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Prediction markets (also known as predictive markets, information markets, decision markets, idea futures, event derivatives, or virtual markets) are exchange-traded markets created for the purpose of trading the outcome of events. The market prices can indicate what the crowd thinks the probability of the event is. A prediction market contract trades between 0 and 100%. It is a binary option that will expire at the price of 0 or 100%. Prediction markets can be thought of as belonging to the more general concept of crowdsourcing which is specially designed to aggregate information on particular topics of interest. The main purposes of prediction markets are eliciting aggregating beliefs over an unknown future outcome. Traders with different beliefs trade on contracts whose payoffs are related to the unknown future outcome and the market prices of the contracts are considered as the aggregated belief.
- 1 History
- 2 Accuracy
- 3 Other issues
- 4 Applications of prediction markets
- 5 Specific types of prediction markets
- 6 See also
- 7 References
- 8 Sources
- 9 External links
Before the era of scientific polling, early forms of prediction markets often existed in the form of political betting. One such political bet dates back to 1503, in which people bet on who will be the papal successor. Even then, it was already considered “an old practice”. According to Paul Rhode and Koleman Strumpf, who has researched the history of prediction markets, there are records of election betting in Wall Street dating back to 1884. One study estimates that average betting turnover per US presidential election is equivalent to over 50 percent of the campaign spend.
Economic theory for the ideas behind prediction markets can be credited to Friedrich Hayek in his 1945 article "The Use of Knowledge in Society" and Ludwig von Mises in his "Economic Calculation in the Socialist Commonwealth". Modern economists agree that Mises' argument combined with Hayek's elaboration of it, is correct. Prediction markets are championed in James Surowiecki's 2004 book The Wisdom of Crowds, Cass Sunstein's 2006 Infotopia, and How to Measure Anything: Finding the Value of Intangibles in Business by Douglas Hubbard. The research literature is collected together in the peer reviewed The Journal of Prediction Markets, edited by Leighton Vaughan Williams and published by the University of Buckingham Press.
Milestones in development of modern electronic prediction markets
- One of the first modern electronic prediction markets is the University of Iowa's Iowa Electronic Markets, introduced during the 1988 US presidential election.
- Around 1990 at Project Xanadu, Robin Hanson used the first known corporate prediction market. Employees used it in order to bet on, for example, the cold fusion controversy.
- HedgeStreet, designated in 1991 as a market and regulated by the Commodity Futures Trading Commission, enables Internet traders to speculate on economic events.
- The Hollywood Stock Exchange, a virtual market game established in 1996 and now a division of Cantor Fitzgerald, LP, in which players buy and sell prediction shares of movies, actors, directors, and film-related options, correctly predicted 32 of 2006's 39 big-category Oscar nominees and 7 out of 8 top category winners.
- In 2001, Intrade.com launched a prediction market trading platform from Ireland allowing real money trading between members on contracts related to a number of different categories including business issues, current events, financial topics, and more. Intrade ceased trading in 2013.
- In July 2003, the U.S. Department of Defense publicized a Policy Analysis Market and on their website, and speculated that additional topics for markets might include terrorist attacks. A critical backlash quickly denounced the program as a "terrorism futures market" and the Pentagon hastily canceled the program.
- In 2005, scientific monthly journal Nature stated how major pharmaceutical company Eli Lilly and Company used prediction markets to help predict which development drugs might have the best chance of advancing through clinical trials, by using internal markets to forecast outcomes of drug research and development efforts.
- Also in 2005, Google Inc announced that it has been using prediction markets to forecast product launch dates, new office openings, and many other things of strategic importance. Other companies such as HP and Microsoft also conduct private markets for statistical forecasts.
- In October 2007 companies from the United States, Ireland, Austria, Germany, and Denmark formed the Prediction Market Industry Association, tasked with promoting awareness, education, and validation for prediction markets. The current status of the association appears to be defunct.
The ability of the prediction market to aggregate information and make accurate predictions is based on the Efficient Market Hypothesis, which states that assets prices are fully reflecting all available information. For instance, existing share prices always include all the relevant related information for the stock market to make accurate predictions.
Surowiecki raises three necessary conditions for collective wisdom: diversity of information, independence of decision, decentralization of organization. In the case of predictive market, each participant normally has diversified information from others and makes their decision independently. The market itself has a character of decentralization compared to expertise decisions. Because of these reasons, predictive market is generally a valuable source to capture collective wisdom and make accurate predictions.
Prediction markets have an advantage over other forms of forecasts due to the following characteristics. Firstly, they can efficiently aggregate a plethora of information, beliefs, and data. Next, they obtain truthful and relevant information through financial and other forms of incentives. Prediction markets can incorporate new information quickly and are difficult to manipulate.
The accuracy of the prediction market in different conditions has been studied and proven by numerous researchers.
- Steven Gjerstad (Purdue) in his paper "Risk Aversion, Beliefs, and Prediction Market Equilibrium", has shown that prediction market prices are very close to the mean belief of market participants if the agents are risk averse and the distribution of beliefs is spread out (as with a normal distribution, for example).
- Justin Wolfers (Wharton) and Eric Zitzewitz (Dartmouth) have obtained similar results to Gjerstad’s conclusions in their paper "Interpreting Prediction Market Prices as Probabilities". In practice, the prices of binary prediction markets have proven to be closely related to actual frequencies of events in the real world.
- Douglas Hubbard has also conducted a sample of over 400 retired claims which showed that the probability of an event is close to its market price but, more importantly, significantly closer than the average single subjective estimate. However, he also shows that this benefit is partly offset if individuals first undergo calibrated probability assessment training so that they are good at assessing odds subjectively. The key benefit of the market, Hubbard claims, is that it mostly adjusts for uncalibrated estimates and, at the same time, incentivizes market participants to seek further information.
- Lionel Page and Robert Clemen have looked at the quality of predictions for events taking place some time in the future. They found that predictions are very good when the event predicted is close in time. For events which take place further in time (e.g. elections in more than a year), prices are biased towards 50%. This bias comes the traders' "time preferences" (their preferences not to lock their funds for a long time in assets).
Due to the accuracy of the prediction market, it has been applied to different industries to make important decisions. Some examples include:
- Prediction market can be utilized to improve forecast and has a potential application to test lab-based information theories based on its feature of information aggregation. Researchers have applied prediction markets to assess unobservable information in Google’s IPO valuation ahead of time.
- In healthcare, predictive markets can help forecast the spread of infectious disease. In a pilot study, a statewide influenza in Iowa was predicted by these markets 2–4 weeks in advance with clinical data volunteered from participating health care workers.
- Some corporations have harnessed internal predictive markets for decisions and forecasts. In these cases, employees can use virtual currency to bet on what they think will happen for this company in the future. The most accurate guesser will win a money prize as payoff. For example, Best Buy once experimented on using the predictive market to predict whether a Shanghai store can be open on time. The virtual dollar drop in the market successfully forecasted the lateness of the business and prevented the company from extra money loss.
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 1945 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”.
One way the prediction market gathers information is through James Surowiecki’s phrase, “The Wisdom of Crowds,” in which a group of people with a sufficiently broad range of opinions can collectively be cleverer than any individual. 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. Due to this lack of knowledge, the crowd’s answers can sometimes be very wrong.
The second market mechanism is the idea of the marginal-trader hypothesis. According to this theory, “there will always be individuals seeking out places where the crowd is wrong”. These individuals, in a way, put the prediction market back on track when the crowd fails and values could be skewed.
In early 2017, 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.e. liquidity or other factors not intended to be measured are taken into account as risk factors by the market participants, distorting the market probabilities. Prediction markets may also be subject to speculative bubbles. For example, in the year 2000 IEM presidential futures markets, seeming "inaccuracy" comes from buying that occurred on or after Election Day, 11/7/00, but, by then, the trend was clear.
There can also be direct attempts to manipulate such markets. In the Tradesports 2004 presidential markets there was an apparent manipulation effort. An anonymous trader sold short so many Bush 2004 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" (2005), 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.
On Thursday, June 23, 2016, the United Kingdom voted to leave the European Union. 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. Clouded by the similar mindset of users in prediction markets, they created a paradoxical environment where they began self-reinforcing their initial beliefs (in this case, that the UK would vote to remain in the EU). Here, we can observe how crippling bias and lack of diversity of opinion can be in the success of a prediction market.
Similarly, during the 2016 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. Koleman Strumpf, a University of Kansas professor of business economics, also suggests that a bias effect took place during the US Elections; the crowd was unwilling to believe in an outcome with Trump winning and caused the prediction markets to turn into “an echo chamber”, where the same information circulated and ultimately lead to a stagnant market.
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": they are free to play (no purchase necessary) and usually offer prizes to the best traders as incentives to participate. 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. For example, a market predicting the death of a world leader might be quite useful for those whose activities are strongly related to this leader's policies, but it also might turn into an assassination market. The incentives involved in practical markets are so low, however, that this is extremely unlikely.
Applications of prediction markets
There are a number of commercial and academic prediction markets operating publicly.
Public prediction markets
- The Iowa Electronic Markets is an academic market examining elections where positions are limited to $500.
- PredictIt is a prediction market for political and financial events.
- SciCast was a combinatorial prediction market that focuses on science and technology forecasting.
- Smarkets is a prediction markets for sporting events.
- FameProject.org is a prediction market focused on pop culture events and news.
- FAZ.NET-Orakel is a prediction Market in Germany, launched in March 2017.
- ClimatePredictionMarket.com is a prediction market developed by Winton Group to form a consensus on climate change, launched in September 2017.
- Augur_(software) is a decentralized oracle and prediction market platform built on the Ethereum blockchain.
- iPredict was a prediction market in New Zealand.
- Microsoft launched a Prediction Lab that for the 2014 US Elections.
- Prediction markets using potential buyers of products are used to test new product concepts and advertising materials through platforms such as Huunu (Consensus Point) and Conjoint.ly.
Internal use by corporations
- The simExchange introduced a perpetual contract that it calls "stocks" to predict the global, lifetime sales of video game consoles and software titles. These stocks do not expire like most contracts on prediction markets because the founder, Brian Shiau, argued that video game sales can continue for years. The premise for these stocks is that Shiau believes the video game industry suffers from a "lack of comprehensive sales data" and he compares the information problem of a game's sales to the information problem of evaluating a company's market value. Hanson warns that such a system may not work if a connection is not enforced. Keith Gamble has described the simExchange as a Keynesian beauty contest and that financial markets have certain remedies such as company buy-outs that cannot happen on the simExchange. Gamble concludes that such a prediction market can work but will be confined to play money.
- Best Buy, Motorola, Qualcomm, Edmunds.com, and Misys Banking Systems are listed as Consensus Point clients.
- Hewlett-Packard pioneered applications in sales forecasting and now uses prediction markets in several business units. Mentioned in academic publications from HP Labs. Also mentioned in Newsweek. It is working towards a commercial launch of the implementation as a product, BRAIN (Behaviorally Robust Aggregation of Information Networks).
- Corning, Renault, Eli Lilly, Pfizer, Siemens, Masterfoods, Arcelor Mittal and other global companies are listed as NewsFutures customers.
- Intel is mentioned in Harvard Business Review (April 2004) in relation to managing manufacturing capacity.
- Microsoft is piloting prediction markets internally.
- France Telecom's Project Destiny has been in use since mid-2004 with demonstrated success.
- Google has confirmed in its official blog that it uses a predictive market internally.
- Novozymes applied prediction markets to an internal innovation contest that had the goal of identifying discontinuous product ideas. Besides accomplishing this goal, the initiative was successful in recombining ideas that had already been proposed by employees, but then ignored; it also supported R&D managers' evaluation by highlighting features of ideas otherwise overlooked.
- The Wall Street Journal reported that General Electric uses prediction market software from Consensus Point to generate new business ideas.
- BusinessWeek lists MGM and Lionsgate Studios as two HSX clients.
- HSX built and operated a televised virtual stock market, the Interactive Music Exchange for Fuse Networks Fuse TV to be used as the basis of their daily live television broadcast, IMX, which ran from January, 2003 through July, 2004. The television audience traded virtual stocks of artists/videos/songs, and predicted which would make it to the top of the Billboard music charts. The first of its kind, Fuse Network and HSX won an AFI Enhanced TV (American Film Institute) Award for innoviation in television interactivity.
- Starwood embraced the use of prediction markets for developing and selecting marketing campaigns. Marketing department started out with some initial ideas and allowed employees to add new ideas or make changes to existing ones. Then subsequently incentives based prediction markets were leveraged to select the best of the lot.
Specific types of prediction markets
Combinatorial prediction markets
A combinatorial prediction market is a type of prediction market where participants can make bets on combinations of outcomes. The advantage of making bets on combinations of outcomes is that, in theory, conditional information can be better incorporated into the market price.
One difficulty of combinatorial prediction markets is that the number of possible combinatorial trades scales exponentially with the number of normal trades. For example, a market with merely 100 binary contracts would have 2^100 possible combinations of contracts. 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.
Decentralized prediction markets
Since 2012, decentralized platforms for prediction markets have been in development. These platforms utilize blockchain technology and cryptocurrencies to provide various advantages over centralized markets, but also more challenges for regulators. A decentralized platform for a prediction markets would be driven by the Colored Coins Concept.
Some advantages of decentralized prediction markets are as follows:
- A centralized well known arbiter may be pressured to resolve an issue incorrectly. An anonymous centralized arbiter is untrustworthy. A decentralized automatic arbiter removes this threat.
- Removing arbiters removes the threat of an arbiter ceasing to exist when the bet is resolved. This threat is quite relevant for contracts with no explicit end date or those which resolve in the distant future.
- A prediction market requires a market maker. Market makers typically want to be compensated for their services. By removing a centralized arbiter the fees associated with facilitating transactions are reduced or eliminated. Lower transaction costs attract more participants in the prediction market which should statistically lead to a more accurate mean prediction off the overall population.
Some risks associated with decentralized prediction markets are as follows:
- Outcomes such as terrorist attacks or behavior with negative externalities could be promoted through opening of decentralized predictive markets. For example one could bet on someone’s death and then facilitate it using an assassination market.
- Finally, the anonymity associated with prediction markets allows for untraceable insider trading. The goalie on a soccer team may bet against his team and purposely throw the game.
- Election Stock Market
- Futures exchange
- Policy Analysis Market
- Prediction games
- Betting exchange
- Binary option
- "Prediction Market". Investopedia.
- Rhode, Paul; Strumpf, Koleman (2008). "Historical Election Betting Markets: An International Perspective" (PDF). Perspectives on Politics.
- Rhode, Paul; Strumpf, Koleman (2004). "Historical Presidential Betting Markets" (PDF). Journal of Economic Perspectives. 18 (2): 127–142. doi:10.1257/0895330041371277.
- "US election guide to prediction markets and bets". Financial Times. Retrieved 2017-02-03.
- "Biography of Ludwig Edler von Mises (1881–1973)", The Concise Encyclopedia of Economics
- Douglas Hubbard "How to Measure Anything: Finding the Value of Intangibles in Business" John Wiley & Sons, 2007
- Stanley W. Angrist (28 August 1995). "Iowa Market Takes Stock of Presidential Candidates (Reprinted with Permission of THE WALL STREET JOURNAL)". The University of Iowa, Henry B. Tippie College of Business. Archived from the original on 30 November 2012. Retrieved 7 November 2012.
- Polgreen, P. M.; Nelson, F. D.; Neumann, G. R.; Weinstein, R. A. (2007-01-15). "Use of Prediction Markets to Forecast Infectious Disease Activity". Clinical Infectious Diseases. 44 (2): 272–279. doi:10.1086/510427. ISSN 1058-4838. PMID 17173231.
- "Using Prediction Markets to Enhance US Intelligence Capabilities — Central Intelligence Agency". www.cia.gov. Retrieved 2017-02-03.
- "PMIA – Come to Know". www.cometoknow.com.
- Surowiecki, James (2005). The Wisdom of Crowds. New York: Anchor Books.
- Ozimek, Adam (2014). "The Regulation and Value of Prediction Markets" (PDF). https://www.mercatus.org/system/files/Ozimek_PredictionMarkets_v1.pdf. External link in
- Steven Gjerstad. ""Risk Aversion, Beliefs, and Prediction Market Equilibrium""(PDF). Econ.arizona.edu. Archived from the original (PDF) on 12 April 2014. Retrieved 2016-08-20.
- Justin Wolfers; Eric Zitzewitz. ""Interpreting Prediction Market Prices as Probabilities"" (PDF). Bpp.wharton.upenn.edu. Archived from the original (PDF)on 12 November 2012. Retrieved 2016-08-20.
- Pennock, David M.; Lawrence, Steve; Giles, C. Lee; Årup Nielsen, Finn (2001). "The real power of artificial markets". Science. 291 (5506): 987–988. doi:10.1126/science.291.5506.987. PMID 11232583.
- Berg, Joyce (2007). "Searching for Google's Value: Using Prediction Markets to Forecast Market Capitalization Prior to an Initial Public Offering" (PDF).
- Polgreen, Philip M.; Nelson, Forrest D.; Neumann, George R. (2007-01-15). "Use of prediction markets to forecast infectious disease activity". Clinical Infectious Diseases. 44 (2): 272–279. doi:10.1086/510427. ISSN 1537-6591. PMID 17173231.
- Lohr, Steve (2008-04-09). "Betting to Improve the Odds". The New York Times. ISSN 0362-4331. Retrieved 2017-02-03.
- Mann, Adam. "Market Forecasts." Nature 538 (2017): 308-10. Web. 3 Feb. 2017.
- O'Grady, Cathleen. "Crowds Are Wise Enough to Know When Other People Will Get It Wrong." Ars Technica. Conde Nast, 28 Jan. 2017. Web. 03 Feb. 2017.
- Dizikes, Peter. "Better Wisdom from Crowds." MIT News. MIT News Office, 25 Jan. 2017. Web. 03 Feb. 2017.
- "manipulation2.dvi" (PDF). Hanson.gmu.edu. Retrieved 2016-08-20.
- "Archived copy". Archived from the original on 20 April 2008. Retrieved 2008-10-05.
- Levingston, Ivan (2016-07-28). "Why political polls and betting odds disagree with each other so much". CNBC. Retrieved 2017-02-03.
- "Who said Brexit was a surprise?". The Economist. 2016-06-24. ISSN 0013-0613. Retrieved 2017-02-03.
- Gelman, Andrew; Rothschild, David (2016-07-12). "Something's Odd About the Political Betting Markets". Slate. ISSN 1091-2339. Retrieved 2017-02-03.
- "Like polls, prediction markets failed to see Trump's victory coming, economist says". The University of Kansas. Retrieved 2017-02-03.
- Katy Bachman (2014-10-31). "Meet the 'stock market' for politics". Politico. Retrieved 2015-01-25.
- a scenario described by Jim Bell in 1997. Bell, Jim (1997-04-03). "Assassination Politics" (PDF). Infowar. Archived (PDF) from the original on 27 January 2011. Retrieved February 28, 2011.
- Fortado, Lindsay (12 September 2017). "Winton Capital sets up climate change prediction market". Financial Times. Retrieved 31 January 2018.
- "the simExchange - The structure of simExchange game stocks". thesimexchange.com.
- "Robin Hanson on the Sim Exchange". Midas Oracle.ORG - Predictions & Innovation.
- "simExchange a Keynesian Beauty Contest". Midas Oracle.ORG - Predictions & Innovation.
- "Keith Jacks Gamble: simExchange is somewhat OK, but will remained confined in play-money land". Midas Oracle.ORG - Predictions & Innovation.
- "Prediction Markets - Real Time Intelligence - Concept Testing". Consensus Point.
- "Archived copy". Archived from the original on 7 September 2004. Retrieved 2004-09-21.
- "Archived copy". Archived from the original on 13 June 2008. Retrieved 2007-04-04.
- "Archived copy". Archived from the original on 8 October 2014. Retrieved 6 October 2014.
- Robert Charette (28 February 2007). "An Internal Futures Market". BI Review Magazine.
- "Official Google Blog: Putting crowd wisdom to work". Official Google Blog.
- "Using Prediction Markets to Track Information Flows: Evidence from Google" (PDF). Bocowgill.com. Retrieved 2016-08-20.
- Lauto, Giancarlo; Valentin, Finn (2016). "How preference markets assist new product idea screening". Industrial Management & Data Systems. 116: 603–619. doi:10.1108/IMDS-07-2015-0320.
- Lauto, Giancarlo; Valentin, Finn; Hatzack, Frank; Carlsen, Maria (2013). "Managing front-end innovation through idea markets at Novozymes". Research-Technology Management. 56: 17–26. doi:10.5437/08956308X5604126.
- "Prediction Market - Market Research - Concept Testing - Crowdsourcing". Consensus Point.
- Totty, Michael (2006-06-19). "Business Solutions - WSJ". Online.wsj.com. Retrieved 2016-08-20.
- "Archived copy". Archived from the original on 22 August 2006. Retrieved 2006-10-30.
- "Archived copy". Archived from the original on 8 May 2007. Retrieved 2007-02-21.
- Hanson, Robin (January 2003). "Combinatorial Information Market Design" (PDF). Information Systems Frontiers. 5 (1): 107–119. doi:10.1023/A:1022058209073.
- Sun, Wei; Hanson, Robin; Laskey, Kathryn; Twardy, Charles (16 Oct 2012). "Probability and Asset Updating using Bayesian Networks for Combinatorial Prediction Markets". Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012). arXiv: . Bibcode:2012arXiv1210.4900S.
- Sun, Wei; Laskey, Kathryn; Twardy, Charles; Hanson, Robin; Goldfedder, Brandon. "Trade-based Asset Model using Dynamic Junction Tree for Combinatorial Prediction Markets". arXiv: . Bibcode:2014arXiv1406.7583S.
- Lott, Maxim (2015-08-20). "New tech promises government-proof prediction markets | Fox News". Retrieved 2016-07-29.
- Academic papers
- Bell, Tom W. Prediction Markets For Promoting the Progress of Science and the Useful Arts - PDF file - George Mason Law Review (14 Geo. Mason L. Rev 37) (2006)
- Berg, Joyce E., & Thomas A. Rietz. The Iowa Electronic Market: Lessons Learned and Answers Yearned - PDF file - 2005-01-00
- Erikson, Robert S., & Christopher Wlezien. "Are Political Markets Really Superior to Polls as Election Predictors?" Public Opinion Quarterly 72(2), Summer 2008, pp. 190–215.
- Gjerstad, Steven. "Risk Aversion, Beliefs, and Prediction Market Equilibrium," University of Arizona Working Paper 04-17, 2005.
- Graefe, A.; Armstrong, J.S. (2011). "Comparing face-to-face meetings, nominal groups, Delphi and prediction markets on an estimation task". International Journal of Forecasting. 27 (1): 183–195. doi:10.1016/j.ijforecast.2010.05.004.
- Gruca, Thomas S.; Berg, Joyce E.; Cipriano, Michael (2005). "Consensus and Differences of Opinion in Electronic Prediction Markets". Electronic Markets. 15 (1): 13–22. doi:10.1080/10196780500034939.
- Hanson, Robin. The Informed Press Favored the Policy Analysis Market - PDF file - 2005-05-05
- Manski, Charles F. Interpreting the Predictions of Prediction Markets - PDF file - Revised Aug 2005—Manski suggests that there needs to be a better theoretic basis for interpreting market prices as probability, and provides a simple model for this.
- Rhode, Paul; Strumpf, Koleman (2004). "Historical Presidential Betting Markets" (PDF). Journal of Economic Perspectives. 18 (2): 127–142. doi:10.1257/0895330041371277. Provides a detailed history of political prediction markets in the US, and shows early markets in the 19th and early 20th Centuries provided accurate forecasts and satisfied market efficiency.
- Rosenbloom, E. S.; Notz, William (2006). "Statistical Tests of Real-Money versus Play-Money Prediction Markets". Electronic Markets. 16 (1): 63–69. doi:10.1080/10196780500491303.
- Servan-Schreiber, Emile; Wolfers, Justin; Pennock, David M.; Galebach, Brian (2004). "Prediction Markets: Does Money Matter?". Electronic Markets. 14 (3): 243–251. doi:10.1080/1019678042000245254.
- Spann, Martin & Skiera, Bernd."Internet-Based Virtual Stock Markets for Business Forecasting" - PDF file - Discusses theory, design options and presents empirical comparisons on forecasting accuracy of prediction markets
- Wolfers, Justin, & Eric Zitzewitz. Prediction Markets - PDF file - 2004-05-00
- Wolfers, Justin, & Eric Zitzewitz.Interpreting Prediction Market Prices as Probabilities - PDF file - Draft version 2007-01-08 - Expands on the work of Manski, providing a more general model wherein it is somewhat rational to interpret market prices as probabilities
- Watkins, Jennifer H.Prediction Markets as an Aggregation Mechanism for Collective Intelligence - Proceedings of 2007 UCLA Lake Arrowhead Human Complex Systems Conference, Lake Arrowhead, CA, 25–29 April 2007.
- Storkey, A.J. Machine Learning Markets - Journal of Machine Learning Research C&WP 15:AISTATS. 2011.
- Storkey A.J., Millin, J., Geras, K. Isoelastic agents and wealth updates in machine learning markets - International Conference in Machine Learning. 2012.
- Video of Robin Hanson's Combinatorial Prediction Markets lecture at the 'Uncertainty in Artificial Intelligence' conference in Helsinki, 2008