September 15 2008 / by Alvis Brigis / In association with Future Blogger.net
Category: Economics Year: General Rating: 4
Enterprise prediction markets have been growing in popularity, but face three major hurdles to success: 1) lack of access to all relevant information, 2) regulatory concerns, and 3) adoption / sticky use. As these are resolved, new-age prediction markets will increase in value, diffuse more quickly and make us smarter as a species.
1. Lack of access to relevant information: My big takeaway from Wisdom of the Crowds, the prediction market bible by journalist James Surowiecki, was that a large group of humans can consistently out-predict individuals, but only if all the brains are knowledgable of the given topic area. For example, farmers won’t be great at predicting next year’s fashion colors – that will be left to the those with more direct exposure to the appropriate industry trends.
Prediction market guru Chris Masse points out a similar flaw plaguing most, if not all, enterprise prediction markets: lack of access to ”’experts’ and other ‘business leaders’”. Masse argues that minus this crucial top-level information a company’s internal “prediction markets would be clueless, useless, and worthless.”
Solutions: The obvious but eminently unpalatable solution is for corporations like Google, GE, and Microsoft that already utilize prediction markets to open-up access to more of their top-level data to employees or even the public. This would immediately result in better predictions, but would obviously benefit their numerous cut-throat competitors. It will take some time for big businesses to implement such transparent practices, though I can imagine the right start-ups could successfully implement such an open strategy and then scale.
On the flip side of coin, companies could up the incentives for successful predicting in external but vastly larger markets, essentially throwing more money and brains at the process. They could then make use of the growing # of top rated performers and ideas (would be shocked if they’re not already mining such data). It seems like this will gradually occur as 1) companies increasingly look to the web for ideas, 2) the semantic web and better search makes everyone smarter faster.
Then again, a more immediately plausible middle road could involve bringing on a group of professional predictors, say 40 – 100 diverse individuals, and then give them access to the highest level information. Of course, they would be required to live in a cave and never again communicate with friends or family…
2. Regulatory concerns: The CFTC is now actively trying to figure out how to regulate online prediction and information markets. At the same time, companies are shying away from embracing more open prediction markets all-out for fears that they could promote, or be seen as promoting, insider trading. It’s obvious that something will need to give soon if indeed U.S. companies are going to reap the maximum benefits of prediction markets.
Solutions: The road here is not very clear and I suspect that the best solutions remain as yet uninvented. Still, we can see that the spectrum of options ranges from 0% to hard core regulation, and 0% – total corporate transparency. As all markets become more fluid and 24-7, new technologies, behavior and legislation will need to emerge. Perhaps a new system for defining and fining those attempting to illegally manipulate the infocosm will be created. It’s still really wide open, as the variables in play are immense. All we can really hope for is that government doesn’t make rash, uninformed decisions that take the legs out from these new quantification structures before they can fully develop.
3. Adoption & stickiness: Fast tech diffusion requires great function, great design, obvious value proposition and totally activated communication channels. Accordingly, prediction markets must get smarter, easier to use, more obviously valuable and more popular. Fortunately there is no shortage of innovation pressure in the space.
Solutions: Function – Prediction market software must get better at 1) identifying/finding experts, 2) simplifying the prediction process, 3) providing relevant information (hello semantic web), and 4) crunching more variables / expanding the prediction pool (smarter social media models, new prediction pool techniques).
Design – Prediction markets are co-evolving with the mobile and cloud web and must adapt themselves to iPhones, facebook apps and RSS. They must be super-simple to use across all of these platforms. Smarter AI and interfaces are critical. Also, the fun quotient must consistently rise, just as in other social media and video games. Not to worry, these are all on the way.
Obvious Value Proposition – As more people earn more rewards, both monetary and social, for participating in prediction markets others adopters will follow.
Activated Communication Channels – As function, form and value proposition continue to improve, more media will cover and even incorporate prediction markets. Like all technologies, this will likely take longer than you think, but when it kicks in it will go faster than you think – the law of s-curves in diffusion. This could all be accelerated by some big media coverage or adoption of prediction markets.
Conclusion: Better, smarter enterprise prediction markets are coming and will require new technology, better design, and a perfect media storm to accelerate their diffusion and functionality. It’s not exactly clear how this will all progress, considering all the techno-social variables, but the trend lines are becoming more defined. More robust prediction markets will result in a fundamental increase in our collective social intelligence and will therefore be encouraged across the globe in various social systems.
That being said, what do you think is the future of enterprise prediction? What am I missing here?