This Is How You Die: Stories of the Inscrutable, Infallible, Inescapable Machine of Death

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This Is How You Die: Stories of the Inscrutable, Infallible, Inescapable Machine of Death Page 14

by Неизвестный


  “Well, that would be annoying. That could be a short- or long-term thing. I guess I’d move to the desert, try to make it a long-term thing.”

  “Move to the desert—that’s what everyone says. But would your situation actually improve? Given that the machine is never wrong?”

  “You’d have me do nothing? That’s certainly one way to make the request pointless.”

  “But you’re no more or less likely to drown if you live in the desert than if you live on a houseboat. It’s certain either way.”

  “Yeah,” Mike said, sounding tired. “I know. I wouldn’t be trying to avoid the inevitable. I’d be trying to nail down the time frame.”

  “Suppose you move to the desert. What would your life be like?”

  “About like it is now. Much drier.”

  “Would you take baths? Or showers?”

  “Oh. Right.”

  “Every time you step into the shower, you’ll be gripped with dread. Is this the time I slip and fall, unconscious, with my face over the drain?”

  Mike deflated. “Right,” he said.

  “Every time it rains: fear. Every time you walk past a community pool: is this the time some prankster or an impossible gust of wind tosses me in? Every time you see an unstopped fire hydrant. Every time your neighbor fills his shitty inflatable wading pool. Every time you pop the top on a beer or develop a wet cough: fear.”

  Mike avoided Eliot’s eyes, and Eliot felt a little thrill of success. They were silent for a few moments; then Mike said, “I think you’re right.”

  “Recently you’ve been struggling with the unknown. It’s a potent fear, I know—believe me, I know—and it can be paralyzing. But your paralysis is temporary. It’s a phase that will pass. If you get the machine’s report, it’ll be like you’re living a horror movie from now until you finally die.”

  “Yargh,” Mike said.

  Eliot sat at their table by the window, looking across the street at their office building, smaller than some of the structures in Washington, D.C., but still, it seemed to him, grimmer. Rosemary approached carrying a cafeteria tray. “Usuals all around,” she said.

  “Thanks so much for lunch,” he said.

  “Please.” Rosemary slid into her chair, across from him. “After a bailout like this morning’s?”

  “It was fun, actually. Nice kid. You had his number from the beginning. I used drowning on him and he left in blissful ignorance.”

  “Shame I was late. The job is satisfying when it goes well.” Rosemary lifted her spoon to her lips and slurped—a ridiculous, throbbing slurp. Eliot smiled behind his napkin. She’d caught him smiling at her over a bowl of soup once before and asked what was funny. He’d made up excuses. “It’s soothing when they seem convinced, isn’t it?” she said.

  “Satisfying, yes. Soothing?”

  “You really believe the arguments we deploy against these poor befuddled people? All the time, you believe them?”

  Eliot chewed a mouthful of sandwich until it was liquid, then took a sip of coffee. “You don’t?” he said.

  “Not all the time, no. I mean, it’s not like I think they’re crap, the arguments. But death is scary. It doesn’t really matter, sometimes, that I know about a thousand case studies from the Oedipus years, or that I know every single client I’ve failed to dissuade has gone on to suffer. Reasons are more convincing when other people believe them. You know that as well as I do.” Rosemary leaned across the table, reaching for a packet of sugar. Eliot looked at the wisps of hair, fallen from their clip, the interplay of light and shadow on her neck and collarbone.

  “Lydia,” he said. “Lydia is my other people, I guess.”

  “God,” Rosemary said. “I guess she’d have to be.”

  “Yeah. I waver for minutes here and there. In particularly bad moments of particularly bad days, I just want to know and have it over with. Those moments pass. But Lydia, she’s up to her neck in the raw data. She could call up her death slip with one line of SQL. I think she honest to God has no desire to know how she’s going to die.”

  “It must be comforting to have that sort of certainty.”

  “It was.”

  “Was?”

  Eliot, thrown, stammered and fumbled with a napkin. “You mean for her,” he said. “Yes, she’s secure in her certainty. I know she is. More than secure. ‘Comfortable’ is probably the right word.”

  Rosemary was smiling, mischievous, looking younger, even, than she was. “It was?” she said.

  Back in his office, Eliot phoned Lydia’s desk. She didn’t answer.

  Over the years Eliot’s office had aged and contracted. He wedged books into every available length of shelf and then laid more across the tops of the others. The room was filled with him—the great wooden desk, its varnish rubbed away under his elbows, the scattered offprints sent to him by grad school colleagues who were publishing at a much brisker pace, the framed handbills for rock shows that were now dingy and dated and, worst of all, out of place—and it depressed him.

  Beneath the surface of his guilt was a current of anger that Eliot tried to tease to the surface. Why should he feel bad for admiring Rosemary’s hair? Why should he feel bad for admitting—especially as obliquely and accidentally as he had—that marriage to Lydia was no longer comfortable?

  Eliot slid open the deep drawer on the lower right of his desk, thinking he might have a calming drink of his celebration bourbon. Inside the drawer, a series of five chrome spheres were suspended via fishing line from a wooden frame. Newton’s cradle, the toy was called, a fixture on the desk of every Hollywood headshrinker ever committed to film. Lydia had given it to him as a gift when he was hired by the feds. He’d been delighted at the visual gag—the stodgy old Newton’s balls on the desk of the hip young psychologist. It was hilarious.

  As the years passed, he grew worried that people weren’t getting the joke. He moved the toy to a shelf behind him, where it wasn’t as prominent, and later to a shelf behind his clients, where they would see it only if they were looking. Finally, after they hired Rosemary, but before he showed her his office, he hid it in a drawer, where it straddled his bottle of bourbon.

  Lydia, Lydia. It was a hell of a gift.

  He picked up the phone and dialed her desk once more. Once more, she didn’t answer. It wasn’t like her.

  In the first days of their marriage, Lydia hadn’t yet started her job in Aggregate Statistics. She was finally approved—security clearances, psychological batteries, proficiency tests in pattern recognition and statistical analysis, and on and on and on—just before their first anniversary. It seemed to him, at the time, a symbol of their fitness for each other. He defended her from distraction while she mucked about in the numbers. She worked with the demographic and death-report data that was collected from all infants at birth, looking for early indications of impending public health disasters. He worked to dissuade adults from filing Death Machine requests, from learning what the government already knew, and triggering the tragedy and chaos that usually followed. It was like a Western: he at the window of the bank with a revolver, fending off the hordes, she inside with a stethoscope on the vault. Like a Western, but more bureaucratic.

  It wasn’t difficult to identify the period when they began to decline as a couple. It was during Lydia’s fifteen minutes. The whole bird fever thing, five years before. In fact, it wasn’t difficult to point to the exact moment their deterioration began: he’d been standing over the stove, stirring a red sauce, when she came home from work and said, “At last, some success.”

  Eliot put down the wooden spoon and gave her a hug. “Your mathly hoodoo is getting some results?” he said.

  “Yeah. We’re pretty sure my spider found an outbreak of bird fever in Boise, something like fifteen or twenty years from now. Found it hundreds, maybe thousands of cases before the normal trip wires would have flagged it.”

  “That’s fantastic,” he said. “How sure is pretty sure?”

  “Sure enou
gh that the director is committing resources. We’ve started the bidding process to synthesize a shotgun vaccine, and as soon as it’s ready the Boise schools will start requiring it.”

  “That’s huge,” he said, turning back to his pot of tomatoes. “That’s money.”

  “I know it. We stand to save a lot of future lives.”

  “When will you know?”

  “I expect new death slips with ‘bird fever’ to start tapering off right away,” Lydia said. Eliot could feel the happiness radiating from her but couldn’t bring himself to meet her eyes. “Like, today,” she said. “If I’ve got this right, we should be down to the odd instance within a few months. We’ll do the official review in a year, but with the number of babies born every day in and around Boise, we should have a pretty good idea within the month.”

  Eliot didn’t feel right. He stripped oregano leaves from a stem into the pot and tried to locate within himself a feeling of happiness or pride. With the wooden spoon, he forced the tiny leaves beneath the simmering surface of the sauce.

  “Eliot?” Lydia said.

  He stepped back from the stove and looked at her. “That’s huge,” he said. “They have a lot of confidence in you.”

  Over the next few months, it got worse. Lydia’s new filters were a breakthrough in identifying future outbreaks, saving unknown numbers of the not yet conceived from deaths due to disease. Her techniques were adopted across the department and she attained as much celebrity as is available to a midlevel number cruncher in a beige government cubicle.

  During that period, each of Lydia’s successes made Eliot seize up inside. She received invitations to give talks at conferences frequently enough that he couldn’t take the time off to accompany her. He went to work every day and did the same things he’d done for years, feeling less like a partner and more like a nuisance, always hovering two steps behind. Her work was published in academic journals and she was profiled in a popular science magazine. He stopped reading the Death Machine trades so as to avoid seeing her name.

  When he arrived at home that evening, the day of Mike Cohen and his lunch with Rosemary, Lydia’s car was already in the driveway. Eliot peered through the windows as he slipped between the cars, holding his briefcase high, looking for any hint of why she was home early. He saw none.

  The house was quiet. He called for her and stuck his head in the living room, the study, the dining room. From the kitchen he spotted her in their backyard garden. She stood there wearing heavy canvas gloves, holding a cultivating fork, vibrating. She took half a step toward a planter on her left, turned toward the bed on her right, paused for a moment, then cast about again. Eliot watched her through the window as he set down his briefcase and draped his blazer over the back of a chair. Her convulsive indecision was almost funny—like a kid at Christmas who can’t get as far as playing with any toy thanks to the distracting temptation of the others. Eliot tried to chuckle, but it came out as a grunt. Could Lydia have been so excited to cultivate the garden that she left work early? It wasn’t possible.

  Eliot pushed through the screen door and down the steps into the backyard. Without the barrier of the window to provide distance, the silence in the yard was eerie. She was radiating panic. “Lydia?” he said.

  Lydia froze and looked at him. Her face was strained and her eyes wouldn’t settle. She threw the cultivating fork aside and made for the house, dropping her shoulder to dodge around him. “Lydia!” he said and put out his arm to catch her around the waist. “What is it?” She folded against him. He spread his hands across her back and felt her shudder against his chest. Never had he seen her this upset. The list of potential causes didn’t seem long. Did something happen to her sister? Had she been fired? Was she having an affair? Eliot leaned away from her enough to look at her face. She wiped thin cords of snot from her lip with the back of her canvas gloves. “What is it, Lydia?” he said. “What happened?”

  “The car’s in the driveway,” she said into his shoulder. “I stranded pasty-faced Gary at work.”

  A quarter of an hour later, Lydia sat across the kitchen table from Eliot, holding a cup of tea. He sat in silence, fascinated by the redness of her eyes. Somehow, hunched over the kitchen table, in the grip of this pitched misery, she put him in mind of the laughing, driven woman he had courted more than a decade before. He waited for her to work her way through a few sips of tea before he tried again. “Lydia?” he said. “What happened?”

  She glanced at him and configured her face in a smile. “There’s going to be a conflagration,” she said. “A conflagration, of all things. In New York.”

  It took Eliot nearly an hour to piece together the story Lydia told him in fits and starts, digressions and metaphors. Partway through, he put on another pot of tea and sat down beside her at the table, their upper arms and shoulders touching.

  Lydia’s Boise algorithm—the one that launched her career—was easy to understand in the abstract. Infectious disease tends to kill, disproportionately, the very young and the very old. If the Department of Aggregate Statistics catches the slips of the cohort who will, eventually, die when they are very old, the people who will be very young at the time of the outbreak haven’t yet been conceived. They haven’t been born, so they haven’t been tested, so their slips haven’t been printed. Though the deaths of the very old are written and cannot be changed, there’s hope that a concerted public health effort, like a vaccination campaign, might prevent the disease from ravaging the future cohort of the very young. Thus it was that, thanks to her work, no future infants would die of the bird fever in Boise.

  Lydia had spent the last year developing a successor to the Boise algorithm, adapting it for applications other than infectious disease, lowering its threshold sensitivity. The challenge was this: while epidemics of disease tend to cluster geographically and demographically, large-scale death due to natural disaster clusters only geographically. These die-offs are demographically scattered, killing healthy adults just as well as infants and the elderly. Without demographic clusters, identifying trends in the raw data of the death slips is much harder to do. Nevertheless, Lydia had some luck catching natural disasters in test data and proceeded to take a stab at real-world data. Her first hit in the real world was the word “conflagration” in New York City. Four hundred death slips going back nearly sixty years came from blood tests of babies in New York.

  “Conflagration” was an unusually obscure word choice for the Death Machine. In the overwhelming majority of cases in which the cause of death was fire, the machine reported “fire.” So Lydia searched New York’s death slips going back sixty years and found 380 hits for “fire.” The machine’s bizarre behavior put her in a paranoid mood. She searched for “inferno” and found 370 hits, just under the threshold sensitivity of her new algorithm, though far below the threshold of the production standard. A search for “broiling” netted 300 hits. She loaded a thesaurus and worked her way through nouns and verbs related to fire. Immolation: 380 hits. Combustion: 350 hits. Cremation, incineration: 340 hits each. Searing, scorching, charring, roasting: each between 250 and 400 hits.

  When she totaled her list, she had thousands of death slips from the city of New York indicating death by fire—far too many to overlook, had they not been cloaked by synonyms. She turned to types of death that often accompany traumatic disasters. Smothering: 350 hits. Asphyxiation, suffocation, hypoxia. Same story. Blunt trauma and bludgeoning. Blood loss and—absurdly, it seemed to Lydia—“exsanguination.” Tens of thousands of death slips, taken altogether, suggested a massive disaster coming to New York.

  “What could do this?” Eliot said. “A terrorist attack? A nuke? It sounds like you’re talking about the total destruction of New York City.”

  “It’s worse than that,” Lydia said. “You still don’t get it.”

  She searched for “conflagration” in Seattle: 240 hits. In Albuquerque: 180 hits. Seven hundred fifty hits each in Chicago, Houston, Miami, and D.C.

  “You�
��ve detected the end of the world?” Eliot said. “You and your computer predicted Armageddon?”

  “Armageddon?” Lydia said, and smiled at her teacup. “I could probably get a hundred and fifty hits for it in every American city.”

  Eliot satisfied the suddenly overwhelming urge to do something with his hands by carrying their teacups to the sink. He could think of nothing to say.

  “There are ways,” Lydia said, “to predict, roughly, when a disaster is going to fall. We can look at the numbers of babies born with a given death slip, and how those numbers change over time. In this case, since it looks like nearly everyone is going to die, it’s a simple linear projection and very precise. We just extrapolate to the time when every baby born gets a disaster-related death slip. It’s twenty-nine years and three months away.”

  He laughed and was startled by the volume of his own voice. “Twenty-nine years?” he said.

  “Eliot,” Lydia said. “We’re barely forty. We’re probably going to live to the end of the world.”

  The next morning, when the clock radio struggled to life—an NPR arts and culture story on a band he didn’t recognize—Eliot and Lydia lay there, listening. When the story finished, Lydia turned to face him. “I don’t think I’m going to work today,” she said.

  Eliot reached over the edge of the bed, stretching for the nightstand, and turned off the radio. “I’m thinking the same,” he said.

  He lay beside her in silence for a time. The thin curtains swelled and glowed. The cherry trees in Potomac Park had dropped their blossoms not so long before, blanketing the banks of the greasy river with petals. They’d do it again the next spring, and again, and twenty-six times after that.

  “Is this going to change?” Lydia said. “After I’ve had a few weeks to contemplate the end of the world, will I want to go back to fine-tuning pattern-matching algorithms in a government cubicle? Ought I?”

 

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