Even if Mazella didn’t seek to make an example of me, I’d be through in this town.
“There! I’m closing up now…criminy!”
I left the self-sealing wall patch to dry, grabbing my tools and hopping back—
—as Ludmilla Kilonova knelt and barfed all over the floor, including my left shoe—
—just before a giant person wearing Golden Palace livery turned the corner and stopped short, staring in ill-concealed disgust.
“Uh, we need an auto-mop in section forty-seven,” he murmured into a shoulder mike. “Yeah, third one tonight. Better tell food services.”
I folded away my tool kit behind my back—one-handed as the other hand stroked my “wife’s” head.
“I guess she had too much,” I started to explain, slurring to disguise my voice. “We’ll clean it up. Sorry. So sorry.”
The guard shook his head and made shooing motions.
“Don’t worry, sir. It happens. Maybe you should head up to your room now. Get some sleep.”
Solicitous guard. I busied myself helping Ludmilla stand and then shuffle on, giving us both an excuse to divert our faces from every camera as we followed a path chosen by the drone, supporting each other, moaning and grumbling.
Only when we were finally ensconced in room 1245 on the second floor, where the other couple had checked in yesterday—and when the flitting drone confirmed there were no bugs—did we both straighten up.
“Smooth move,” I commented while peeling off the pixelated suit. “But did you have to throw up on my shoe? I like this pair.”
“Oh, quit bellyaching,” she replied from the bathroom, between gargles. “You try doing that on command.”
I shrugged and refrained from commenting that I’ve given tougher performances. Still, she ranked several notches higher in my esteem. Especially now that, for the very first time, I could see her eyes, uncovered by specs, glittering with adrenaline rush.
“So,” she commented, wiping her mouth with a washcloth. “What’ll we do until checkout time?”
Feigning fatigue and nonchalance—though I knew she could read me, too—I faked a yawn.
“I wonder what’s on pay-per-view.”
The Mazellas were on to something with their betting system. Word spread among bookies—Vegas and online—not to try nibbling at the Golden Palace oddsmakers.
Thanks to the tap that Ludmilla and I planted, Sophia Van Took’s team quickly zeroed in on the GP secret sauce—an improved algorithm for weighting wagers from a crowd. An incremental improvement, then. No transcendent or magical leap. Not this time. I could read relief on Sophia’s face, plus disappointment.
That was the holy grail, of course—the combined hope and dread that propelled interest by so many groups, from Amazon and Google to NASA. From Palantir and TIBCO to Goldman-Sachs, to the Vatican and the Chinese People’s Liberation Army. Humanity badly needed better predictive methods. But if one group or secretive elite ever got their hands on something truly effective, it wouldn’t matter much whether they were corporate, criminal, or some foreign alliance. Human nature being what it is, even Sophia’s agglomeration of academics and civil servants might be tempted to leverage such power, rationalizing they were only acting for the common good.
And her team operated under safeguards. Multiple paths of civilian oversight. I shivered at the thought of a true anticipation engine being discovered and monopolized by the likes of Johann Mazella.
Is such a machine or system even possible? The dream of every prophet, fortune-teller, priest, planner, investor, protector, and lover, ever since our brows got pushed forward by those lamps, the prefrontal lobes.
In modern times, much of the investment went into “intelligent” computation, fed with massive information. Ideally, all information. The World Meteorological Model consumed more computing power than some major cities, dividing Earth’s surface, atmosphere, and oceans into ever smaller cells, transforming those pathetic old four-hour “weather reports” into a finely meshed gas-vapor-energy sim that lets folks plan what to wear on vacation, ten days ahead. A miracle so routine that several billion ingrates take it for granted, then diss the genius scientists who built the WMM for believing climate can change. Don’t get me started.
And yet, even the best modeling programs kept bumping against their twin enemies, complexity and chaos. The famous butterfly effect, where time (our ancient foe) amplifies even tiny perturbations—say, the flapping of a monarch’s wings—into a hurricane of downstream variations. Later efforts to push the WMM forward by even one more hour threatened to double the system’s computational needs.
How about quantum computers? Arrays of qubits processing fine skeins of possibility in parallel. Spectacularly parallel—if mystically inclined cyberneticists are right that quantum machines tap networks of entangled computers in alternate universes. And yet…
…yet the mesh models seemed helpless when it comes to analyzing human affairs.
Clearly, the problem was not in the machines, but software. Lacking a Hari Seldon, but swamped with all kinds of Big Data, we didn’t know how to mix and stir and bake all the ingredients.
From ancient warlords to insurance companies seeking better underwriting formulas; from investment arbitrage to handicapping political races, to predicting the next move by terrorists or strategic powers, to planning a new store for your doughnut-shop chain, there would be no end of eager customers for improved foresight services.
Only, even if you solve the complexity and chaos problem, there’s another rub. If you keep the method secret, you’ll eventually turn the whole world against you, or else fall into multiple traps of overconfident delusion. But if you share it, adversaries will apply every new forecasting method and cancel each other out! We’ve seen that happen to every brilliant stock market analytics tool.
The cancellation effect can be a good thing! What was it Sun Tzu said about war? Or maybe Clausewitz? Conflict only becomes violently physical when one side is mistaken about the other’s abilities or intentions. It’s why Eisenhower, humanity’s second most underrated statesman, made such a point of “open skies,” pushing development of spy satellites so both sides might see and predict better, calming their worst fears.
As Sophia’s team calmed when they evaluated data from the tap that Kilonova and I installed at the Golden Palace.
All right, so the Mazellas hadn’t made an epic breakthrough. But what kind of breakthrough had they made?
Sophia’s project caught my curiosity, so her top analyst—Simon Anderson—gave me reading materials. Books by Poundstone and Rebonato and Hanson and Pentland and MacLean shed some light on quantitative analysis and risk assessment. The details went way over my head—especially since I still had nine shows a week to perform—but I dug some of the gist. Enough to realize the quants had bitten off way more than they could chew. The better their models got, the more likely they’d prove brittle when some fickle, human factor veered unexpectedly.
“You get a more robust system when there’s diversity,” Anderson explained. “With scenarios, the storyteller often pushes one part of the narrative, trying to make a point. But that tendentious tendency eases when you increase the number of contributors.”