements that seem to be pushing your song into the hit cluster. These are the variables that are pulling your song away from the hit cluster. The problem seems to be in your bass line.’ And the producer will make a bunch of mixes, where they do something different with the bass lines—increase the decibel level, or muddy it up. Then they come back to us. And we say, ‘Whatever you were doing with mix No. 3, do a little bit more of that and you’ll be back inside the hit cluster.'”

McCready stressed that his system didn’t take the art out of hit-making. Someone still had to figure out what to do with mix No. 3, and it was entirely possible that whatever needed to be done to put the song in the hit cluster wouldn’t work, because it would make the song sound wrong—and in order to be a hit a song had to sound right. Still, for the first time you wouldn’t be guessing about what needed to be done. You would know. And what you needed to know in order to fix the song was much simpler than anyone would have thought. McCready didn’t care about who the artist was, or the cleverness of the lyrics. He didn’t even have a way of feeding lyrics into his computer. He cared only about a song’s underlying mathematical structure. “If you go back to the popular melodies written by Beethoven and Mozart three hundred years ago,” he went on, “they conform to the same mathematical patterns that we are looking at today. What sounded like a beautiful melody to them sounds like a beautiful melody to us. What has changed is simply that we have come up with new styles and new instruments. Our brains are wired in a way—we assume—that keeps us coming back, again and again, to the same answers, the same pleasure centers.” He had sales data and Top 30 lists and deconvolution software, and it seemed to him that if you put them together you had an objective way of measuring something like beauty. “We think we’ve figured out how the brain works regarding musical taste,” McCready said.

It requires a very particular kind of person, of course, to see the world as a code waiting to be broken. Hume once called Kames “the most arrogant man in the world,” and to take this side of the argument you have to be. Kames was also a brilliant lawyer, and no doubt that matters as well, because to be a good lawyer is to be invested with a reverence for rules. (Hume defied his family’s efforts to make him a lawyer.) And to think like Kames you probably have to be an outsider. Kames was born Henry Home, to a farming family, and grew up in the sparsely populated cropping-and-fishing county of Berwickshire; he became Lord Kames late in life, after he was elevated to the bench. (Hume was born and reared in Edinburgh.) His early published work was about law and its history, but he soon wandered into morality, religion, anthropology, soil chemistry, plant nutrition, and the physical sciences, and once asked his friend Benjamin Franklin to explain the movement of smoke in chimneys. Those who believe in the power of broad patterns and rules, rather than the authority of individuals or institutions, are not intimidated by the boundaries and hierarchies of knowledge. They don’t defer to the superior expertise of insiders; they set up shop in a small loft somewhere downtown and take on the whole music industry at once. The difference between Hume and Kames is, finally, a difference in kind, not degree. You’re either a Kamesian or you’re not. And if you were to create an archetypal Kamesian—to combine lawyerliness, outsiderness, and supreme self-confidence in one dapper, Charlie Brown-headed combination? You’d end up with Dick Copaken.

“I remember when I was a sophomore in high school and I went into the bathroom once to wash my hands,” Copaken said. “I noticed the bubbles on the sink, and it fascinated me the way these bubbles would form and move around and float and reform, and I sat there totally transfixed. My father called me, and I didn’t hear him. Finally, he comes in. ‘Son. What the . . . are you all right?’ I said, ‘Bubbles, Dad, look what they do.’ He said, ‘Son, if you’re going to waste your time, waste it on something that may have some future consequence.’ Well, I kind of rose to the challenge. That summer, I bicycled a couple of miles to a library in Kansas City and I spent every day reading every book and article I could find on bubbles.”

Bubbles looked completely random, but young Copaken wasn’t convinced. He built a bubble-making device involving an aerator from a fish tank, and at school he pleaded with the math department to teach him the quadratic equations he needed to show why the bubbles formed the way they did. Then he devised an experiment, and ended up with a bronze medal at the International Science Fair. His interest in bubbles was genuine, but the truth is that almost anything could have caught Copaken’s eye: pop songs, movies, the movement of chimney smoke. What drew him was not so much solving this particular problem as the general principle that problems were solvable—that he, little Dick Copaken from Kansas City, could climb on his bicycle and ride to the library and figure out something that his father thought wasn’t worth figuring out.

Copaken has written a memoir of his experience defending the tiny Puerto Rican islands of Culebra and Vieques against the U.S. Navy, which had been using their beaches for target practice. It is a riveting story. Copaken takes on the vast Navy bureaucracy, armed only with arcane provisions of environmental law. He investigates the nesting grounds of the endangered hawksbill turtle, and the mating habits of a tiny yet extremely loud tree frog known as the coqui, and at one point he transports four frozen whale heads from the Bahamas to Harvard Medical School. Copaken wins. The Navy loses.

The memoir reads like a David-and-Goliath story. It isn’t. David changed the rules on Goliath. He brought a slingshot to a sword fight. People like Copaken, though, don’t change the rules; they believe in rules. Copaken would have agreed to sword-on-sword combat. But then he would have asked the referee for a stay, deposed Goliath and his team at great length, and papered him with brief after brief until he conceded that his weapon did not qualify as a sword under §48(B)(6)(e) of the Samaria Convention of 321 B.C. (The Philistines would have settled.) And whereas David knew that he couldn’t win a conventional fight with Goliath, the conviction that sustained Copaken’s long battle with the Navy was, to the contrary, that so long as the battle remained conventional—so long as it followed the familiar pathways of the law and of due process—he really could win. Dick Copaken didn’t think he was an underdog at all. If you believe in rules, Goliath is just another Philistine, and the Navy is just another plaintiff. As for the ineffable mystery of the Hollywood blockbuster? Well, Mr. Goldman, you may not know anything. But I do.


Dick Copaken has a friend named Nick Meaney. They met on a case years ago. Meaney has thick dark hair. He is younger and much taller than Copaken, and seems to regard his friend with affectionate amusement. Meaney’s background is in risk management, and for years he’d been wanting to bring the principles of that world to the movie business. In 2003, Meaney and Copaken were driving through the English countryside to Durham when Meaney told Copaken about a friend of his from college. The friend and his business partner were students of popular narrative: the sort who write essays for obscure journals serving the small band of people who think deeply about, say, the evolution of the pilot episode in transnational TV crime dramas. And, for some time, they had been developing a system for evaluating the commercial potential of stories. The two men, Meaney told Copaken, had broken down the elements of screenplay narrative into multiple categories, and then drawn on their encyclopedic knowledge of television and film to assign scripts a score in each of those categories—creating a giant screenplay report card. The system was extraordinarily elaborate. It was under constant refinement. It was also top secret. Henceforth, Copaken and Meaney would refer to the two men publicly only as “Mr. Pink” and “Mr. Brown,” an homage to “Reservoir Dogs.”

“The guy had a big wall, and he started putting up little Post-its covering everything you can think of,” Copaken said. It was unclear whether he was talking about Mr. Pink or Mr. Brown or possibly some Obi-Wan Kenobi figure from whom Mr. Pink and Mr. Brown first learned their trade. “You know, the star wears a blue shirt. The star doesn’t zip up his pants. Whatever. So he put all these factors up and began moving them around as the scripts were either successful or unsuccessful, and he began grouping them and eventually this evolved to a kind of ad-hoc analytical system. He had no theory as to what would work, he just wanted to know what did work.”

Copaken and Meaney also shared a fascination with a powerful kind of computerized learning system called an artificial neural network. Neural networks are used for data mining—to look for patterns in very large amounts of data. In recent years, they have become a critical tool in many industries, and what Copaken and Meaney realized, when they thought about Mr. Pink and Mr. Brown, was that it might now be possible to bring neural networks to Hollywood. They could treat screenplays as mathematical propositions, using Mr. Pink and Mr. Brown’s categories and scores as the motion-picture equivalents of melody, harmony, beat, tempo, rhythm, octave, pitch, chord progression, cadence, sonic brilliance, and frequency.

Copaken and Meaney brought in a former colleague of Meaney’s named Sean Verity, and the three of them signed up Mr. Pink and Mr. Brown. They called their company Epagogix—a reference to Aristotle’s discussion of epagogic, or inductive, learning—and they started with a “training set” of screenplays that Mr. Pink and Mr. Brown had graded. Copaken and Meaney won’t disclose how many scripts were in the training set. But let’s say it was two hundred. Those scores—along with the U.S. box-office receipts for each of the films made from those screenplays—were fed into a neural network built by a computer scientist of Meaney’s acquaintance. “I can’t tell you his name,” Meaney said, “but he’s English to his bootstraps.” Mr. Bootstraps then went to work, trying to use Mr. Pink and Mr. Brown’s scoring data to predict the box-office receipts of every movie in the training set. He started with the first film and had the neural network make a guess: maybe it said that the hero’s moral crisis in act one, which rated a 7 on the 10-point moral-crisis scale, was worth $7 million, and having a gorgeous red-headed eighteen-year-old female lead whose characterization came in at 6.5 was worth $3 million and a 9-point bonding moment between the male lead and a four-year-old boy in act three was worth $2 million, and so on, putting a dollar figure on every grade on Mr. Pink and Mr. Brown’s report card until the system came up with a prediction. Then it compared its guess with how that movie actually did. Was it close? Of course not. The neural network then went back and tried again. If it had guessed $20 million and the movie actually made $110 million, it would reweight the movie’s Pink/Brown scores and run the numbers a second time. And then it would take the formula that worked best on Movie One and apply it to Movie Two, and tweak that until it had a formula that worked on Movies One and Two, and take that formula to Movie Three, and then to four and five, and on through all two hundred movies, whereupon it would go back through all the movies again, through hundreds of thousands of iterations, until it had worked out a formula that did the best possible job of predicting the financial success of every one of the movies in its database.

That formula, the theory goes, can then be applied to new scripts. If you were developing a $75-million buddy picture for Bruce Willis and Colin Farrell, Epagogix says, it can tell you, based on past experience, what that script’s particular combination of narrative elements can be expected to make at the box office. If the formula says it’s a $50-million script, you pull the plug. “We shoot turkeys,” Meaney said. He had seen Mr. Bootstraps and the neural network in action: “It can sometimes go on for hours. If you look at the computer, you see lots of flashing numbers in a gigantic grid. It’s like ‘The Matrix.’ There are a lot of computations. The guy is there, the whole time, looking at it. It eventually stops flashing, and it tells us what it thinks the American box-office will be. A number comes out.”

The way the neural network thinks is not that different from the way a Hollywood executive thinks: if you pitch a movie to a studio, the executive uses an ad-hoc algorithm—perfected through years of trial and error—to put a value on all the components in the story. Neural networks, though, can handle problems that have a great many variables, and they never play favorites—which means (at least in theory) that as long as you can give the neural network the same range of information that a human decision-maker has, it ought to come out ahead. That’s what the University of Arizona computer scientist Hsinchun Chen demonstrated ten years ago, when he built a neural network to predict winners at the dog track. Chen used the ten variables that greyhound experts told him they used in making their bets—like fastest time and winning percentage and results for the past seven races—and trained his system with the results of two hundred races. Then he went to the greyhound track in Tucson and challenged three dog-racing handicappers to a contest. Everyone picked winners in a hundred races, at a modest two dollars a bet. The experts lost $71.40, $61.20, and $70.20, respectively. Chen won $124.80. It wasn’t close, and one of the main reasons was the special interest the neural network showed in something called “race grade”: greyhounds are moved up and down through a number of divisions, according to their ability, and dogs have a big edge when they’ve just been bumped down a level and a big handicap when they’ve just been bumped up. “The experts know race grade exists, but they don’t weight it sufficiently,” Chen said. “They are all looking at win percentage, place percentage, or thinking about the dogs’ times.”

Copaken and Meaney figured that Hollywood’s experts also had biases and skipped over things that really mattered. If a neural network won at the track, why not Hollywood? “One of the most powerful aspects of what we do is the ruthless objectivity of our system,” Copaken said. “It doesn’t care about maintaining relationships with stars or agents or getting invited to someone’s party. It doesn’t care about climbing the corporate ladder. It has one master and one master only: how do you get to bigger box-office? Nobody else in Hollywood is like that.”

In the summer of 2003, Copaken approached Josh Berger, a senior executive at Warner Bros. in Europe. Meaney was opposed to the idea: in his mind, it was too early. “I just screamed at Dick,” he said. But Copaken was adamant. He had Mr. Bootstraps, Mr. Pink, and Mr. Brown run sixteen television pilots through the neural network, and try to predict the size of each show’s eventual audience. “I told Josh, ‘Stick this in a drawer, and I’ll come back at the end of the season and we can check to see how we did,’ ” Copaken said. In January of 2004, Copaken tabulated the results. In six cases, Epagogix guessed the number of American homes that would tune in to a show to within .06 per cent. In thirteen of the sixteen cases, its predictions were within two per cent. Berger was floored. “It was incredible,” he recalls. “It was like someone saying to you, ‘We’re going to show you how to count cards in Vegas.’ It had that sort of quality.”

Copaken then approached another Hollywood studio. He was given nine unreleased movies to analyze. Mr. Pink, Mr. Brown, and Mr. Bootstraps worked only from the script—without reference to the stars or the director or the marketing budget or the producer. On three of the films—two of which were low-budget—the Epagogix estimates were way off. On the remaining six—including two of the studio’s biggest-budget productions—they correctly identified whether the film would make or lose money. On one film, the studio thought it had a picture that would make a good deal more than $100 million. Epagogix said $49 million. The movie made less than $40 million. On another, a big-budget picture, the team’s estimate came within $1.2 million of the final gross. On a number of films, they were surprisingly close. “They were basically within a few million,” a senior executive at the studio said. “It was shocking. It was kind of weird.” Had the studio used Epagogix on those nine scripts before filming started, it could have saved tens of millions of dollars. “I was impressed by a couple of things,” another executive at the same studio said. “I was impressed by the things they thought mattered to a movie. They weren’t the things that we typically give credit to. They cared about the venue, and whether it was a love story, and very specific things about the plot that they were convinced determined the outcome more than anything else. It felt very objective. And they could care less about whether the lead was Tom Cruise or Tom Jones.”

The Epagogix team knocked on other doors that weren’t quite so welcoming. This was the problem with being a Kamesian. Your belief in a rule-bound universe was what gave you, an outsider, a claim to real expertise. But you were still an outsider. You were still Dick Copaken, the blue-blazered corporate lawyer who majored in bubbles as a little boy in Kansas City, and a couple of guys from the risk-management business, and three men called Pink, Brown, and Bootstraps—and none of you had ever made a movie in your life. And what were you saying? That stars didn’t matter, that the director didn’t matter, and that all that mattered was story—and, by the way, that you understood story the way the people on the inside, people who had spent a lifetime in the motion-picture business, didn’t. “They called, and they said they had a way of predicting box-office success or failure, which is everyone’s fantasy,” one former studio chief recalled. “I said to them, ‘I hope you’re right.’ ” The executive seemed to think of the Epagogix team as a small band of Martians who had somehow slipped their U.F.O. past security. “In reality, there are so many circumstances that can affect a movie’s success,” the executive went on. “Maybe the actor or actress has an external problem. Or this great actor, for whatever reason, just fails. You have to fire a director. Or September 11th or some other thing happens. There are many people who have come forward saying they have a way of predicting box-office success, but so far nobody has been able to do it. I think we know something. We just don’t know enough. I still believe in something called that magical thing—talent, the unexpected. The movie god has to shine on you.” You were either a Kamesian or you weren’t, and this person wasn’t: “My first reaction to those guys? Bullshit.”


A few months ago, Dick Copaken agreed to lift the cloud of unknowing surrounding Epagogix, at least in part. He laid down three conditions: the meeting was to be in London, Mr. Pink and Mr. Brown would continue to be known only as Mr. Pink and Mr. Brown, and no mention was to be made of the team’s current projects. After much discussion, an agreement was reached. Epagogix would analyze the 2005 movie “The Interpreter,” which was directed by Sydney Pollack and starred Sean Penn and Nicole Kidman. “The Interpreter” had a complicated history, having gone through countless revisions, and there was a feeling that it could have done much better at the box office. If ever there was an ideal case study for the alleged wizardry of Epagogix, this was it.

The first draft of the movie was written by Charles Randolph, a philosophy professor turned screenwriter. It opened in the fictional African country of Matobo. Two men in a Land Rover pull up to a soccer stadium. A group of children lead them to a room inside the building. On the ground is a row of corpses.

Cut to the United Nations, where we meet Silvia Broome, a young woman who works as an interpreter. She goes to the U.N. Security Service and relates a terrifying story. The previous night, while working late in the interpreter’s booth, she overheard two people plotting the assassination of Matobo’s murderous dictator, Edmund Zuwanie, who is coming to New York to address the General Assembly. She says that the plotters saw her, and that her life may be in danger. The officer assigned to her case, Tobin Keller, is skeptical, particularly when he learns that she, too, is from Matobo, and that her parents were killed in the country’s civil war. But after Broome suffers a series of threatening incidents Keller starts to believe her. His job is to protect Zuwanie, but he now feels moved to act as Broome’s bodyguard as well. A quiet, slightly ambiguous romantic attraction begins to develop between them. Zuwanie’s visit draws closer. Broome’s job is to be his interpreter. On the day of the speech, Broome ends up in the greenroom with Zuwanie. Keller suddenly realizes the truth: that she has made up the whole story as a way of bringing Zuwanie to justice. He rushes to the greenroom. Broome, it seems, has poisoned Zuwanie and is withholding the antidote unless he goes onstage and confesses to the murder of his countrymen. He does. Broome escapes. A doctor takes a look at the poison. It’s harmless. The doctor turns to the dictator, who has just been tricked into writing his own prison sentence: “You were never in danger, Mr. Zuwanie.”

Randolph says that the film he was thinking of while he was writing “The Interpreter” was Francis Ford Coppola’s classic “The Conversation.” He wanted to make a spare, stark movie about an isolated figure. “She’s a terrorist,” Randolph said of Silvia Broome. “She comes to this country to do a very specific task, and when that task is done she’s gone again. I wanted to write about this idea of a noble terrorist, who tried to achieve her ends with a character assassination, not a real assassination.” Randolph realized that most moviegoers—and most Hollywood executives—prefer characters who have psychological motivations. But he wasn’t trying to make “Die Hard.” “Look, I’m the son of a preacher,” he said. “I believe that ideology motivates people.”

In 2004, Sydney Pollack signed on to direct the project. He loved the idea of an interpreter at the United Nations and the conceit of an overheard conversation. But he wanted to make a commercial movie, and parts of the script didn’t feel right to him. He didn’t like the twist at the end, for instance. “I felt like I had been tricked, because in fact there was no threat,” Pollack said. “As much as I liked the original script, I felt like an audience would somehow, at the end, feel cheated.” Pollack also felt that audiences would want much more from Silvia Broome’s relationship with Tobin Keller. “I’ve never been able to do a movie without a love story in it,” he said. “For me, the heart of it is always the man and the woman and who they are and what they are going through.” Pollack brought Randolph back for rewrites. He then hired Scott Frank and Steven Zaillian, two of the most highly sought-after screenwriters in Hollywood—and after several months the story was turned inside out. Now Broome didn’t tell the story of overhearing that conversation. It actually happened. She wasn’t a terrorist anymore. She was a victim. She ”t an isolated figure. She was given a social life. She wasn’t manipulating Keller. Their relationship was more prominent. A series of new characters—political allies and opponents of Zuwanie’s—were added, as was a scene in Brooklyn where a bus explodes, almost killing Broome. “I remember when I came on ‘Minority Report,’ and started over,” said Frank, who wrote many of the new scenes for “The Interpreter.” “There weren’t many characters. When I finished, there were two mysteries and a hundred characters. I have diarrhea of the plot. This movie cried out for that. There are never enough suspects and red herrings.”

The lingering problem, though, was the ending. If Broome wasn’t after Zuwanie, who was? “We struggled,” Pollack said. “It was a long process, to the point where we almost gave up.” In the end, Zuwanie was made the engineer of the plot: he fakes the attempt on his life in order to justify his attacks on his enemies back home. Zuwanie hires a man to shoot him, and then another of Zuwanie’s men shoots the assassin before he can do the job—and in the chaos Broome ends up with a gun in her hand, training it on Zuwanie. “The end was the hardest part,” Frank said. “All these balls were in the air. But I couldn’t find a satisfying way to resolve it. We had to put a gun in the hand of a pacifist. I couldn’t quite sew it up in the right way. Sydney kept saying, ‘You’re so close.’ But I kept saying, ‘Yeah, but I don’t believe what I’m writing.’ I wonder if I did a disservice to ‘The Interpreter.’ I don’t know that I made it better. I may have just made it different.”

This, then, was the question for Epagogix: If Pollack’s goal was to make “The Interpreter” a more commercial movie, how well did he succeed? And could he have done better?


The debriefing took place in central London, behind the glass walls of the private dining room of a Mayfair restaurant. The waiters came in waves, murmuring their announcements of the latest arrival from the kitchen. The table was round. Copaken, dapper as always in his navy blazer, sat next to Sean Verity, followed by Meaney, Mr. Brown, and Mr. Pink. Mr. Brown was very tall, and seemed to have a northern English accent. Mr. Pink was slender and graying, and had an air of authority about him. His academic training was in biochemistry. He said he thought that, in the highly emotional business of Hollywood, having a scientific background was quite useful. There was no sign of Mr. Bootstraps.

Mr. Pink began by explaining the origins of their system. “There were certain historical events that allowed us to go back and test how appealing one film was against another,” he said. “The very simple one is that in the English market, in the sixties on Sunday night, religious programming aired on the major networks. Nobody watched it. And, as soon as that finished, movies came on. There were no lead-ins, and only two competing channels. Plus, across the country you had a situation where the commercial sector was playing a whole variety of movies against the standard, the BBC. It might be a John Wayne movie in Yorkshire, and a musical in Somerset, and the BBC would be the same movie everywhere. So you had a control. It was very pure and very simple. That was a unique opportunity to try and make some guesstimates as to why movies were doing what they were doing.”

Brown nodded. “We built a body of evidence until we had something systematic,” he said.

Pink estimated that they had analyzed thousands of movies. “The thing is that not everything comes to you as a script. For a long period, we worked for a broadcaster who used to send us a couple of paragraphs. We made our predictions based on that much. Having the script is actually too much information sometimes. You’re trying to replicate what the audience is doing. They’re trying to make a choice between three movies, and all they have at that point is whatever they’ve seen in TV Guide or on any trailer they’ve seen. We have to take a piece here and a piece here. Take a couple of reference points. When I look at a story, there are certain things I’m looking for—certain themes, and characters you immediately focus on.” He thought for a moment. “That’s not to deny that it matters whether the lead character wears a hat,” he added, in a way that suggested he and Mr. Brown had actually thought long and hard about leads and hats.

“There’s always a pattern,” he went on. “There are certain stories that come back, time and time again, and that always work. You know, whenever we go into a market—and we work in fifty markets—the initial thing people say is ‘What do you know about our market?’ The assumption is that, say, Japan is different from us—that there has to be something else going on there. But, basically, they’re just like us. It’s the consistency of these reappearing things that I find amazing.”

“Biblical stories are a classic case,” Mr. Brown put in. “There is something about what they’re telling and the message that’s coming out that seems to be so universal. With Mel Gibson’s ‘The Passion,’ people always say, ‘Who could have predicted that?’ And the answer is, we could have.”

They had looked at “The Interpreter” scripts a few weeks earlier. The process typically takes them a day. They read, they graded, and then they compared notes, because Mr. Pink was the sort who went for “Yojimbo” and Mr. Brown’s favorite movie was “Alien” (the first one), so they didn’t always agree. Mr. Brown couldn’t remember a single script he’d read where he thought there wasn’t room for improvement, and Mr. Pink, when asked the same question, could come up with just one: “Lethal Weapon.” “A friend of mine gave me the shooting script before it came out, and I remember reading it and thinking, It’s all there. It was all on the page.” Once Mr. Pink and Mr. Brown had scored “The Interpreter,” they gave their analyses to Mr. Bootstraps, who did fifteen runs through the neural network: the original Randolph script, the shooting script, and certain variants of the plot that Epagogix devised. Mr. Bootstraps then passed his results to Copaken, who wrote them up. The Epagogix reports are always written by Copaken, and they are models of lawyerly thoroughness. This one ran to thirty-eight pages. He had finished the final draft the night before, very late. He looked fresh as a daisy.

Mr. Pink started with the original script. “My pure reaction? I found it very difficult to read. I got confused. I had to reread bits. We do this a lot. If a project takes more than an hour to read, then there’s something going on that I’m not terribly keen on.”

“It didn’t feel to me like a mass-appeal movie,” Mr. Brown added. “It seemed more niche.”

When Mr. Bootstraps ran Randolph’s original draft through the neural network, the computer called it a $33-million movie—an “intelligent” thriller, in the same commercial range as “The Constant Gardener” or “Out of Sight.” According to the formula, the final shooting script was a $69-million picture (an estimate that came within $4 million of the actual box-office). Mr. Brown wasn’t surprised. The shooting script, he said, “felt more like an American movie, where the first one seemed European in style.”

Everyone agreed, though, that Pollack could have done much better. There was, first of all, the matter of the United Nations. “They had a unique opportunity to get inside the building,” Mr. Pink said. “But I came away thinking that it could have been set in any boxy office tower in Manhattan. An opportunity was missed. That’s when we get irritated—when there are opportunities that could very easily be turned into something that would actually have had an impact.”

“Locale is an extra character,” Mr. Brown said. “But in this case it’s a very bland character that didn’t really help.”

In the Epagogix secret formula, it seemed, locale matters a great deal. “You know, there’s a big difference between city and countryside,” Mr. Pink said. “It can have a huge effect on a movie’s ability to draw in viewers. And writers just do not take advantage of it. We have a certain set of values that we attach to certain places.”

Mr. Pink and Mr. Brown ticked off the movies and television shows that they thought understood the importance of locale: “Crimson Tide,” “Lawrence of Arabia,” “Lost,” “Survivor,” “Castaway,” “Deliverance.” Mr. Pink said, “The desert island is something that we have always recognized as a pungent backdrop, but it’s not used that often. In the same way, prisons can be a powerful environment, because they are so well defined.” The U.N. could have been like that, but it wasn’t. Then there was the problem of starting, as both scripts did, in Africa—and not just Africa but a fictional country in Africa. The whole team found that crazy. “Audiences are pretty parochial, by and large,” Mr. Pink said. “If you start off by telling them, ‘We’re going to begin this movie in Africa,’ you’re going to lose them. They’ve bought their tickets. But when they come out they’re going to say, ‘It was all right. But it was Africa.’ ” The whole thing seemed to leave Mr. Pink quite distressed. He looked at Mr. Brown beseechingly.

Mr. Brown changed the subject. “It’s amazing how often quite little things, quite small aspects, can spoil everything,” he said. “I remember seeing the trailer for ‘V for Vendetta’ and deciding against it right there, for one very simple reason: there was a ridiculous mask on the main character. If you can’t see the face of the character, you can’t tell what that person is thinking. You can’t tell who they are. With ‘Spider-Man’ and ‘Superman,’ though, you do see the face, so you respond to them.”

The team once gave a studio a script analysis in which almost everything they suggested was, in Hollywood terms, small. They wanted the lead to jump off the page a little more. They wanted the lead to have a young sidekick—a relatively minor character—to connect with a younger demographic, and they wanted the city where the film was set to be much more of a presence. The neural network put the potential value of better characterization at an extra $2.46 million in U.S. box-office revenue; the value of locale adjustment at $4.92 million; the value of a sidekick at $12.3 million—and the value of all three together (given the resulting synergies) at $24.6 million. That’s another $25 million for a few weeks of rewrites and maybe a day or two of extra filming. Mr. Bootstraps, incidentally, ran the numbers and concluded that the script would make $47 million if the suggested changes were not made. The changes were not made. The movie made $50 million.

Mr. Pink and Mr. Brown went on to discuss the second “Interpreter” screenplay, the shooting script. They thought the ending was implausible. Charles Randolph had originally suggested that the Tobin Keller character be black, not white, in order to create the frisson of bringing together a white African and a black American. Mr. Pink and Mr. Brown independently came to the same conclusion. Apparently, the neural network ran the numbers on movies that paired black and white leads—”Lethal Weapon,” “The Crying Game,” “Independence Day,” “Men in Black,” “Die Another Day,” “The Pelican Brief”—and found that the black-white combination could increase box-office revenue. The computer did the same kind of analysis on Scott Frank’s “diarrhea of the plot,” and found that there were too many villains. And if Silvia Broome was going to be in danger, Mr. Bootstraps made clear, she really had to be in danger.

“Our feeling—and Dick, you may have to jump in here—is that the notion of a woman in peril is a very powerful narrative element,” Mr. Pink said. He glanced apprehensively at Copaken, evidently concerned that what he was about to say might fall in the sensitive category of the proprietary. “How powerful?” He chose his words carefully. “Well above average. And the problem is that we lack a sense of how much danger she is in, so an opportunity is missed. There were times when you were thinking, Is this something she has created herself? Is someone actually after her? You are confused. There is an element of doubt, and that ambiguity makes it possible to doubt the danger of the situation.” Of course, all that ambiguity was there because in the Randolph script she was making it all up, and we were supposed to doubt the danger of the situation. But Mr. Pink and Mr. Brown believed that, once you decided you weren’t going to make a European-style niche movie, you had to abandon ambiguity altogether.

“You’ve got to make the peril real,” Mr. Pink said.

The Epagogix revise of “The Interpreter” starts with an upbeat Silvia Broome walking into the United Nations, flirting with the security guard. The two men plotting the assassination later see her and chase her through the labyrinthine cor-ridors of what could only be the U.N. building. The ambiguous threats to Broome’s life are now explicit. At one point in the Epagogix version, a villain pushes Broome’s Vespa off one of Manhattan’s iconic East River bridges. She hangs on to her motorbike for dear life, as it swings precariously over the edge of the parapet. Tobin Keller, in a police helicopter, swoops into view: “As she clings to Tobin’s muscular body while the two of them are hoisted up into the hovering helicopter, we sense that she is feeling more than relief.” In the Epagogix ending, Broome stabs one of Zuwanie’s security men with a knife. Zuwanie storms off the stage, holds a press conference, and is shot dead by a friend of Broome’s brother. Broome cradles the dying man in her arms. He ” dies peacefully,” with ” a smile on his blood-spattered face.” Then she gets appointed Matobo’s U.N. ambassador. She turns to Keller. “‘This time,’ she notes with a wry smile . . . ‘you will have to protect me.’ ” Bootstraps’s verdict was that this version would result in a U.S. box-office of $111 million.

“It’s funny,” Mr. Pink said. “This past weekend, ‘The Bodyguard’ was on TV. Remember that piece of”—he winced—”entertainment? Which is about a bodyguard and a woman. The final scene is that they are right back together. It is very clearly and deliberately sown. That is the commercial way, if you want more bodies in the seats.”

“You have to either consummate it or allow for the possibility of that,” Copaken agreed.

They were thinking now of what would happen if they abandoned all fealty to the original, and simply pushed the movie’s premise as far as they could possibly go.

Mr. Pink went on, “If Dick had said, ‘You can take this project wherever you want,’ we probably would have ended up with something a lot closer to ‘The Bodyguard’—where you have a much more romantic film, a much more powerful focus to the two characters—without all the political stuff going on in the background. You go for the emotions on a very basic level. What would be the upper limit on that? You know, the upper limit of anything these days is probably still ‘Titanic.’ I’m not saying we could do six hundred million dollars. But it could be two hundred million.”


It was clear that the whole conversation was beginning to make Mr. Pink uncomfortable. He didn’t like “The Bodyguard.” Even the title made him wince. He was the sort who liked “Yojimbo,” after all. The question went around the room: What would you do with “The Interpreter”? Sean Verity wanted to juice up the action-adventure elements and push it to the $150- to $160-million range. Meaney wanted to do without expensive stars: he didn’t think they were worth the money. Copaken wanted more violence, and he also favored making Keller black. But he didn’t want to go all the way to “The Bodyguard,” either. This was a man who loved “Dear Frankie” as much as any film he’d seen in recent memory, and “Dear Frankie” had a domestic box-office gross of $1.3 million. If you followed the rules of Epagogix, there wouldn’t be any movies like “Dear Frankie.” The neural network had one master, the market, and answered one question: how do you get to bigger box-office? But once a movie had made you vulnerable—once you couldn’t even retell the damn story without getting emotional—you couldn’t be content with just one master anymore.

That was the thing about the formula: it didn’t make the task of filmmaking easier. It made it harder. So long as nobody knows anything, you’ve got license to do whatever you want. You can start a movie in Africa. You can have male and female leads not go off together—all in the name of making something new. Once you came to think that you knew something, though, you had to decide just how much money you were willing to risk for your vision. Did the Epagogix team know what the answer to that question was? Of course not. That question required imagination, and they weren’t in the imagination business. They were technicians with tools: computer programs and analytical systems and proprietary software that calculated mathematical relationships among a laundry list of structural variables. At Platinum Blue, Mike McCready could tell you that the bass line was pushing your song out of the center of hit cluster 31. But he couldn’t tell you exactly how to fix the bass line, and he couldn’t guarantee that the redone version would still sound like a hit, and you didn’t see him releasing his own album of computer-validated pop music. A Kamesian had only to read Lord Kames to appreciate the distinction. The most arrogant man in the world was a terrible writer: clunky, dense, prolix. He knew the rules of art. But that didn’t make him an artist.

Mr. Brown spoke last. “I don’t think it needs to be a big-budget picture,” he said. “I think we do what we can with the original script to make it a strong story, with an ending that is memorable, and then do a slow release. A low-budget picture. One that builds through word of mouth—something like that.” He was confident that he had the means to turn a $69-million script into a $111-million movie, and then again into a $150- to $200-million blockbuster. But it had been a long afternoon, and part of him had a stubborn attachment to “The Interpreter” in something like its original form. Mr. Bootstraps might have disagreed. But Mr. Bootstraps was nowhere to be seen.