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What are Prediction Markets?

Prediction markets are fascinating platforms where individuals can forecast and place bets on future events, from elections to economic indicators. They harness the wisdom of crowds, often yielding surprisingly accurate predictions. By trading shares on the outcome of events, participants effectively reveal collective expectations. Intrigued by the power of collective foresight? Discover how prediction markets might shape our understanding of tomorrow.
Michael Anissimov
Michael Anissimov
Michael Anissimov
Michael Anissimov

Prediction markets are a system for predicting the likelihood of various events, and making money off accurate predictions. About a dozen exist and are open to the public, including the Popular Science Predictions Exchange, TradeSports, the Iowa Electronic Markets, NewsFutures, Bet2Give, Hollywood Stock Exchange, The simExchange, Intrade, and Betfair. They are generally centered around a general domain of predictions, such as politics, movies and films, and technology and futurism. Other names for prediction markets include decision markets, information markets, idea futures, event derivatives, and virtual markets.

The basic idea of a prediction market is that more accurate predictions will emerge from the betting activity of the collective, which rewards accurate predictions through wins and punishes inaccurate predictions through losses. However, many prediction markets use imaginary money rather than real money. Still, evidence shows that prediction markets using fictional money can be successful at making accurate predictions. One study compared NFL predictions for an entire season using an imaginary money based prediction market and a real money based prediction market, and found the latter to be no more predictive than the former.

Businessman giving a thumbs-up
Businessman giving a thumbs-up

Prediction markets have a significant history, stretching back to at least the 1940s, when people bet on which politicians would win a given campaign. However, it wasn't until 1990 when the first corporate prediction market was created, by Robin Hanson at the software company Project Xanadu. This prediction market was used to bet on scientific controversies of the time, such as whether or not cold fusion is feasible. This was followed by other prediction markets in the late 1990s and early 2000s, when Internet use became widespread and the idea of a prediction market became more widely known.

Today, there is controversy over the effectiveness of prediction markets, or even whether they are ethical or not. For instance, Robin Hanson, mentioned earlier, tried to set up a prediction market, using public funding, shortly after 9/11 to judge the probability of terrorist attacks. This idea was quickly struck down by people who saw the whole idea as unethical. Academics have published papers arguing that prediction markets are only useful when the probability of the event is close to 1 or 0. For prediction markets to prove their efficacy in the real world to a wider audience, they will need to make better demonstrations of their predictive power.

Michael Anissimov
Michael Anissimov

Michael is a longtime AllTheScience contributor who specializes in topics relating to paleontology, physics, biology, astronomy, chemistry, and futurism. In addition to being an avid blogger, Michael is particularly passionate about stem cell research, regenerative medicine, and life extension therapies. He has also worked for the Methuselah Foundation, the Singularity Institute for Artificial Intelligence, and the Lifeboat Foundation.

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Michael Anissimov
Michael Anissimov

Michael is a longtime AllTheScience contributor who specializes in topics relating to paleontology, physics, biology, astronomy, chemistry, and futurism. In addition to being an avid blogger, Michael is particularly passionate about stem cell research, regenerative medicine, and life extension therapies. He has also worked for the Methuselah Foundation, the Singularity Institute for Artificial Intelligence, and the Lifeboat Foundation.

Learn more...

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