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.

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.

Jenny Ambrozek reflects on prediction markets on her blog after attending the PM Cluster Summit last week in Chicago. Below is an excerpt from Amzbrozek’s blog.

Have Prediction Markets arrived as an Enterprise Knowledge Sharing & Innovation Platform?

A small and really smart group of people convened by John Maloney (at the Gleacher Executive Center in Chicago), on November 6 to explore the latest developments in collective intelligence and use of prediction markets.

For those new to prediction markets finding a public prediction market to explore is increasingly easy, for example see the Industry Standard and CFO Magazine. Andrew McAfee lists prediction markets as part of Enterprise 2.0. This Inside Knowledge Prediction Markets Masterclass (co-authored with colleague Victoria Axelrod) describes the prediction market landscape in 2008.

Why did I leave Chicago thinking that enterprise use of prediction markets to tap grassroots employee knowledge for forecasting, and in support of innovation is about to blossom?

Three reasons:

1. High Profile Proven Enterprise Prediction Market Applications

Exemplifying the time for new ideas and technology to find their way into widespread adoption first use of an enterprise prediction market is credited to Robin Hanson (George Mason University professor and Consensus Point prediction market platform provider Chief Scientist) and dates to 1990. 

The wider adoption of prediction markets by companies from Google to Best Buy, Cisco Systems, GE Healthcare, General Mills, Qualcomm and ArcelorMittal, is widely reported including in this New York Times articles. Friday Rami Levy, added to the list in explaining Motorola’s evolved use of a prediction market to filter ideas and speed innovation.

2. Technology Evolution

As a pioneering prediction market provider since 1994, Chicago Cluster sponsor Consensus Point hosts high profile clients Best Buy, Motorola and Qualcomm among others.

Each provider is carving out a niche and extending enterprise prediction market applications.  In the process platforms are evolving, made easier to use and integrate into day-to-day business processes.

3.  Growing Enterprise Understanding

The case has been made for the business value that comes from reaching out and engaging more diverse minds to solve business problems and co-create new opportunities. A host of books from James Surowiecki’s Wisdom of the Crowds (2004) to Yochai Benkler’s Wealth of Networks (2006), Dan Tapscott’s Wikinomics  (2006) and Clara Shih’s Facebook Era (2009) detail the trend. 

While enterprise prediction markets have been the province of innovative companies, the Chicago participants pointed to a diverse and expanding array of new applications.  

Putting prediction markets to work in enterprises demands a wide array of skills from technical understanding for making markets perform within the culture of an organization, to relationship building to engage participants and encourage contribution.  Quantitative skills + tie to business strategy + relationship building + technology are all essential.