The case on Google’s internal corporate prediction market that I wrote with Peter Coles and Karim Lakhani is now available for wide distribution (a teaching note for this case is also available to faculty). The case’s introduction explains what prediction markets are, and why they might be interesting to business leaders:
"Prediction markets were very much like stock markets. They contained securities, each of which had a price. People used the market to trade with one another by buying and selling these securities. Because traders had differing beliefs about what the securities were worth, and because events occurred over time that altered these beliefs, the prices of securities varied over time.
In a stock market like the New York Stock Exchange the securities being traded were shares in companies, the prices of which reflected beliefs about the value of the companies. In a prediction market, in contrast, the securities being traded were related to future events such as an American presidential election. In this case, the market could be designed so that each security was linked to a candidate, and its price was the same as the estimated probability that the candidate would win, according to the market’s traders.
Prediction markets on the Internet had proved to be remarkably accurate at predicting the results of political elections and other events, and the Googlers had wanted to see if they could also be productively used within companies to forecast events of interest such as the launch date of a product or whether a competitor would take a specific action. The experiences of the previous seven quarters had shown that Google Prediction Markets (GPM) were in fact quite good at predicting such events. Googlers put none of their own money at risk when they traded within GPM; instead, they bought and sold securities within GPM using “Goobles,” an artificial currency."
I’m going to teach this case on Tuesday in my MBA course, and am really looking forward to it. It’s one of my favorite classes of the semester, and will be made even better by the fact that Bo Cowgill, the Googler who initiated prediction markets within the company, will come to Boston to share his insights with my class (and also with Tom Malone‘s at MIT).
Cowgill has written a paper with Justin Wolfers and Eric Zitzewitz analyzing data from Google’s markets, and Wolfers and Zitzewitz also wrote a more general overview of prediction markets. The Wikipedia article on the topic is another good resource. Prediction markets on the Web include the Iowa Electronic Markets, InTrade, NewsFutures, and the Hollywood Stock Exchange.
Our case concentrates on two issues: how to encourage more trades and more liquidity within a corporate prediction market like Google’s, and how business leaders can and should use the information provided by the market.
After writing the case, teaching it a few times, and spending some time understanding the mechanics and utility of prediction markets, I share the puzzlement articulated by James Surowiecki in his book The Wisdom of Crowds:
". . . the most mystifying thing about [prediction] markets is how little interest corporate America has shown in them. Corporate strategy is all about collecting information from many different sources, evaluating the probabilities of potential outcomes, and making decisions in the face of an uncertain future. These are tasks for which [prediction] markets are tailor-made. Yet companies have remained, for the most part, indifferent to this source of potentially excellent information, and have been surprisingly unwilling to improve their decision making by tapping into the collective wisdom of their employees."
Why is this? It’s not because the technology is hard to acquire: Inkling Markets, Xpree, and Consensus Point, among others, will happily provide a company with Web-based prediction market software. So what is the real stumbling block? Is it that companies don’t really want the most accurate information about future events to come out and be widely known?
Leave a comment and let us know what you think, or what your experience has been. I’ll post more on this topic after our class on Tuesday.