Download this post to watch the video if your feed reader does not show it to you.

Download this post to watch the video —if your feed reader does not show it to you.

External Links:

- CrowdCast

- CFO Prediction Market

- Matt Fogarty’s twitter – [Midas Oracle's twitter]

Previously:

- CrowdCast = Collective Forecasting = Collective Intelligence That Predicts

- CrowdCast = market mechanism = binary spreads with a market maker

Mat Fogarty:

Previously: CrowdCast = Collective Forecasting = Collective Intelligence That Predicts

CrowdCast

The Promise of Prediction Markets [*] #e2conf33

When will the product really ship? How much will we sell next year? While accurate forecasts are a cornerstone of a well-run enterprise, the methods to arrive at those forecasts haven’t changed in decades. Prediction markets offer the ability to tap the wisdom of the corporate crowd to forecast key metrics. While wikis, blogs, and tagging have become quite accepted as workplace tools, prediction markets have suffered from a level of complexity that has stifled widespread adoption. In this talk, the audience will learn more about prediction markets and how they bring value to corporate forecasters (case studies from Electronic Arts and Johnson and Johnson).

Speaker – Leslie Fine, Chief Scientist, CrowdCast

Leslie leads the design of the Crowdcast prediction engines as well as client customization. Prior to joining Crowdcast, she spent 8 years at HP Labs designing and implementing BRAIN, HP’s prediction system. Prior to joining HP, Leslie received her PhD from Caltech in Economics, where she studied mechanism design, particularly inducing liquidity in thin markets and information market design.

Speaker – Matthew Fogarty, CEO, CrowdCast

[*] The promise of… collective forecasting, rather —since CrowdCast has abandoned the market technology.

CrowdCast is on Twitter. — Looks “cool” (as would say Bo “Mister R” Cowgill, who has a binary vocabulary set, “cool” and “lame”). I like the logo. I am following them, and you should do that too.

CrowdCast is on FaceBook. — I am not a huge user of FaceBook, but I do check it twice a week to accept invites, and manage my group memberships. I have just joined the CrowdCast Fan Club, and you should do too.

Do they have a LinkedIn page for CrowdCast? They should. It is the latest. I like LinkedIn.

I can spot Mat Fogarty and Leslie Fine… Who the hell are the other people?…

Here is the original picture:

CrowdCast website

They will have to update their website, as soon as their brand-new collective forecasting solution package comes out of the lab. Their Internet site should be the nuclear core of their marketing.

- “Mat is a funny, friendly and great person to work with. He’s highly entrepreneurial and innovative business leader, with passion and drive. I’d recommend Crowdcast to anyone – they know how to create best-of-breed online software products.” April 3, 2009

Joe Miles, General Manager, efp Markets, FC Group Ltd

- “Mat and I worked on numerous presentations and analyses for EA’s CFO, CEO and Board of Directors. Mat is not only detail-oriented but he also thinks strategically. Most importantly, I appreciate Mat as a person with high integrity.” September 4, 2007

James Lee, Investor Relations Manager, Electronic Arts

- “Mat is a great strategic thinker basing his decisions on the facts and cutting through the nonsense. Unlike many managers he also has the ability to execute on his strategies.” August 30, 2007

Eric Kress, Business Analyst, Electronic Arts

Awesome.

Stupidly, Adam Siegel and Emile Servan-Schreiber (Mat’s 2 main competitors) have not asked others to publish testimonies. They should wake up from their deep napping —before it is too late (that is, before CrowdCast becomes the de facto leader of the EPM software vendors).

Chris Hibbert (of Zocalo) has one great recommendation from one MIT-involved guy.

[...] 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.

- Adam Siegel of Inkling Markets

- Mat Fogarty of CrowdCast

- George Tziralis of AskMarkets

- Jed Christiansen of Mercury