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Although prediction markets are often fairly accurate and successful, there are many times the market fails in making the right prediction or making one at all.
Based mostly on an idea in by Austrian economist Friedrich Hayek , prediction markets are "mechanisms for collecting vast amounts of information held by individuals and synthesizing it into a useful data point".
However, this information gathering technique can also lead to the failure of the prediction market. Oftentimes, the people in these crowds are skewed in their independent judgements due to peer pressure, panic, bias, and other breakdowns developed out of a lack of diversity of opinion.
One of the main constraints and limits of the wisdom of crowds is that some prediction questions require specialized knowledge that majority of people do not have.
The second market mechanism is the idea of the marginal-trader hypothesis. In early , researchers at MIT developed the "surprisingly popular" algorithm to help improve answer accuracy from large crowds.
The method is built off the idea of taking confidence into account when evaluating the accuracy of an answer.
The method asks people two things for each question: What they think the right answer is, and what they think popular opinion will be. The variation between the two aggregate responses indicates the correct answer.
The effects of manipulation and biases are also internal challenges prediction markets need to deal with, i.
Prediction markets may also be subject to speculative bubbles. There can also be direct attempts to manipulate such markets.
In the Tradesports presidential markets there was an apparent manipulation effort. An anonymous trader sold short so many Bush presidential futures contracts that the price was driven to zero, implying a zero percent chance that Bush would win.
The only rational purpose of such a trade would be an attempt to manipulate the market in a strategy called a " bear raid ". If this was a deliberate manipulation effort it failed, however, as the price of the contract rebounded rapidly to its previous level.
As more press attention is paid to prediction markets, it is likely that more groups will be motivated to manipulate them. However, in practice, such attempts at manipulation have always proven to be very short lived.
In their paper entitled "Information Aggregation and Manipulation in an Experimental Market" ,  Hanson, Oprea and Porter George Mason U , show how attempts at market manipulation can in fact end up increasing the accuracy of the market because they provide that much more profit incentive to bet against the manipulator.
Using real-money prediction market contracts as a form of insurance can also affect the price of the contract. For example, if the election of a leader is perceived as negatively impacting the economy, traders may buy shares of that leader being elected, as a hedge.
These prediction market inaccuracies were especially prevalent during Brexit and the US Presidential Elections. Even until the moment votes were counted, prediction markets leaned heavily on the side of staying in the EU and failed to predict the outcomes of the vote.
According to Michael Traugott , a former president of the American Association for Public Opinion Research , the reason for the failure of the prediction markets is due to the influence of manipulation and bias shadowed by mass opinion and public opinion.
Similarly, during the US Presidential Elections, prediction markets failed to predict the outcome, throwing the world into mass shock.
Like the Brexit case, information traders were caught in an infinite loop of self-reinforcement once initial odds were measured, leading traders to "use the current prediction odds as an anchor" and seemingly discounting incoming prediction odds completely.
Because online gambling is outlawed in the United States through federal laws and many state laws as well, most prediction markets that target US users operate with "play money" rather than "real money": Notable exceptions are the Iowa Electronic Markets , which is operated by the University of Iowa under the cover of a no-action letter from the Commodity Futures Trading Commission , and PredictIt , which is operated by Victoria University of Wellington under cover of a similar no-action letter.
Some kinds of prediction markets may create controversial incentives. A combinatorial prediction market is a type of prediction market where participants can make bets on combinations of outcomes.
One difficulty of combinatorial prediction markets is that the number of possible combinatorial trades scales exponentially with the number of normal trades.
These exponentially large data structures can be too large for a computer to keep track of, so there have been efforts to develop algorithms and rules to make the data more tractable.
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