The Candy House(24)

I returned to Chicago nine weeks after leaving. On the ride from O’Hare to my apartment, I gazed at the sparkling buildings downtown and felt my failure and exile slide back around me. Nothing had changed. The act of entering my studio was forensic, like visiting a crime scene. The stale air had a faint toxic sweetness. I was afraid that if I went to sleep there, I would never wake up. So I stayed awake, organizing my possessions overnight and arranging for them to be boxed and shipped to Drew and Sasha’s house. I left keys and instructions and money in an envelope for the super, and hoped for the best.

Dawn had barely broken when I got back on the Blue Line to the airport. As I watched the illumination of the Chicago sky, I was dreaming of the desert. I wanted to fill its emptiness with a different story than the one I’d lived so far. Like Sasha had.

I rented a room in town and began studying for the California bar, feeling a rumbling return of my old appetite for arguments and statutes. Within a few months, I was doing legal work for some of Drew’s indigent patients. At the end of my first year, I was elected mayor of our San Bernardino town. If these victories seem improbable, I invite you to recall the narrative power of redemption stories. America loves a sinner, lucky for me.

Drew and I never talk about our odd history, but I think my “success,” and any good I’ve managed to do, have brought him gladness. Sasha tells me, during her long hugs that I’ve come to depend on, that I’ve helped him to relax. After our twice-weekly tennis games, Drew usually stops and has a beer with me rather than bolting straight back to his clinic. When the balloons are out, as they often are, we raise our glasses at the sky before we drink.





Rhyme Scheme


M has four primary freckles on her nose and approximately twenty-four secondary freckles. I say “approximately” not because her secondary freckles can’t be counted—few things in this world can’t be counted—but because I can’t stare at M’s nose for long enough to count her secondary freckles without making her uncomfortable. Her hair is thicker than the hair of 40 percent of the women who work among us and longer than 57 percent, and she wears hair bands 24 percent of the time, scrunchies 28 percent of the time, and her hair loose 48 percent of the time. She is exactly one week older than I am—25.56 to my 25.54—a fact I learned from the icebreaker our team leader, O’Brien, conducted during a taco party he hosted at his house for our whole team when we first became a team. Each of us gave the date, time, and place of our birth, and O’Brien plotted our data on a dynamic 3D Earth model and slowly rotated it so that we could see all forty-three team members ping into existence over the course of our aggregate age span. In the model, M and I seemed to come to life at the same instant.

I’ve crowdsourced M’s prettiness casually among members of our team’s larger unit under the pretense of trying to decide, as a single heterosexual male, whether or not she is pretty, but in actuality to gauge the breadth and strength of my competition. Of the 81 percent who found M pretty, 64 percent are not competitive, being males or nonbinaries attached to or interested in other people, or else females—of whom the 15 percent who identify as gay or bi are not a threat because M is “straight.” Obviously, I recognize the existence of a spectrum of desire between straight and gay, but placing M on this spectrum would require either an honest reporting of her sexual history, which I am in no position to acquire, or gray grabs of M’s sexual memories and fantasies from the collective—an act of such grotesque personal violation that she would justifiably revile me afterward, thus defeating the point.

Of the remaining 36 percent male or nonbinary respondents who might conceivably compete with me in pursuing a relationship with M, fully half possess at least one possibly-to-likely-disqualifying personal trait: 14 percent = noticeable body odor or other personal hygiene violations (nose picking, ear drilling, etc.); 11 percent = online warlordry; 9 percent = old (over thirty-five); 7 percent = radically self-obsessed; 6 percent = obsessed with Bix Bouton; 3 percent = prone to miscellaneous offenses, including engaging in Iraq War reenactments, telling sexist jokes, smoking cigarettes, or wearing bandanas. Okay, that last one is a pet peeve of mine but probably not M’s. I hate bandanas.

Now to the remaining 18 percent of poll respondents who represent possible competing contenders for M’s affection. And here is where the data begin to fail, because how can I calculate whose chances are best? The key to M’s heart may lie in something quirky and impossible to predict without intimate knowledge of her background and memories and psychological state—which, again, I could acquire only invasively. Maybe the person who brings M a blue stuffed hippo will be the one she falls in love with, and which of us will do it? I will do it. I see a blue stuffed hippo at Walmart and think, Maybe this is x: the unknown value required to secure M’s love. And then I have the same thought about a small ballerina music box. And then I have the same thought about some really long tulips that are actually made of silk. And then about a packet of rubber bands all different colors, and then about some things I pick up off the ground and even from the trash, always with the thought, Any one of these may be x—that ineffable, unpredictable detail that makes one person fall in love with another person.

Now, given that I am a counter—or, to put it professionally, a senior empiricist and metrics expert—it is reasonable to ask whether, by taking enough random cracks at assigning a value to x, I will statistically improve my chances of making M fall in love with me. The answer is “yes and no.” Yes, because perfect bone marrow matches can be found between total strangers by sorting through enough random donor data. No, because I would have to devote the rest of my life (assuming an average American male life span) plus eighty-five more years solely to the task of acquiring random objects before I would increase my statistical likelihood of finding the “right object,” at which point M and I would both be dead. And all of this assumes that x is an object, which it may not be!