We’ve been reading Harvard Business Review blogger and MIT Center for Digital Business researcher Andrew McAfee’s excellent book, Enterprise 2.0, which is full of valuable lessons for the enterprise, including that prediction markets are a very useful collaborative tool.

For instance, here’s an interesting discovery from the Google Prediction Markets, originally proposed internally in December 2004:

Analyses … revealed that at every point in time, even as much as ten weeks away from the closing date of the market, the most expensive outcome was the one most likely to actually occur. It seemed that GPM’s markets, in other words, could quickly and accurately distinguish among possible outcomes, identify the one most likely to occur, and attach a high price to that outcome.

This is exactly what our Foresight platform does on a regular basis for our customers.

Regular readers might remember a few months back when we cross-posted one of his posts from the Harvard Business Review blog. You might also recall when we posted a presentation that Linda Rebrovick (our CEO) gave in Chicago at the Prediction Markets Cluster conference in Chicago last November.

Linda noted the following best practice examples in her presentation:

• integrate into enterprise processes
• nurture executive sponsorship
• go big or go home
• make accessible to all
• customize to your business
• make it part of your value proposition

We were struck how similar these examples were to the Six Organizational Strategies identified by McAfee:

• Determine Desired Results
• Prepare for the Long Haul
• Communicate, Educate, and Evangelize
• Move into the Flow
• Measure Progress, not ROI
• Show That Enterprise 2.0 Is Valued

Coming back to the commentary on GPM, McAfee continues:

Google’s prediction markets shared with all markets a fundamental property: the ability to generate highly valuable information by bringing people together who have little or nothing in common.

Okay, we don’t actually know how different Linda and Andrew are, but we’re pleased that our executive leadership understood key lessons before an interested commentator went to press with his book. It’s almost… predictive.

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.

Ingenix, a Consensus Point partner, recognizes how important the power of collective intelligence can be in healthcare. With the Ingenix Prediction Market, Ingenix is helping employers optimize how healthcare dollars get spent. The end result is more profitable companies with healthier, happier employees.

Ingenix also offers a variety of solutions directly to physicians. We wouldn’t be surprised to see collective intelligence solutions from Consensus Point helping Ingenix empower doctors as well as employers.

Last week, Jonathan Bush, CEO of Athenahealth, a physician billing and practice management firm, was interviewed in the Wall Street Journal. In a wide-ranging interview covering various dimensions of healthcare policy and the ramifications from technology and innovation, this stood out to us:

Mr. Bush thinks the main benefit is the “collective intelligence” that he is starting to weave together from the 87% of American physicians who practice solo or in groups of five doctors or fewer.

Time and again the wisdom of crowds has proven valuable and most times more accurate than a single SME. Applying prediction markets in healthcare can yield benefits for policy and delivery of services.

For more on the Ingenix Prediction Market and Ingenix solutions, go to www.ingenix.com/informationis.

The Obama administration raised the innovation bar by incorporating social media into its campaign and day-to-day operations. Tales from the Technoverse’s blog post on “Technologies to Watch in 2010,” confirms that prediction markets are being successfully incorporated into government entities and will continue to be on the rise in 2010.  The Consensus Point government clients have been successful in projecting results and reducing uncertainty with prediction markets.

Excerpt from Tales from the Technoverse, “Technologies to Watch in 2010″

[Government 2.0] will also lead to greater use of 2.0 technologies to implement various versions of crowd sourcing. Where Intellipedia and Aspace are big news, internal wiki’s will become more second-nature. Pilots associated with prediction markets, using groups to predict things like project results or other public facing data, are starting to be piloted by early adopters.

Andrew McAfee shares a “teaching moment” demonstrating the real-life application of prediction markets.  McAfee’s example shows that, even in its simplest form, prediction markets are very accurate and have a wide range of uses. 

Prediction Markets: A Teaching Moment 
cross posted from the Harvard Business blog by Andrew McAfee
2:14 PM Tuesday December 1, 2009 

Nick Bostrom, Director of The Future of Humanity Institute at Oxford University, considers the impact that prediction markets could have on the creation of government policy in the UK. 

Rebooting Britain: Make policy using prediction markets

By Nick Bostrom | 01 December 2009

This article was published in the January issue of Wired UK magazine.

How do we know what to think about the future? Politicians make confident predictions. If we elect them, unemployment will allegedly go down, the economy will grow, crime will be reduced, and terrorist attacks will be prevented. If we elect their opponents, the opposite will happen. These opponents, of course, disagree. Whom should we trust?

We could listen to the media pundits, but pundits are usually given a platform because they are articulate and entertaining, not because they have a track record of being right. We could listen to academic experts, but both sides of a political dispute can usually point to some experts who support their view. Or we could try to form our own opinions; but we may not know very much about the issue at hand, and at any rate, it is unclear why we should believe that our opinions would be any more reliable than the opinions of all those millions of people who have considered the issue and embraced the opposite view.

One way of generating predictions is betting markets. If people are allowed to buy and sell bets that some hypothesis is true, then the fluctuating price of those bets can be interpreted as a probability estimate of that hypothesis. The hypothesis could be that some particular horse will win a race, or it could be that weapons of mass destruction will be found if our forces invade some particular country. The principle is the same in both cases but, as pointed out by the economist Robin Hanson, the information that could be revealed is much more valuable in the second case.

In every known head-to-head field comparison between speculative markets and other forward-looking institutions, the speculative markets have been at least as accurate. More often than not, they prevail. Orange-juice futures improve on National Weather Service forecasts, horse race markets beat horse race experts, Oscar markets beat columnists’ tips, gas demand markets beat gas demand experts, stock markets beat the official NASA panel at identifying the company responsible for the Challenger accident, election markets beat national opinion polls, and corporate sales markets beat official corporate forecasts.

Prediction markets can aggregate many small pieces of information held by large numbers of people from diverse backgrounds. Prediction markets seem to work well because they reward accuracy (rather than the ability to tell a convincing story) and punish error (rather than the voicing of politically inconvenient opinions).

No system for making predictions is going to be perfect, but so far the empirical evidence seems to quite strongly favour prediction markets compared to alternative ways of generating forecasts. Therefore, the traditional ways of forecasting uncertain political futures – pundits, academic experts, debates between leading politicians, and personal gut feelings – should be supplemented by the creation of prediction markets wherever possible. When the issue at hand is sufficiently important, such markets should be subsidised by the state as a relatively inexpensive form of intelligence gathering.

Horse-betting is selfish, but betting on policy-relevant outcomes would be public service. Pundits should be expected to put their money where their mouths are, and everybody who can afford to lose £10 or £20 should be encouraged to participate. Journalists should be asked to include information about prediction market estimates in their coverage of controversial topics. Schoolchildren should be taught applied probability theory in the classroom and given the opportunity to practice their skills in real-world settings.

This way, I think, Britain would make shrewder policy decisions. Moreover, its population would learn to think about uncertainty in a sophisticated and mature manner. In our complex modern world, that would be a winner.

Nick Bostrom is director of the Future of Humanity Institute at Oxford University.

The Consensus Point solution has proven to be an effective tool for innovation management.  Read below about an effective innovation process and how the Consensus Point idea and prediction markets have improved the innovation process for corporations.

James Gardner, Chief Technology Officer at the Department for Work and Pensions in the UK and author of the blog, Banker Vision, explains the key components of corporate innovation.  Gardner’s “tools of innovation” also are aligned with the fundamental building blocks of prediction and idea markets. 

The Tools of Innovation – excerpts from James Gardner’s BankerVision 
Our commentary in italics

A.M Mills, the author of Hell Bent on Success asks me to explain what I mean by the “tools of innovation”. It is possible to summarise, I think, because everything you need to know about doing innovation happens in four stages.

1. Futurecasting
The first is futurecasting. This is the process of working out what is likely to happen in the future so you can guide subsequent iterations of your innovation process. In the book, for example, I explain a specific futurecasting process based loosely on a scenario planning methodology, but anything that gets structured consideration of the future on the table is a good thing. Why is this important? Well, firstly, you can never ask a senior person for support on something new, especially if that new thing impacts current business or revenue, without rehearsal. They need time to think over consequences, and making them think about the future helps them do that. The second reason is that random innovation without a guide won’t always result in new things that solve the strategic problems of the firm. Out of the box thinking is all very well, but if you are not only out of the box, but out of the ballpark, it is usually not helpful.  

For example, GE has run imagination markets successfully for several years, providing a vehicle for leaders to think about the future in the context of other options. GE imagination markets resulted in higher quality ideas than traditional methods.

• 60% of ideas rated as high quality
• Gathered valuable ideas and engaged employees in a fun way

2. Ideation
Ideation is the process of collecting ideas and deciding how good they are relative to all the other ideas that you might have. The thing is, if you’re running a programme, you’ll likely have far more ideas than resources to execute them. So you have to have a way of deciding what you’re going to work on. Of course, if you’re still thinking that the answer to your innovation challenge is getting the good ideas, then you have some work to do. Ideas are everywhere, and usually all you need to do is find a good way of collecting them. Anyway, you’ll have more ideas than you know what to do with, so one of the main tools of innovation is a decent way to prioritise. Usually, people create various scoring systems to do this, but crowd based methods, such as voting and prediction markets work just as well. 

For example, Motorola launched an idea market in 2007, powered by Consensus Point, for  employees to prioritize thousands of ideas on a quarterly basis. Motorola realized the following benefits, in addition to effectively prioritizing ideas: 

• Increased number of new ideas that are pursued from 11% to 22%
• Decreased number of duplicate ideas by 50%

3. Innovate
The third stage is what I call the “innovate” stage, which is really all about the tools and processes you use to work out – in detail – the stuff that has to happen before an idea is actually fundable.  Anyway, to get to the crux of the matter here, you need to answer three questions. “Can we do this?”, which is technically, operationally, and environmentally, is the idea actually possible. “Should we do this?”, which is primarily economic, i.e. can we afford it, and if we can, will anyone want it?. And, finally, “When?”, which is mainly about the response of competitors or internal players. Answering all that means you have a case which is a candidate for funding and delivery. As you’d expect, there are lots of things you do for each of those questions to get to decent answers for as little investment as possible. 

For example, GE employees participating in the company’s “Imagination Market” trade or buy “ideas” based on how closely they believe an idea is aligned to the business objectives, how an idea compares to other alternatives, and if the idea is operationally feasible. Most often, the ideas represent new technology or new product ideas.

4. Execution
Once you have money, the final stage is execution, which is all about building the thing and getting it out in the market. Key tools here include all the things you need to do to win over users, prove you know what you’re doing from an operational perspective (remember, it’s innovation, so its new, so no one will have made it work before), and a ton of other things. But I think the most important thing is you don’t even get to the Execute stage until you’ve done a substantial amount of groundwork first.

Gardner’s “tools of innovation” are a useful guide to effective innovation management, and idea/innovation markets enable several steps in his recommended process.