Prediction markets are breaking out of their esoteric niche and finding more and more applications in helping managers make decisions. For example, HP used to forecast the price of DRAM chips based on discussions of 10 top executives; by creating a prediction market, i.e., creating a trading system and giving employees across the company “money” to bet on the price of DRAM, essentially aggregating their knowledge, HP was able to improve the accuracy of its forecasts and thus improve the quality of its decisions (http://money.cnn.com/magazines/business2/business2_archive/2006/09/01/8384339/index.htm). More interesting, GE uses prediction markets to decide what new research to pursue.How does this apply to gaming? I was speaking to a manager at an area games publisher recently, and he was very keen on using prediction markets within his company to pinpoint the actual release dates of new games titles. There exists, however, the potential for much greater applications. With the network capabilities of the latest console generation, online gaming is gaining in importance, allowing games makers greater access to game-playing communities — and their insights into what games are likely to be successful. Creating prediction markets that encompass company employees and gamers should lead to companies picking games to develop that are more likely to be successful, focusing development dollars where they are most likely to be productive.
On a related note, I find interesting the idea of applying game mechanics to other products. I expect to see collaborative filtering systems (in essence, prediction) become more like games to encourage use. As is, Digg and Slashdot are part game, complete with collecting, points, feedback, and exchanges (a MMPORG?). As collaborative filtering learns how to incorporate customization/personalization, I expect we’ll see more complete solutions than the simpler systems that have gained popularity to date.