–> The sur-excited Wolfy exaggerates the usefulness of the prediction markets.

–> ‘Yay, free markets!… Greed is good!… Yep, people respond to incentives!… Markets work!… Capitalism triumphs.’

In a new study, Daniel Reeves, Duncan Watts, Dave Pennock and I compare the performance of prediction markets to conventional means of forecasting, namely polls and statistical models. Examining thousands of sporting and movie events, we find that the relative advantage of prediction markets is remarkably small. [...]

Given that sports and entertainment markets are among the most mature and successful, our results challenge the view that prediction markets are substantively superior to alternative forecasting mechanisms. Nevertheless, it is certainly possible that there are forecasting applications where either the relative advantage of markets is larger, or that such differences in performance are consequential. Thus, while prediction markets may yet prove to be useful, it would seem the enthusiasm for their predictive prowess has outpaced the evidence.

Prediction Without Markets — by Sharad Goel, Daniel M. Reeves, Duncan J. Watts, David M. Pennock

http://www.cam.cornell.edu/~sharad/papers/pred-wo-markets.pdf

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Robin Hanson’s comment:

Whoever said that every prediction market would always be more accurate than any other mechanism? [*] I’d say they are more-accurate more often than they are less-accurate, compared to mechanisms with similar resources. And your plots at the bottom look like they are testing calibration, not accuracy.

Read Daniel Reeves’s answer to Robin Hanson.

[*] Google, doc.

http://www.chicagotribune.com/business/yourmoney/chi-0704160447apr17,0,2547860.column?coll=chi-business-hed

Prof, you were 100% wrong.

Prediction markets on which country will host the Olympics have never worked.

- BetFair’s event derivative prices (on the far right of the chart, you can see that the price went down to zero):

- InTrade’s event derivative prices (on the far right of the chart, you can see that the price went down to zero):

- HubDub’s event derivative prices:

Who will recieve the winning bid to host the 2016 Olympics?

- Here’s what happened to the Paris 2012 event derivative:

P.S.: For those who don’t follow Midas Oracle on a daily basis (how dare), Google Caffeine is the future Google Search.

UPDATE: The Inkling Markets website goes on page 2, on Caffeine. Good.

UPDATE: Google Caffeine is off for a few hours.

This BetFair video totally misinforms the public about the history of the betting exchange industry. It actually started in the United States of America, with the Iowa Electronic Markets in 1988, the ForeSight Exchange in 1994, and the Hollywood Stock Exchange in 1996.

Go reading more research papers, Erik.

Swine Influenza Prediction Markets

I like how they use icons, and the way they display probabilities on that page. It is clear, and we can get the info real quick.

Emile tells me that IHPM was developed with NewsFutures source code, but customized by them.

[...] Prediction markets are gaining interest because the Internet allows greater worldwide access to them, as well as to the ever-increasing amount of data stored on any topic imaginable (which theoretically allows participants to make more informed predictions, individually and in aggregate). These factors, plus the enormous amount of computing power that will make it possible to instantly calculate exponentially small odds, are stimulating new research on advanced computational models in prediction markets. These models could be capable of analyzing entire events such as the annual NCAA collegiate basketball tournament, which begins a 63-game schedule with 2⁶³ possible outcomes by the tournament’s end. [...]

Growing opportunities in internal private-sector prediction markets are also revealing divergent philosophies among the markets’ designers. Many of the public markets feature price-adjustment algorithms built around answering discrete multiple-choice outcomes, such as which candidate will win an election or if a product will launch in month x, y, or z. [...]

IEM steering committee member Thomas Rietz, a professor of finance at the university, says the aggregate zero-risk design of the IEM allows the markets to perfectly reflect the aggregate forecast opinions of its participants. By aggregate zero-risk, Rietz explains that when a trader enters a particular bilateral (either/or) market, he or she must buy one share of each choice, called a bundle, for a total cost of $1. If the trader holds the bundle until the market concludes, there is neither profit nor gain. If the trader guesses the outcome successfully, and sells the losing unit of the bundle to another trader while the market is running, he or she picks up the original $1 bet plus whatever price was agreed upon for the losing share that was sold. If the trader chooses to hold onto the loser and sell the eventual winner, however, they will incur the $1 loss at payout time. At any given time, the number of eventual winning shares and losing shares is equal and held by the traders. So, the university bears no counterparty risk and there is no need to provide hedging margins that irrationally affect outcomes. “The price you would be willing to buy or sell for today is your expectation of its value in the future—the prices can be directly interpreted as a forecast,” Rietz says. “In ordinary futures markets, there is a long-lasting debate, going back to John Maynard Keynes in the 1930s, over whether prices can legitimately be used as forecasts, and it all hinges on whether or not people demand a return or face a risk in aggregate when they’re investing in these contracts.” [...]

One enduring research problem on combinatorial markets is mitigating the effects a virtually unlimited spectrum of outcomes will have on creating markets that are so thin in trades they do not serve their purpose of aggregating information. In such markets, which might bear a resemblance to an enterprise prediction market in that there are not enough participants to provide a statistically valid spread of opinion, Pennock says a market-maker algorithm might serve as a price setter within widely acceptable limits. “I believe that approximation algorithms will be fine for the market maker, because people don’t really care about making bets on things that are incredibly unlikely, like 10−6 chance,” Pennock says. “But as long as you’re betting on something with a 10% chance of happening, we’ll be able to approximate pretty quickly with a market-maker price.” [...]

David Pennock’s website and blog

I disagree.

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