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—Alex Pentland, Reality Mining of Mobile Communications: Toward a New Deal on Data (2008)
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It’s said to be embedded in our prefrontal lobes, twin nubs above the eyes that let us think about the future. Somehow, a bunch of clever apes fell into the habit of fantasizing about what might happen next…

…if I enter that thicket…

…if we refuse the other tribe’s ultimatum…

…if I propose this idea at today’s meeting…

…or wear this outfit…

…or declare this new rule…

…or try to run that yellow light…

Einstein called it the gedankenexperiment, or thought experiment. We’re good at telling ourselves stories about what lies ahead. And certainly the habit does help, a bit, exposing obvious errors to avoid…

…though only up to a point. Anticipation all too easily becomes hallucination, envisioning, and then expecting what we want to happen. That’s fine in a novel, film, or stage show. But it’s a damn poor way for leaders to make policy.

Hey, I just explained the saga of endlessly repeated blunders called “history.” Nonetheless, we do keep trying. Tea leaves and priestly pronouncements gave way to primly mechanistic war games—which convinced Czar Nicholas and Kaiser Wilhelm to charge ahead, destroying both empires.

War games transformed into scenario-based planning. (So now you had a decision tree of diverse ways to be wrong!) Then clever mathematical models fought each other for relevance—a darwinnowing that did help to expose this or that danger, but almost never pointed to solutions.

Are your models flawed? Add more variables! Or more data…big data sets. Humongous ones. Gather and collate everything!

And feed it to AI…but what kind of AI? There are so many, all disappointingly far from omniscient, all just as confused as the rest of us when they try their hand at prophecy. Especially the “quants” who would get rich exploiting market models—preying on those who are just one step behind—then wreck their own trading house by pressing the wagers too far. Just like a Vegas gambler, so sure that his winning streak is something ordained to last forever.

Always lurking like a chiding ghost is the shade of Hari Seldon, Isaac Asimov’s fictional “psychohistorian,” who made prediction look so easy on the pages of a 1940s wish-fantasy novel. The archetype seer whose models of civilization—the rise or fall of whole empires—might finally reduce all irksome human chaos and variability to cool numbers and equations. How many economists, sociologists, politicians, and/or psychopaths started out by lifting their gaze from a Foundation book, staring ahead, and murmuring: “Hey, I could do that!” Paul Krugman. Osama bin Laden. Milton Friedman, Shoko Asahara, Carl Sagan, Newt Gingrich…. The sheer range of nerdy Asimovians, from brilliant to crazy but all drawn to the same dream, would be amazing if it weren’t frightening.

But, heck, why not blame Karl Marx, whose followers felt so sure they sussed the driving forces of humanity? Or believers in the greatest (if heretical) acolyte of Marx—Ayn Rand—whose seductive incantations followed all the master’s patterns to reach opposite conclusions.

And so, “laws of sociology” grew less fashionable. Simulations improved only glacially, while choking on tsunamis of Big Data. At which point the powers of the world—desperate for guidance—rediscovered Adam Smith.

Maximize the number of participants! If individual models and modelers can fool themselves, then make them compete with one another in a marketplace of ideas. Utilize the same competitive forces that propel evolution—the most creative force in the universe! The same driver as markets. As science.

Hence the “wisdom of crowds” became the next fashion in prognostication. And there were good initial signs! Wikipedia, Kickstarter, Duolingo, and Amazon’s Mechanical Turk all showed promising outcomes from lateral cooperation and competition, so much more agile than hierarchical command.

* * *

Crowd-sourced analysis started with SETI@home, when thousands lent their home computers to a network that analyzed radio telescope data, sifting for alien signals—an approach that expanded to genome research, protein-folding problems, and a myriad other collaborations between scientists and “smart mobs” of amateurs, like the Zooniverse Project, where amateur aficionados help identify lunar craters, translate old ships’ logs, identify galaxies, and find planets round other stars.

Clearly, some sort of distributed wisdom was at work. But the sixty-four-trillion-dollar question loomed. Can it be applied to peering ahead? To prediction?

Again, look at human history. Sure, arrogant human leaders proved foolish, nine times out of ten. But were there cases, in the past, when mobs or mass movements did any better? Could mobs be made much, much smarter?

—M.N. Plano, How We Did It (2025)
* * *

Kilonova took care of the most dangerous cameras, and a good thing too. Mazella’s mob would recognize me in two heartbeats. Quicker than that, now that all casinos employed computerized face recog. Sure, Sophia Van Took would offer me a new identity—and face-job—if things went wrong tonight. But I like Vegas. And showbiz.

The Feds had better be one step ahead this time, I thought, glancing at my clandestine companion, then kissing our little drone for luck and letting it go. The machine would swoop about, barely more noticeable than a gnat, noting every lens and biometric scanner along our path, then latch-spooking those we could not evade. Unless Sophia’s people had missed a step in the perpetual tug-of-info-war. One mistake and the least of my problems might be hiding under Witness Protection. Johann Mazella played for keeps.

The drone hovered just outside our hiding place, under a buffet table where a bribed busboy had wheeled us as the Golden Palace kitchens were closing. The little flier took its time, then confirmed that we truly were in a surveillance shadow.

I glanced at my companion. Ludmilla Kilonova owned a pleasant smile, though I had never seen her eyes. Those windows to the soul were always hidden by shades—high-tech specs that overlaid the world with augmented reality data. But I suspected another reason, a flattering one. She knows what I can do.

We’re on. Her specs picted to mine. A more secure channel than whispering.

Fine, I replied, scrolling words with my tongue. You first, Mata Hari.

I couldn’t see the eye roll, but twitches of cheek muscles confirmed one. Well, well, fair enough. Kilonova’s cover story was genuine—she really was in town to give a talk on late stellar evolution at the astronomical convention. Still, I never had the slightest doubt about her real profession.

Follow me, magic man.

She wriggled her way out of the buffet table, then slithered across a tile floor inset with gilt GP casino logos. I might have enjoyed watching her graceful moves, had I not been worried about my own ass. The specs showed a very narrow tunnel for us to crawl along. It wouldn’t do for my jeans to bump the boundary.