Introduction to Prediction Markets

Prediction Markets are an implementation of the broader concept of Collective Intelligence. In general, Collective Intelligence is an intelligence that emerges from the shared knowledge of individuals which can then be used to make decisions. With Prediction Markets (PM), this intelligence emerges through the use of market mechanisms (buying/selling securities) where the pay out depends upon the outcomes of future events. In short, the collective is attempting to predict the future.

Prediction Markets should be familiar to us because a stock market is really just a forum for making predictions about the value of some underlying security. Participants buy and sell shares in a company, for example, based on information they feel is relevant to the future value of that company. A security's price is an aggregated bit of information that is not only a prediction about the future, but is also new information from which more predictions can be made. That last part is important because prices are information that cause participants to act in a market.

A real-world example of using PMs to make decisions is Best Buy's TagTrade system. This system is used by Best Buy employees to provide information back to management on issues like customer sentiment. The linked article explains one particular incident:

TagTrade indicated that sales of a new service package for laptops would be disappointing when compared with the formal forecast. When early results confirmed the prediction, the company pulled the offering and relaunched it in the fall. While far from flawless, the prediction market has been more accurate than the experts a majority of the time and has provided management with information it would not have had otherwise

Another interesting example comes from Motorola and their attempts to deal with idea/innovation requests from their employees. Their ThinkTank system was set up to allow employees to submit ideas on products and innovations. Those in charge with weeding through these requests were initially overwhelmed. To improve the process, Motorola used PM software to allow employees to purchase shares in the submitted ideas. At the end of 30 days the market was closed and those ideas that had the highest share price got pursued, and employees holding stock in those ideas got a bonus.

(Some other companies using Prediction Markets are IBM, Google (PDF), Microsoft, and Yahoo! Some of these companies use internal prediction markets (employees only) while others provide external markets (general population). The Iowa Electronics Market (IEM), associated with the University of Iowa, uses PMs to predict election outcomes. IEM has been in existence for over 20 years, and has studies showing their predictions being more accurate than phone polls.)

The bonus paid out by Motorola points to an important aspect of PMs - incentives. With good incentives participants stay interested in the process and look for ways to make more accurate predictions. Driving people to discover new information about future events can lead to interesting behavior in a company.

Another key aspect of PMs is the idea of weighting. That is, the ability of traders to put some weight behind their predictions. Those who are more confident in their predictions can purchase/sell more shares in those outcomes. Contrast this with a simple survey where an expert's opinion gets the same weight as a layman's (one person one vote).

Railinc is now starting to venture into using Prediction Markets with Inkling's software and services. Some of the topics for which predictions could be made are bonus metrics, customer surveys, project metrics, and fun things like World Cup results. One thing that will be interesting to track over the coming months is the value of PMs in such a small company (Railinc has approximately 150 employees). Value from PMs tends to come from larger populations where errors can be canceled out and participation rates stay constant. The hope is that at some point these markets will be opened to various parties in the rail industry thereby increasing the population and alleviating this concern. If the markets were opened up to external parties then the topics could be broadened to include regulatory changes, industry trends, product suggestions, and ideas to improve existing applications. So, the potential is there if the execution is handled properly.

Prediction Markets provide an interesting way to efficiently gather dispersed information. Using this innovative tool, Railinc will attempt to tap into the Collective Intelligence of its employees and, hopefully, the rail industry.

More to come.