Reading this piece induced flashbacks of 2008 when prediction markets were all the rage.
The power of prediction markets
Scientists are beginning to understand why these ‘mini Wall Streets’ work so well at forecasting election results — and how they sometimes fail.
It was a great way to mix science with gambling, says Anna Dreber. The year was 2012, and an international group of psychologists had just launched the ‘Reproducibility Project’ — an effort to repeat dozens of psychology experiments to see which held up1. “So we thought it would be fantastic to bet on the outcome,” says Dreber, who leads a team of behavioural economists at the Stockholm School of Economics.
In particular, her team wanted to see whether scientists could make good use of prediction markets: mini Wall Streets in which participants buy and sell ‘shares’ in a future event at a price that reflects their collective wisdom about the chance of the event happening. As a control, Dreber and her colleagues first asked a group of psychologists to estimate the odds of replication for each study on the project’s list. Then the researchers set up a prediction market for each study, and gave the same psychologists US$100 apiece to invest.
When the Reproducibility Project revealed last year that it had been able to replicate fewer than half of the studies examined2, Dreber found that her experts hadn’t done much better than chance with their individual predictions. But working collectively through the markets, they had correctly guessed the outcome 71% of the time3.
Experiments such as this are a testament to the power of prediction markets to turn individuals’ guesses into forecasts of sometimes startling accuracy. That uncanny ability ensures that during every US presidential election, voters avidly follow the standings for their favoured candidates on exchanges such as Betfair and the Iowa Electronic Markets (IEM). But prediction markets are increasingly being used to make forecasts of all kinds, on everything from the outcomes of sporting events to the results of business decisions. Advocates maintain that they allow people to aggregate information without the biases that plague traditional forecasting methods, such as polls or expert analysis.
In science, applications might include giving agencies impartial guidance on the proposals that are most worth funding, helping panels to find a consensus in climate science and other fields or, as Dreber showed, giving researchers a fast and low-cost way to identify the studies that might face problems with replication.
But sceptics point out that prediction markets are far from infallible. “There is a viewpoint among some people that once you set up a market this magic will happen and you’ll get a great prediction no matter what,” says economist Eric Zitzewitz at Dartmouth College in Hanover, New Hampshire. That is not the case: determining the best designs for prediction markets, as well as their limitations, is an area of active research.
Nevertheless, prediction-market supporters argue that even imperfect forecasts can be helpful. “Hearing there’s an 80 or 90% chance of rain will make me take an umbrella,” says Anthony Aguirre, a physicist at the University of California, Santa Cruz. “I think there’s a big space between being able to time travel and physically see what will happen, and then throwing up your hands and saying it’s totally unpredictable.”
The magic of gambling
People have been betting on future events for as long as they have played sports and raced horses. But in the latter half of the nineteenth century, US efforts to set betting odds through marketplace supply and demand became centralized on Wall Street, where wealthy New York City businessmen and entertainers were using informal markets to bet on US elections as far back as 1868. These political betting pools lasted into the 1930s, when they fell victim to factors such as stricter gambling laws and the rise of professional polling. But while they lasted they had an impressive success rate, correctly picking the winners of 11 out of 15 presidential races, and correctly identifying that the remaining 4 contests would have extremely tight margins....MORE