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[…] 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
The attacker was probably someone with too much time on his hands —-a prediction market vendor who can’-t sell…- or another idle prediction market person.
Who are the prediction market consultants who took part of the conversation prompted by the publication of the devastating story by The Economist?
– Adam Siegel of Inkling Markets
– Mat Fogarty of CrowdCast
– George Tziralis of AskMarkets
– Jed Christiansen of Mercury
Notably absent from the conversation:
– David Perry of Consensus Point
– “-Chief Scientist”- Robin Hanson of Consensus Point
– Emile Servan-Schreiber of NewsFutures
– Chris Hibbert of Zocalo
– The HSX people
– The academic canaries who are over-quoted by the New York Times and the Wall Street Journal
Taking all this into account, I am updating my “-Consultants”- listing published at CFM. I am putting the consultants who participate in web conversations ahead of the others, and in bold, so as to signal to my numerous readers who they should contact first —-should they have any inquiry about enterprise prediction markets. And I will consider doing the same for the other Midas Oracle listings.
Starting today, there will be retaliations of measured and graduated amplitude against any prediction market people or prediction market company who snobs the important conversations about prediction markets —-which take place on Midas Oracle or elsewhere.
You can’-t be bragging everywhere that you are a “-prediction market expert”- and be absent from important conversations. If you don’-t converse with us, then you are not such a good expert —-”-you’-re the weakest link, bye bye.”-
NEXT: It is not about Midas Oracle…- It is about taking part of the conversation about (enterprise) prediction markets on the Web.
This past week, The Economist wrote on the yet-unfulfilled promise of prediction markets. At CrowdCast (ex-Xpree), we believe prediction markets are not yet mainstream because the current solutions are built on mechanisms designed for the stock market, not for the enterprise.
The stock trading metaphor works for a large, liquid stock market, but is unsuitable for enterprise forecasting. The concept of shorting and covered calls is far from intuitive for your average employee, and the stock mechanism makes it hard to ask the simplest of questions relevant for corporate forecasters. For example, buying or selling a collection of virtual stocks representing probabilities of sales falling in particular ranges is an incredibly obtuse way of asking for a single sales forecast. Finally, the stock mechanism relies on copious liquidity to ensure meaningful metrics, which is often not available with the limited crowds available in the enterprise.
However, innovation moves on and we question the assumption that prediction markets have to rely on the stock market analogy. At CrowdCast, we have been working on a new mechanism, that takes into account participant behavior and aptitude as much as market efficiency. The product we are launching in April will deliver easy, engaging, and expressive information exchanges, without the limitations of traditional notions of stock markets.
When you get the questions, incentives, and mechanism right, a prediction market can be an incredibly powerful management tool. Employees share insights anonymously and are measured and rewarded for their intelligence. Widely deployed, this has the potential to fundamentally change the nature of the organizational contract, moving from information flow based on hierarchy and silos, to enterprise-wide direct communication.
A whole new take on prediction markets- available from CrowdCast in April 2009.
Cross-posted from the Xpree blog
Previously: Are collective intelligence solutions being oversold?
Xpree CEO Mat Fogarty:
Management struggles to understand and plan for the future. When forecasts are inaccurate, corporations incur huge costs due to inventory write-offs, stock-outs, misallocated resources or cost of capital. Collective intelligence delivers objective, accurate forecasts in real time, thus saving many millions of dollars for our corporate clients. The solution is not being oversold, to the contrary, the potential vastly exceeds current awareness and adoption.
“-If foresight is not the whole part of management, at least it is an essential part of it”- (Henri Fayol, 1916).
In my 10 years experience as a management accountant and corporate planner, I have witnessed multiple forecasts suffer from inaccuracy due to uncertainty and biases. Whether forecasting a launch date, sales volume or cost of development, it is the systematic biases due to incentive systems, politics and common cognitive errors that contribute more to inaccuracy than the uncertainty. The problem stems from the fact that the owner of a forecast is normally the owner of the business unit / sales team / project, and budgets and bonuses are based on forecasts. This necessitates game playing and politics and makes the development of an objective, accurate forecast near impossible.
Collective intelligence can overcome these problems by incentivising a diverse crowd of knowledgeable employees to share their insight, balancing the resulting estimates, and rewarding accuracy and timeliness.
However, we are at an early stage in the development of this opportunity. There is still work ahead of us to develop the ideal mechanism to combine simplicy of UI [user interface] with richness of information gathering. In addition, we need to further develop the way collective intelligence interfaces with traditional corporate structures, processes and systems. These are Xpree’-s challenges…- stay tuned.
Mat, congrats for your expansion.
– Will Jim oversell the predictive power of the enterprise prediction markets?
All the other “-business development”- directors I see out there are overselling, in my view.
TV-famous horse racing pundit: John McCririck.
John McCririck endorsing BetFair