“I bet you didn’t wake up thinking you had to answer that question today,” says Warren Hatch, who is co-leading this workshop.
There are about 12 of us taking this training, including a guy from the Department of Defense. Over the next two days, we scratch our heads, trade bits of insight, try to shed our cognitive biases (more on that later), and see if we have the chops for predicting things professionally. I am definitely out, but I suspect a couple in this group qualify. Those who do will be a step closer to gaining an elite, though geeky, kind of status: It’s called a “superforecaster.” And if you are one, you can join the global network of über predictors, the best of the best, who work with the company that arranged this workshop in the first place. It is called Good Judgment. Hatch is its CEO.
While I’m at my laptop sweating it out for Good Judgment in September, experts are making headlines in the real world answering similar questions. “Inflation is elevated and will likely remain so in coming months,” predicts Federal Reserve chairman Jerome Powell; “There is a chance that we will see big declines in coming years,” wagers a Yale economist on home prices. Anthony Fauci, meanwhile, says a Northeast surge of Delta is “possible.” It’s an interesting contrast. As a culture, we’ve come to accept “Likely,” “Possible,” or “There’s a chance in the coming years” as the best our top authorities can tell us about what lies ahead. But what does likely mean, in a concrete way? Is it a 51 percent odds of happening, or 85 percent? Are 2022 and 2023 considered “coming years,” or are 2024 and 2025?
We may not demand this level of specificity from our experts, but we sure need it in business. And as Good Judgment proves, you actually can quantify vague hunches like these with scalpel-like accuracy— simply with the human brain, no AI or big data.
Few people do it with more Olympian skill than Good Judgment’s superforecasters. But as with most sports, we can all get better. We just need to train.
UNLIKE MANY COMPANIES that begin life in a dark bar scribbled on a cocktail napkin, Good Judgment was born in the belly of the U.S. government. In a way, it goes back to 9/11. After analysts appeared to miss signals of the catastrophic terrorist attack, a group called IARPA (or Intelligence Advanced Research Projects Activity) was created in 2006, modeled after the defense agency DARPA. Its goal was to conduct daring, innovative research that improves American intelligence. By 2010, the intelligence community had started using an internal classified prediction market where top-secret-cleared employees could make trades on whether an event would happen. But IARPA wondered if there was an even better way to use the wisdom of the crowd to foresee what was coming.
That’s why, in 2011, it launched a huge forecasting tournament for the public. At the beginning, there were five teams, and over the next four years, thousands of ordinary Joes and Janes would answer about 500 questions, like: Will North Korea launch a new multistage missile before May 10, 2014? Will Robert Mugabe cease to be president of Zimbabwe by September 30, 2011? The teams had to reach certain benchmarks of accuracy; if they failed, they were eliminated. After the first two years, only one team remained. It was led by Philip Tetlock and Barbara Mellers at the University of Pennsylvania’s Wharton School and called Good Judgment.
Tetlock was already deep into the science of prediction. Back in the 1980s, he’d become curious as to why so many foreign policy experts had failed to predict the Soviet Union’s fate, and it inspired him to analyze broad swaths of predictions. As it turns out, the average expert was roughly as accurate as a dart-throwing chimpanzee. (That’s not quite how he put it, but close enough that he doesn’t mind the joke.) So he developed a more systematic approach—not just to predictions but to identifying the kinds of people who are good at making predictions. To compete in IARPA’s tournament, he and Mellers recruited 3,200 volunteers, then winnowed them down to the top 2 percent, which they called super forecasters. Among that group was Hatch, a Wall Street guy who’d left Morgan Stanley to set up his own small investment firm, and who was trading on a forecasting platform on the side.
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