The field of prediction markets seems to be going through a bit of a crisis of confidence recently. I would personally trace it to the recent election (where other forecasters like Nate Silver made forecasts as good as PM’s), as well as recent press like the Economist article. The general feeling is a questioning of prediction markets: if they’re so good at forecasting, why aren’t they being used much more widely? I think I have the start a good reason why.
Traditional forecasting is done through highly analytical techniques using past data. Statistical measures are used to generate forecasts, with probability ranges. This industry is quite large, and is highly exacting.
Prediction markets take an orthogonal approach to traditional forecasting. Instead of a “top-down” approach where huge data sets are analyzed, prediction markets use a “bottom-up” approach that combine individuals’ forecasts.
The reason prediction markets haven’t been adopted widely is because they are a tool that approaches the forecasting problem from a completely different perspective.
An example — Enterprise Business Intelligence
I’ve recently been looking into the Enterprise Business Intelligence/Business Management industry, and came across what I think is a similar phenomenon. The vast majority of the industry is composed of massive analytical solutions from the likes of SAP, Oracle, IBM, etc. They are massive companies, and implementing a “solution” can easily take a year or more. Their clients design the system from a “top-down” perspective, determining from the outset what the processes and procedures are going to be.
But then there is software like Thingamy. Thingamy is the creation of Sig Rinde, a Norwegian living in the south of France. Instead of looking at enterprise business intelligence from the top-down, he has created software that approaches the problem from the bottom-up. Instead of establishing pre-defined processes (that may not even work or will be changed by the time the software is configured), Thingamy tracks emergent processes as they happen. It can start with a very small, hard-to-define process and then scales up as the business needs it.
While Thingamy has gotten some good press and attention over the years, it’s still a fairly small company. Again, I believe this is because it takes a fundamentally different approach to the problem compared to the rest of the current industry. Hugely different approaches cause cognitive dissonance, which slow adoption.
What does this mean?
There are new types of technologies that approach business problems from entirely different directions. Prediction markets is one of these technologies. Using PM’s means companies have to upset some of their current notions about how power and influence flow in a company, relying on “soft” information from lower-level employees. A different approach also means that in certain situations they’ll be clearly superior, but also that in other situations they won’t be. Traditional methods and thumb-rules for situations just don’t automatically work.
For example, prediction markets where there is a lot of public information (like election markets) may prove to integrate new news and information more quickly, but may not be quite as accurate as other methods in the final analysis. But where information is scarce (like some internal corporate forecasts), a prediction market may be ideal. In general, new ways of thinking have to be established to know when and where to use this new tool effectively. That’s why I believe prediction markets will take quite some time to see any sort of a spike in growth; expect a slow burn for a long time.
Just a quick note for you all. I’m curious about how well Google’s AdSense can be used to monetize a blog, so I’m going to be running AdSense on this blog on a one-month trial. If you have any opinions on this, please feel free to e-mail me or comment below.