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What Research Tells Us About Making Accurate Predictions

February 02, 2015

“Prediction is very difficult,” the old chestnut goes, “especially about the future.” And for years, social science agreed. Numerous studies detailed the forecasting failures of even so-called experts. Predicting the future is just too hard, the thinking went; HBR even published an article about how the art of forecasting wasn’t really about prediction at all.

That’s changing, thanks to new research.

We know far more about prediction than we used to, including the fact that some of us are better at it than others. But prediction is also a learned skill, at least in part — it’s something that we can all become better at with practice. And that’s good news for businesses, which have tremendous incentives to predict a myriad of things.

The most famous research on prediction was done by Philip Tetlock of the University of Pennsylvania, and his seminal 2006 book Expert Political Judgment provides crucial background. Tetlock asked a group of pundits and foreign affairs experts to predict geopolitical events, like whether the Soviet Union would disintegrate by 1993. Overall, the “experts” struggled to perform better than “dart-throwing chimps”, and were consistently less accurate than even relatively simple statistical algorithms. This was true of liberals and conservatives, and regardless of professional credentials.

But Tetlock did uncover one style of thinking that seemed to aid prediction. Those who preferred to consider multiple explanations and balance them together before making a prediction performed better than those who relied on a single big idea. Tetlock called the first group foxes and the second group hedgehogs, after an essay by Isaiah Berlin. As Tetlock writes:


Since the book, Tetlock and several colleagues have been running a series of geopolitical forecasting tournaments (which I’ve dabbled in) to discover what helps people make better predictions. Over the last six months, Tetlock, Barbara Mellers, and several of their Penn colleagues have released three new papers analyzing 150,000 forecasts by 743 participants (all with at least a bachelor’s degree) competing to predict 199 world events. One paper focuses solely on high-performing “super forecasters”; another looks at the entire group; and a third makes the case for forecasting tournaments as a research tool.

The main finding? Prediction isn’t a hopeless enterprise— the tournament participants did far better than blind chance. Think about a prediction with two possible outcomes, like who will win the Super Bowl. If you pick at random, you’ll be wrong half the time. But the best forecasters were consistently able to cut that error rate by more than half. As Tetlock put it to me, “The best forecasters are hovering between the chimp and God.”

Perhaps most notably, top predictors managed to improve over time, and several interventions on the part of the researchers improved accuracy. So the second finding is that it’s possible to get better at prediction, and the research offers some insights into the factors that make a difference.

Intelligence helps. The forecasters in Tetlock’s sample were a smart bunch, and even within that sample those who scored higher on various intelligence tests tended to make more accurate predictions. But intelligence mattered more early on than it did by the end of the tournament. It appears that when you’re entering a new domain and trying to make predictions, intelligence is a big advantage. Later, once everyone has settled in, being smart still helps but not quite as much.

Domain expertise helps, too. Forecasters who scored better on a test of political knowledge tended to make better predictions. If that sounds obvious, remember that Tetlock’s earlier research found little evidence that expertise matters. But while fancy appointments and credentials might not have correlated with good prediction in earlier research, genuine domain expertise does seem to.

Practice improves accuracy. The top-performing “super forecasters” were consistently more accurate, and only became more so over time. A big part of that seems to be that they practiced more, making more predictions and participating more in the tournament’s forums.

Teams consistently outperform individuals. The researchers split forecasters up randomly, so that some made their predictions on their own, while others did so as part of a group. Groups have their own problems and biases, as a recent HBR article explains, so the researchers gave the groups training on how to collaborate effectively. Ultimately, those who were part of a group made more accurate predictions.

Teamwork also helped the super forecasters, who after Year 1 were put on teams with each other. This only improved their accuracy. These super-teams were unique in one other way: as time passed, most teams became more divided in their opinions, as participants became entrenched in their beliefs. By contrast, the super forecaster teams agreed more and more over time.

More open-minded people make better predictions. This harkens back to Tetlock’s earlier distinction between foxes and hedgehogs. Though participants’ self-reported status as “fox” or “hedgehog” didn’t predict accuracy, a commonly used test of open-mindedness did. While some psychologists see open-mindedness as a personality trait that’s static within individuals over time, there is also some evidence that each of us can be more or less open-minded depending on the circumstances.

Training in probability can guard against bias. Some of the forecasters were given training in “probabilistic reasoning,” which basically means they were told to look for data on how similar cases had turned out in the past before trying to predict the future. Humans are surprisingly bad at this, and tend to overestimate the chances that the future will be different than the past. The forecasters who received this training performed better than those who did not. (Interestingly, a smaller group were trained in scenario planning, but this turned out not to be as useful as the training in probabilistic reasoning.)

Rushing produces bad predictions. The longer participants deliberated before making a forecast, the better they did. This was particularly true for those who were working in groups.

Revision leads to better results. This isn’t quite the same thing as open-mindedness, though it’s probably related. Forecasters had the option to go back later on and revise their predictions, in response to new information. Participants who revised their predictions frequently outperformed those who did so less often.

Together these findings represent a major step forward in understanding forecasting. Certainty is the enemy of accurate prediction, and so the unstated prerequisite to forecasting may be admitting that we’re usually bad at it. From there, it’s possible to use a mix of practice and process to improve.

However, these findings don’t speak to one of the central findings of Tetlock’s earlier work: that humans typically made worse predictions than algorithms. Other research has found that one reliable way to boost humans’ forecasting ability is to teach them to defer to statistical models whenever possible. And the “probabilistic training” described above really just involves teaching humans to think like simple algorithms.

You could argue that we’re learning how to make better predictions just in time to be eclipsed in many domains by machines, but the real challenge will be in blending the two. Tetlock’s paper on the merits of forecasting tournaments is also about the value of aggregating the wisdom of the crowd using algorithms. Ultimately, a mix of data and human intelligence is likely to outperform either on its own. The next challenge is finding the right algorithm to put them together.

Walter Frick is a senior editor at Harvard Business Review.

This article is about FORECASTING
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