Google urges staff to gamble

Prediction markets forecast the outcome of elections more accurately than polls. That is why Google has revealed it's been using an internal prediction market to learn what its employees really think, and thereby improve its business decisions.

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Prediction markets forecast the outcome of elections more accurately than polls. That is why Google has revealed it's been using an internal prediction market to learn what its employees really think, and thereby improve its business decisions.

Basically operating like a stock market, Google's prediction system lets employees bet on probable outcomes. Will a project be finished on time? How many users will gmail have? Who will win baseball's World Series?

With about 1,500 employees making 80,000 "trades" over the past two and a half years, Google officials say it's the largest corporate experiment with prediction markets they know.

Google employees bet "Goobles" instead of real currency, but at the end of each quarter the most successful bettors can win cash or other prizes.

More importantly, said Google economic analyst Bo Cowgill, the trading system lets the Google hierarchy discover its employees' uncensored opinions.

"If you let people bet on things anonymously, they will tell you what they really believe because they have money at stake," Cowgill said. "This is a conversation that's happening without politics."

Cowgill described the prediction market this week at the O-Reilly ETech conference on emerging technology. Cowgill and economists from Dartmouth College and the University of Pennsylvania also published a paper on Google's experiment in January.

From a practical standpoint, the Google prediction market gives managers insights that might impact business decisions and which they might not have obtained in any other way. In one example, "the market was predicting that [a project] was behind," Cowgill said. "A manager says 'What's going on here', . . . starts investigating and finds some glitches."

Analysis of the markets found a distinct pro-Google bias, however. Cowgill separated outcomes into the categories "good for Google" and "bad for Google," excepting such unrelated prediction markets as "will Harriet Miers be confirmed?" Ultimately, people betting on "bad for Google" were able to make a killing because traders bet too heavily on the good outcomes.

People were even more optimistic about specific events related to Google on days when the company's stock price went up, even if the increase had nothing to do with the events being wagered on.

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