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MARKET FORECASTS Prediction markets can be uncannily accurate sometimes. Scientists have begun to understand why they work, and how they can fail. HHMMMMN _t 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 'Repro- ducibility Proj ect' an effort to repeat dozens of psychology experiments to see which held upl. \"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 partici- pants buy and sell 'shares' in a future event at a price that reects their collective wisdom about the chance of the event happening. As a control, Dreber and her colleagues rst asked 303 l NATURE l VOL 538 l 20 OCTOBER 2016 a group ofpsychologists 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 examinedz, 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 times. Experiments such as this are a testament to the power of prediction markets to turn indie viduals' guesses into forecasts of sometimes startling accuracy. That uncanny ability ensures ILLUSTRATION BY SEBASTlEN THl BAULT E -'_J _'.j r.) x 2! CI in that during every US presidential election, vot ers avidly follow the standings for their favoured candidates on exchanges such as Betfair and the Iowa Electronic Markets (IEM). But pre- diction markets are increasingly being used to make forecasts of allkinds, on everything from the outcomes of sporting events to the results of business decisions. Advo cates main tain that they allow people to aggregate information without the biases that plague traditional forecasting methods, such as polls or expert analysis (see page 304). In science, applications might include giving agencies impartial guidance on the proposals that are most worth fund ing, helping panels to nd a consensus in climate science and other elds or, as PREDICTS launched the notforprofit IEM as a network based teaching and research tool; ahead of the 8 November presidential election that year, they set up a market to predict the fraction of votes that would go to each of the presidential candi dates (see 'How a market predicts'). The frac tions changed daily as traders interpreted fresh Prediction markets use investors' opinions to generate a price for 'shares' in a given event. HOW A MARKET Each share will pay US$31 ii the event comes true. The price rises or falls Dreber showed, giving researchers a fast Em \""1\" $"PP'Y \"ames and lowcost way to identify the studies T \"nth demand' that might face problems with replication. _ _ . $9.30 = 70% But sceptics point out that prediction ingsltlodrl':g::laf markets are far from infallible. \"There $050 in the event's The balance point is a viewpoint among some people that The. initial probability: \"presents the . . v nce Is investors' consensus kc l BE probabllrty. tion 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, predictionmarket 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 HIGH: IIF EIHBLIHE 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 sup ply and demand became centralized on Wall Street, where wealthy New York City business men 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 profes sional 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. The predictionmarket idea was revived by the spread of the Internet, which dramatically lowered the entry barriers for creating and par ticipating in prediction markets. In 1988, the University of Iowa's Tippie College of Business EXAMPLE: 2008 [IS PRESIDENTIAL ELEBTIUH Barack Obama (Democrat) versus John McCain (Republican) The market's prediction constantly shifted to accommodate news. polls and other information. Democrat 1 Critlcal stage of finan cial collapse 0 ba ma secures nomination N _ 2222:: 2:322:1lm named ' J _ nominatlon g mate MI 0 lIIlII Jan 08 Jul 08 Reoubl can information about polls, the economy and other issues. On the eve of the election, the market predicted that the Republican nominee, George H. W Bush, would be victorious with 53.2% of the vote which is exactlywhat he got. And in 2008, a study found that the IEM's predictions across ve presidential elections were more accurate than the polls 74% of the time". The success of the IBM helped to inspire the creation of dozens of other prediction markets. In 1996, for example, the Hollywood Stock Exchange was launched to forecast openingweekend boxoffice take and other movierelated outcomes; its markets cor rectly predicted that Hamlet would be a op that year and that Jerry Mnguire would be a hit. In the early 2000s, employees of informa tiontechnology company HewlettPackard l'EHIUI'll'. Iililil participated in prediction markets that beat the firm's official projections of quarterly printer sales 75% of the time. And in September 2002, six months beforethe USled invasion of Iraq, the Dublinbased betting site TradeSport s. com gained international notoriety when it ran a prediction market on when Iraqi dicta tor Saddam Hussein would b e ousted. By the time the war began in March 2003, betters were 90% certain Hussein would be out byApril and 95% sure he'd be gone by May or June. He was deposed in April. llllllE'l' RESEARCH Prediction markets have also had some highprole misres, however such as giving the odds of a Brexit 'stay' vote as 85% on the day of the referendum, 23 June. (UK citizens in fact narrowly voted to leave the European Union.) And prediction markets lagged well behind conventional polls in predicting that Donald Trump would become the 2016 Republican nominee for US president. Such examples have inspired academ ics to probe prediction markets. Why do they work as well as they do? What are their limits, and why do their predictions 5 ometimes fail? Perhaps the most fundamental answer to the first question was provided in 1945 by Austrian economist Friedrich Hayek. He argued that markets in general could be viewed as mechanisms for collecting vast amounts of information held by indi viduals and synthesizing it into a useful data point namely the price that peo ple are willing to pay for goods or services. Economists theorize that prediction markets do this information gathering in two ways. The rst is through 'the wis dom of crowds' aphrase popularized by business journalist Iames Surowiecki in his book of that name (Doubleday, 2004). The idea is that a group of people with a sufficiently broad range of opin ions can collectively be cleverer than any individual. An oftencited case is a game in which participants are asked to estimate the number of jelly beans in a jar. Although indi vidual guesses are unlikely to be right, the accu mulated estimates tend to form a bell curve that peaks close to the actual answer. When investor )ackTreynor ran this experiment on 56 students in 1987, their mean estimate for the number of beans 871 was closerto the correct answer of 850 than all but one of their guesses? As Surowiecki and others have emphasized, however, crowds are wise only ifthey harbour a sufficient diversity of opinion. \"Then they don't when people's independent judgements are skewed by peer pressure, panic or even a char ismatic speaker the wisdom of crowds can easily fall prey to collective breakdowns. The housing bubble of the mid2000s, which was a major contributor to the 200708 financial 9f) r'TRl-'D \"3:116 l um 3.19 l NATTiTPF' l Qn'! Illila FEATURE crash, was one such breakdown of judgement. But this is where the second market mechanism comes in. Sometimes called the marginaltrader hypothesis, it describes how in theory there will always be individuals seeking out places where the crowd is wrong. In the pro cess, these traders will identify undervalued contracts to buy and overvalued contracts to sell, which tends to push prices back towards a sensible value. An example can be seen in the 2015 lm The Big Short, which dramatizes the true story of a hedge fund that bet against the irrational exuberance of the US housing market and gained substantially from the crash. its funding ended. But it helped to inspire Metaculus, a market launched in November 2015 by Aguirre and his colleague Greg Laugh lin, an astrophysicist now at Yale University in New Haven, Connecticut. The site grew out of Aguirre's interest in finding 'superpredictors' people whose forecasting skills are far above average. Metaculus asks participants to estimate the prob abilities of such things as, \"Will a clini cal trial begin by the end of 20 1 7 using CRISPR to genetically modify a living human?\" or \"Will the National Ignition Facility announce a shot at breakeven fusion by the start of 20 17? \". As in SciCast, Metaculus participants do \"WHEN SllMEIINE STAHTS TIT SUGGEST A BET. PEllPlE IMMEDIATELY STAHT Tll BlAHIFY WHAT THEY MEAN.\" Laboratory experiments have been used to test many aspects of this theoretical framework, including how well prediction markets aggre gate information under different conditions. In a 2009 experiment\" that was designed to mimic scientic research and publishing, research ers set up three prediction markets in which participants tried to predict which hypothesis about a fictitious biochemical pathway would end up being true. FIElII-TESTIHE THE FIITHHE In one market, key pieces of information about the pathway were available to all participants; the traders quickly converged on the correct answer. in another, analogous to proprietary corporate research, information was privately held by individuals; the traders often failed to reach a consensus. And in the third, analogous to results being discovered in di'erent labs and then published in journals, information was ini tially kept private and then made public. The market was able to find the right answer but the individuals who discovered useful informa tion first could use their private knowledge to anticipate the markets and extract a small prot. One of the first prediction markets devoted exclusively to scientific questions grew out of a project started in 201 1 by economist Robin Hanson of George Mason University in Fair fax, Virginia. Eventually known as SciCast, the project included a website where participants could wager on questions such as, \"Will there be a labconrmed case of the coronavirus Middle East Respiratory Syndrome (MERS or MERSCOV) identified in the United States by 1 June 2014?\". (There was.) SciCast's assessments were more accurate than an unin formed prediction model 85% of the time (see go.nature.coml2dm611p). SciCast was discontinued in 2015, when 310 | NATURE not use actual money: players instead move a slider representing their belief in the likeli hood of an answer and accrue a track record for being correct. The lack of cash bets is partly a matter of practicality, says Aguirre. \"When it's 'Will Hillary win?', zillions of people will buy on that. But if it's 'Will this new paper on arXiv get more than ten citations?', you're not going to find enough people with real money to make an accurate prediction.\" But it's also the case that real money isn't strictly necessary for a successful prediction market: several stud ies\" have shown that traders can be equally well motivated by the prestige of being right. Metaculus currently has around 2,000 active users, although its creators hope to accrue 10,000 or more. Already, the site has produced evidence that successful prediction is a skill that can be learned. The best players work out the optimal time to adjust their guesses up or down, and their performance gradually improves. Laughlin and Aguirre suggest that Metaculus could be useful to journalists and other members of the public who want to know which questions mo st interest scientists. Fund ing agencies might similarly be attracted to its results. \"Having prediction markets that are getting an evenhanded assessment is poten tially a way of aiding the decision for what pro jects are most worth funding,\" says Laughlin. But scientific prediction markets have yet to gain much traction with researchers or the public. One important reason is that most political and business questions get clearcut answers in relatively short time periods, and this is where prediction markets excel. But few wouldbe traders have the patience to endure the decades of effort, ambiguity and experi mentation that are often required to answer questions in science. This problem is hardly unique to prediction VOL 538 | 20 OCTOBER 2016 markets, however: \"It is in general easier to make shorttenn than longtenn predictions,\" says Aguirre. As long as prediction markets offer a wayto update guesses in light of new informa tion, proponents argue, they will do as well or better than other forecasting metho ds. Scientic prediction markets also suffer more from ambiguity issues than do political or economic ones. In an election, one person is eventually declared the winner, whereas in sci ence, resolutions are rarely so neat. But predic tionmarket advocates don't think that this is necessarily a cause for concern. \"When some one starts to suggest a bet, people immediately start to clarify what they mean,\" says Hanson. Aguirre says that he and Laughlin take great pains on Metaculus to ensure that predictions are welldefined and easy to understand. \"Thether prediction markets can work for science remains an open question. When Dreber's team repeated 18 economics experi- ments as part of a followup to her psychol ogy investigation, both the prediction markets and surveys of individuals overestimated the odds of each study's reproducibility\". Dreber isn't sure why this happened. She points out that the psychologists in the rst study were all already interested in replication whereas the economists in the second were not involved in the reproducibility project so they might have been better at collectively estimating reproducibility. Prediction markets in general still need to deal with challenges such as how to limit manipulation and overcome biases. Yet con ventional representative polling, which once relied on answers from phonecalls to randomly sampled landlines, is being jeopardized by the movement to mobile phones and online mes saging. Because the accuracy of prediction markets is at least on par with, if not better than, polls, economist David Rothschild of Microsoft Research in New York City thinks that prediction markets are well placed to take over if polling goes into decline. \"1 can create a poll that can mimic everything about a predic tion market,\" he says. \"Except markets have a way of incentivizing you to come back at 2 am. and update your answer.\" I Adam Mann is a freelance writer in Oakland, California. 1. Open Science Collaboration Perspect Psychol. Sci. 1', SST-65:0 (2012). Open Science Collaboration Science 349. aac4? 16 (2015). Dreber,A eial. Proc. Natlllcaci. Sci. USA 112, 15343-1534? (2015). Berg, J. E.. Nelson. F. D. & Rietz. T. A lnt .l. Forecast. 24, 285-300 (2008). Treynor, J. L Financ. Anal. J. 43, 50-53 (198?). Almenberg, J.. Kittlitz, K. 8. Pfeiffer. T. PLoS ONE 4. e8500 (2009). i". Rosenbloom. E S. 8. Notz, W. Electron. Mark. 16, 63-69 (2006). 8. Servan-Schreiber, E.. 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