We attended last week’s Nashville Technology Council Member Breakfast, where the big news was Microsoft CEO Steve Ballmer’s visit to Nashville. Seeing Mr. Ballmer show off Bing and other new Microsoft technologies was certainly impressive, but the relevance of the other speaker, Abbie Lundberg—the former Editor-in-Chief of CIO—wasn’t lost on us, either.

Ms. Lundberg referenced a session at the National Retail Federation’s recent Retail’s BIG Show 2010 expo in which Wal-Mart’s EVP and CIO Rollin Ford told attendees that corporations don’t have a lot of secrets anymore. So the only competitive advantage becomes speed and getting from point A to point B faster.

Lundberg also revealed that, as of December 2009, surveys indicated that 40% of CIOs would increase in spending on IT. This dovetailed nicely with an MIT study that demonstrated that IT-savvy firms are 20% more profitable (if you can help us cite the study, please let us know in the comments).

Our prediction markets platform not only helps companies get from point A to point B faster, it helps them understand why arriving at point B is better than arriving at points C, D, or Z. We offter tremendous business value for companies having difficulty finding that competitive advantage.

Maybe our ability to offer innovative competitive advantage through technology is why CIO decided to write about our customer success with Motorola. If you’re a CIO increasing your IT spend this year, you might consider investing in prediction markets. We recommend Foresight.

Correction: Apparently, we misread our notes or were typing too fast. As originally written, we incorrectly stated that CIOs were projecting spending increases of 40% in 2010. Our apologies to Ms. Lundberg.

In September 2009, McKinsey & Company revealed the results of a global survey on trends in Web 2.0 in the enterprise. Prediction markets were included among 12 core Enterprise 2.0 technologies. Adoption within global corporations has risen from less than 1% in 2007 to 8% in 2009.

We were delighted that prediction markets were identified as a key Web 2.0 technology. However:

Respondents who report that Web technologies have strengthened their companies’ links to customers also cite blogs and social networks as important. Both allow companies to distribute product information more readily and, perhaps more critically, they invite customer feedback and even participation in the creation of products.

Similarly, among those capturing benefits in their dealings with suppliers and partners, the tools of choice again are blogs, social networks, and video sharing. While respondents tell us that tapping expert knowledge from outside is their top priority, few report deploying prediction markets to harvest collective insights from these external networks.

This disconnect is puzzling to us. Prediction markets offer an efficiency of consensus that is not delivered by enterprise social networks. Platforms like Foresight offer effective leading business indicators that convert straight to actionable decisions.

Respondents, have you considered requesting additional information from us so that we can help you harvest collective insights from your external networks?

Just before the end of the year, we read Ian Ayres’s musings on prediction markets over at Freakonomics. Writing on his personal blog, Consensus Point Chief Scientist Robin Hanson responded to the post and elaborated on whether prediction markets better served as methods or forums:

1. How to pick city policies, vs. how to pick the mayor.
2. How to cook a meal, vs. how to pick a restaurant.
3. How to win a game, vs. how to decide which team won.
4. How to do a study, vs. how to pick a study to publish.

These are four examples of methods vs. forums. Methods are ways to do things; forums are ways to pick who decides what to do. Yes, in a sense forums are methods, since choosing who decides indirectly picks what to do. But that is what makes forums powerful; good forums induce people to find good methods. Good elections induces good city policies, good restaurant competition induces good cooking, good game rules induce good play, and good journal review induces good articles.

To me, prediction markets are mostly interesting as forums, not methods. Alas many seem to confuse the two.

Robin elegantly puts the history of the concept into context and dismisses the idea that the wisdom of the crowds serves as an equalizer; rather true wisdom is revealed by self-selecting experts with incentives. He then goes on to suggest that academic journals might not be the best forum for choosing forecasting methods.

“Prediction markets” started from speculative markets, e.g. stocks, where accuracy comes much less from non-expert participation and much more from participants with incentives to self-select as experts. Any team that considers itself expert enough can pay to prove itself, but in fact most teams stay away and prices tend to be dominated by real experts, who get paid and really know better than most.

Prediction markets aren’t about emphasizing ordinary Joes over credentialed bigshots; they are about emphasizing whomever tends to be right. Simple opinion averages maybe be reasonable indicators of crowd wisdom, but they have too little of the forum-ness of letting self-selected expert teams come to dominate.

It seems to me that when academics like Aryes call for academic studies of prediction markets as methods, instead of as forums, they are implicitly suggesting that current academic institutions should be the forum in we choose forecasting methods. If academic journals prefer a method, they suggest, that’s the method the world should use.

In contrast, I suggest prediction markets may be a better forum than academic journals for choosing forecasting methods. Maybe the world shouldn’t use a method just because academics say its great; maybe those impressed with a method should have to put their money where their mouth is and trade on that method’s forecasts in prediction markets. Maybe the rest of us should just accept prediction market prices as our best estimates; if and when prediction market prices become dominated by traders using a method, that is when the rest of us will have implicitly accepted that method as best.

How might the academy respond? Our guess is with skepticism. Care to bet?

Okay, maybe the title is a bit overblown, but Consensus Point Co-founder and Chief Technology Officer Ken Kittlitz was second author (with Johan Almenberg and Thomas Pfeiffer) on a recent study at Harvard’s Program for Evolutionary Dynamics involving the application of prediction markets to scientific publication:

Prediction markets are powerful forecasting tools. They have the potential to aggregate private information, to generate and disseminate a consensus among the market participants, and to provide incentives for information acquisition. These market functionalities can be very valuable for scientific research. Here, we report an experiment that examines the compatibility of prediction markets with the current practice of scientific publication. We investigated three settings. In the first setting, different pieces of information were disclosed to the public during the experiment. In the second setting, participants received private information. In the third setting, each piece of information was private at first, but was subsequently disclosed to the public. An automated, subsidizing market maker provided additional incentives for trading and mitigated liquidity problems. We find that the third setting combines the advantages of the first and second settings. Market performance was as good as in the setting with public information, and better than in the setting with private information. In contrast to the first setting, participants could benefit from information advantages. Thus the publication of information does not detract from the functionality of prediction markets. We conclude that for integrating prediction markets into the practice of scientific research it is of advantage to use subsidizing market makers, and to keep markets aligned with current publication practice.

Imagine our surprise that the experiment further validates the use of prediction markets as powerful forecasting tools.