“Like with Delphi?” I asked, poking deliberately.
Simon shrugged. “OK, sure. Back in the 1950s, the Rand Corporation tried simply polling large numbers of people, getting them to vote on what they thought might happen in the future. John Brunner’s 1960s book The Shockwave Rider portrayed that method working better than it ever wound up performing in real life. Outside a novel, the results weren’t impressive.”
“Um. Duh? Delphi just measured the average opinion of a herd. Herds follow whatever’s fashionable. That’s no way to build a smart mob.”
“Oh? Then how would you do it?”
“Competition! That’s what wagering has always been about. It’s why the Golden Palace oddsmaking system had you spooked.”
“Hm. Then why haven’t statesmen and politicians and captains of industry long ago adapted, using competitive wagering systems to predict, and to make better policy?”
“Beats me. Maybe because betting always had such a low reputation. And it was vulnerable to cheating. Money is a good incentive, but it also warps everything, like gravity around a black hole. Anyway, haven’t there been efforts to adapt the approach lately, by setting up prediction markets?”
“Sure. Professor Robin Hanson established one of the first modern versions, with later variants run by everyone from SAP and Intrade to the Long Now Foundation. Start by gathering a large number of savvy volunteers. Only, instead of polling or voting, you get them wagering against each other—usually with pride-points, or else small charitable donations—just enough to get their competitive juices flowing. When it’s adversarial, folks care more, pay closer attention, maybe study a bit, before betting.
“IARPA then took things a bit further with their Good Judgment Project, creating a large pool and giving the volunteers access to lots of unclassified background material, tracking outcomes and seeking individuals with good predictive success. Some amateurs outscored top CIA analysts! The best were then put together in teams of various kinds—”
I leaned forward. “And the results?”
“Good. It’s partly classified. But a moderate step forward.”
“Still, only another incremental step.” I pondered for a moment. “Jeez, one would think that this IARPA approach ought to get the most investment of all.”
“Oh?” Simon glanced at his watch, then looked back at me archly. “The overall outcomes weren’t that much better than other predictive systems.”
“Yes, but you’re missing the big picture. We should be sifting the largest pool possible, not for the predictions themselves, but in order simply to find out who is right a lot.”
“Well, sure, I get that—”
“Do you? The IARPA program appears to have preselected by all sorts of criteria. How big was their pool?”
“It started at about a thousand.”
“A trifle. It should be hundreds of thousands, and with very loose criteria, with just one aim—find out who’s right more often than not. Then study the heck out of those people.”
“You’re talking about a predictions registry,” Simon said with a sigh. “It’s been tried, on a small scale. One Utopian goal was to give added credibility to people who are—as you say—right a lot. So that it translates into reputation.”
Utopian, indeed, I thought. And for once, Simon and I agreed about something.
“Like the way Nate Silver vaulted from nerdy number cruncher to media star for his election forecasts. Yeah, we should be scanning and scoring millions, so that being right a lot counts more in building credibility than money, charisma, or connections.”
“Or sleight of hand and illusion spinning?”
I lifted both shoulders in a quick shrug. Fair enough.
Simon stroked back his thinning blond hair.
“Look, I need to get back to work. And don’t you have a show to do?”
His face was easier to read than a ten-year-old playing poker for the first time. Frustration and eagerness to get away. And wondering why his boss had saddled him with a stage magician, saying “answer all his questions.”
I checked my specs. “Yeah, I gotta go. Still, I think I know why competition—markets and all similar approaches—has proved disappointing so far.”
“Oh, yes?”
“Because all systems that’ve been tried so far are voluntary. Folks who participate are already engaged, involved. They approve and want the experiment to work.”
“And what’s wrong with that?”
“Everything! In life, you don’t get to pick and choose when to compete. I can’t miss a show. A ballplayer has to try to make every game. A company that skips a year without a product is in trouble. A politician who skips an election—”
“And what does this have to do with prediction markets?”
“It’s simple, Simon. There are thousands, maybe millions of folks who make their living by pushing confident prognostications about the future. Stock analysts, cable news pundits, religious doomcasters…none of whom ever wants to be scored on the basis of accuracy! They smooth-talk others into betting on their forecasts…sometimes every penny the suckers own. They hedge their language and even say contradictory things, so they can point later at “successes.” What breakthrough tech could possibly do society and civilization more good than if we started tallying that army of persuasive arm-wavers and ranking them by how often—or how rarely—they were right?
“If it’s Utopian to imagine sifting millions of blithe predictions and applying market-like accountability for failure—giving positive reputation cred to people who are right a lot—then how much better to hold accountable those persuasive jerks who never seem to get dinged when they keep on being wrong!”
Vehemence. I seldom indulge, but this time it boiled up from within. Of course I hate such charlatans far more than the scientific hoaxers I’m called upon to help expose. As a professional liar, I concoct illusions that folks knowingly bought and paid for, fully aware that they are being fooled. But I can spot signs—the tells—when someone at a pulpit or on a pundit program or public policy forum is fibbing so smoothly, perhaps swaying millions. Warping a civilization that’s been pretty good to us.
Simon Anderson blinked at me.
“Utopian indeed. How on Earth would you construct a predictions market that’s not voluntary?”
OK, so that’s how I got the idea for Liar-Outer…and its more flashy competitor FIBuster. And it led to the reason I had to leave Vegas, running for my life.
Heck, I wouldn’t be in so much trouble if FIBuster weren’t so successful. Me and Simon and a few others started with just a few million from some Silicon Valley moguls, creating an online system where folks can post predictions made by any public figure, with or without the person’s cooperation. Starting with direct quotes, but getting past all the hedging and hemming and hawing and bullshit. Specially made AI semantics programs help distill out an essence, the gist, paraphrasing for simplicity. For what scientists call falsifiability.
When a public figure complains, he or she is given three chances to refine the paraphrasing, so long as the outcome remains clear and falsifiable. Predictors are welcome to give odds, as well. Only at that point, well, they’ve volunteered and become part of the system’s economy.
And if he or she won’t cooperate? Then it goes up anyway, as a bet. Stakes are chosen from a grid based on how rich and/or pushy the would-be Nostradamus was, and how many other folks believed ’em. Win or lose, the result is posted, with most lost wagers assigned to various charities. And if they refuse to pay? Well, that’s fine. There’s no legal obligation. But it’s funny how quickly a sense of moral obligation took shape. And cable TV con artists started taking the worst hits on their cred.