Leslie Fine (CrowdCast Chief Scientist) to me:
Actually, our mechanism is a market, it’s just not a stock market. We use an automated market maker to efficiently price every bet, adjust crowd beliefs, and price an interim sell. In essence, participants trade binary spreads with the market maker.
Because our new version was not yet market-ready, I did not enter the markets vs. non-markets debate when you were having it some months ago. However, among other reasons, we avoid collective forecasting because it is too similar to collaborative forecasting, which is key in supply chain. Honestly, when all is said and done, our clients care not what the mechanism is. They care that we can efficiently gather team intelligence and translate it into actionable business intelligence. That is our mission.
Previously: CrowdCast = Collective Forecasting = Collective Intelligence That Predicts
Leslie Fine wrote on the CrowdCast blog (April 29, 2009):
“We have abandoned the MSR in favor of a new method that users are already finding much simpler and that requires a lower level of participation and sophistication than the usual stock market analogy.”
CrowdCast is trying to improve participation in their prediction markets, but at what cost? By substituting a simpler mechanism for placing “bets”, they are trying to make it easier for participants to take part (they don’t need to know how stock markets work to play). However, Leslie also mentions that their mechanism requires a lower level of participation (maybe as few as 10)! This appears to be yet another example of a vendor forgetting the first principles of prediction markets. In this case, they are trying to downplay the requirement of a “crowd”.
While I like what I can see of the new market mechanism (a few seconds of the market distribution and a sample betting range, as displayed in their very short video), there is not very much information forthcoming about how it works and how bets are priced in the interim. Perhaps we will find out more during the webcast on Thursday.
I have been investigating the concept of betting ranges in prediction markets, and I can tell you that it is very complex and requires several assumptions about participant intentions, share valuations and outcome payoffs. Without further information about CrowdCast’s assumptions, it is too early to comment on their mechanism. Soon, I will post my findings about the application of ranges in prediction markets. At this point, I suspect that, while there are some benefits, there are also several drawbacks, which, ultimately may make it no better than other mechanisms.
Personally, I see this (and many other “innovations”) as merely tinkering with the tool, rather than focusing on the accuracy and consistency of prediction market forecasts.
I’ll keep you posted.
Paul,
Keep us informed.