Essay About Traders’ Accounts And Internal Prediction Market

Google Prediction Markets Case Study
PREDICTION MARKETS AT GOOGLECASE STUDY-Hrishabh Ashok SobtiGoogle was founded by Larry
The IEM model relied on the use mutually exclusive securities that covered all possible outcomes for an event. If an event E had possible outcomes – A, B and C, and a participant wanted to invest in outcome C, he/she could buy that security using ‘Goobles’5.  And if that C was trading at a price of 0.25, the market “believed” there was a 25% chance that C would occur by the end of that particular quarter. The team decided to use Gooble. At the start of each quarter, traders’ accounts were reset, and all traders were given 10,000 Goobles.They also realized that a direct monetary award could tempt some people to behave counterproductively, yet wanted to reward successful traders. To that cause, they defined that each trader’s final balance would entitle them to the same number of lottery tickets. The more balance at the end of a quarter, the more chances for winning a $1000 lottery. Since this would encourage them to maximize the number of tickets, they would trade as intelligently as possible. Other rewards included T-shirts to top performers.While Cowgill and his team had the righ idea, and an thought-out implementation, the concern raised by Dolores Haze6 cannot be overlooked. Despite the accuracy of the market – where the most expensive outcomes actually occur, GPM can not practically replace other planning or forecasting tools for the following reasons:Out of the 14 functions as google, how do you guarantee a fair highlight of each function’s topics of forecast? Rather – How do you make sure that no function feels neglected.From the “Total Accounts Over Time”infographics, only ~2000 accounts are created, out of a total of 45000+ employees. That’s less than 4% participation – only a fraction of which participate. Although the total shares traded over time have grown steeply, it can be attributed to a few users trading much more frequently than other.Given Vladimir Humber’s account on GPM particpation, it is clear that users are also misleading the market.The accuracy of GPM could be highly contextual – the “Trading Base by Function” infographic clearly shows that most participants tend to make more trades relevant to their own function. Trading over a project’s timely completion can accurately be predicted by the members of the relevant function – naturally boosting the accuracy of prediction. However, it is most interesting how GPM works in complete harmony with the work culture – imitating the function of a stock market, yet reinforcing values like recognition over monetary gain.