“-Our market maker automatically adjusts its level of liquidity depending on trading volume. Prices start off very responsive and, as volume increases, liquidity grows, obviating the need to somehow guess the ‘-right’- level before trading even starts.”-
For your 6 y/o.
Are our betting exchanges’- APIs programmable?
That is from our good friend Emile Servan-Schreiber of NewsFutures, who is participating in the discussion on the LinkedIn group on prediction markets.
Go read his comment.
Dear Mr. [PROFESSOR’S NAME GOES HERE],
I am writing on behalf of [PREDICTION MARKET SOFTWARE VENDOR’S NAME], a Netherlands based consulting firm, because our research has identified you as a scholar with some expertise in Prediction Markets or a related field. [THEY JUST SCRAPPED MIDAS ORACLE’S LISTINGS] We would like to assess your suitability and interest to join the panel of scientific advisors with [PREDICTION MARKET SOFTWARE VENDOR’S NAME] (www.url.com).
In doing so, we propose to explore your area of experience and knowledge of Prediction Markets. Having established this, we would like to list you as our scientific advisor on your area of expertise on Prediction Markets. This will allow us to seek your services for a negotiated fee, once your particular expertise in developing or interpreting a Prediction Market issue is required.
I trust this message clarifies in short the mutual benefits in our cooperation. Please feel free to contact me to discuss further details.
[LOW-LEVEL EMPLOYEE’S NAME GOES HERE]
[PREDICTION MARKET SOFTWARE VENDOR’S NAME]
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?
New prediction market software (and blog)
Best wishes to them.
I’-ve developed a combinatorial betting tech that lets a few or many users edit an always-coherent joint probability distribution over all value combinations of some set of base variables. Far futures base variables might include the years of important tech milestones, population, wealth, or mortality values at particular future dates, etc. Each user edit would be backed by a bet, a bet invested in assets paying competitive interest/returns. This combo bet tech worked well in published lab tests, several firms have used it, and I’-m now working with Consensus Point to deliver a robust commercial implementation. More on the tech here, here, and here.
See the explainer from David Pennock, which we will link to, again, later on.