Decision events of common interest such as elections or contests are often preceded by measures to predict their outcome. Conventional measures include polls and interviews. From a perspective of collective knowledge, the accuracy of such measures is naturally limited, because the opinions of insiders have the same weight as the opinion of clueless individuals.
Prediction markets  are a way to map the probability of an event to the price of a market share by allowing participants to bet on or against the event and aggregating their opinions. The advantage of this method emerges from a self-controlling mechanism of the market’s participants: Insiders will place a much higher bet than individuals with little knowledge about the event . Prediction markets thus draw much of their accuracy from insider trading, a behavior that is frowned upon or even prosecuted on many other markets. Participants generally have a motivation to get more information and thus increase their predictions’ accuracy.
Prediction markets have not always been able to beat other methods’ accuracy , but their predictions are considered better than that of “almost any of the individual participants in the market” . However, prediction markets can suffer from problems known from traditional markets like market manipulation attempts and speculative bubbles. While prediction markets based on real currency are not legal in many parts of the world, real money (or real risk) is considered a key ingredient to their accuracy. Therefore many of the existing initiatives use virtual money combined with prizes for well-performing participants.
Early examples of successful prediction markets are the Iowa Electronic Markets  and markets used internally by well-known corporations such as Hewlett-Packard and Intel for sales- and production-related predictions.
 Wikipedia: Prediction Market
 Howe (2008): Crowdsourcing: – Why the Power of the Crowd is Driving the Future of Business
 Graefe et al. (2011): Comparing face-to-face meetings, nominal groups, delphi, and prediction markets on an estimation task
 Hubbard (2010): How to Measure Anything: Finding the Value of Intangibles in Business, Second Edition, Chapter 13
 Iowa Electronic Markets (IEM)