Free The King of Fear by Drew Chapman

“Native language. Education.”
    â€œYou know why I think you’re doing this? Because you’re still in love with her. She burned your ass, and you’re going to spend the rest of your life trying to prove to her that you’re worthy.”
    â€œWait, I’m confused. Earlier, you said she’s angry at me because I screwed her. Now you’re saying I’m obsessed with her because she screwed me. Which is it?”
    â€œHaven’t decided yet. Maybe both. I’ll know better after I eat.”
    Garrett ignored Mitty, and she switched the channel to a house-hunting reality show. Mitty loved reality shows. They could be about anything, she didn’t care: redecorating, building motorcycles, clearing out storage lockers. She couldn’t get enough of them, and if that defined her as a mouth-breathing dumbass, well, so be it. She ate her sandwich as she watched, listening as Garrett’s fingers click-clacked on the keyboard next to her. After a few minutes her curiosity got the better of her. She rolled over on the bed to sneak a peek at what he was doing.
    â€œOnce you’ve got the questions, how you gonna define them?” she asked, despite her better judgment.
    Garrett smiled. “Like I said, evidential probability. Work our way backwards to an optimal personality. What would be the prime characteristic of a person coming into the country to do economic hacking? Would they be twenty years old? Twenty-five? Thirty? Forty? Assign a value to each age. Ninety percent, eighty percent, et cetera.”
    â€œYeah, but how do we assign values? Our percentage assignments areguesses.” She’d done just as much programming in her life as Garrett had, maybe more. She might not have had the statistical background that he did, but she was a quick study and loved numbers almost as much as Garrett did. That was part of why they remained friends. That, and beer. And video games.
    â€œWe do as much research as we can, right now, online. What’s the average age of arrested hackers? In the US? Abroad. We can find those numbers. Anything we don’t know, you and I discuss, and then guess. Guesses are assigned a lower value than researched answers. Bayesian statistical analysis.”
    Mitty lay back on the bed, staring up at the ceiling. “And the discussion between us—how are we gonna resolve disputes?”
    â€œOccam’s razor. Simplest answer wins. If there is no obvious simplest answer, then split the results into two questions and give them equal weight in the profile.”
    Mitty thought about this. She’d gone to Fordham University on a free ride, the first person in her family to graduate from college. She’d studied social work when she first got there, figuring she would go back to her old neighborhood in the South Bronx to help immigrant families looking for their version of the American dream, but then she took her first computer-programming class and never considered social work again. Coding was like writing a story, only you were doing it in this language that was both made-up and yet made perfect sense. And the coolest thing about it was that when the story was finished, it came out the other side—on a computer screen—as an entirely different beast. What started as a series of if/then propositions transformed itself into a living, breathing program. Mitty loved that.
    â€œCould work,” she said. “But you might come up with nonsense. I mean—you build a hypothetical profile, but the real person, the flesh and blood, they’re totally different. Because real people never fit a profile exactly.”
    â€œAgreed.” Garrett continued to enter queries into his database. “But it’s better than nothing.”
    Mitty shrugged, then flipped through the channels on the motel TV. She skipped over Fox News, then quickly cycled back to it. She stared at the screen. “Oh, shit, big guy. We got