[...] We are thus grateful to the folks at Google, along with coauthors from NBER and Dartmouth, for publishing some of the first real results of their internal prediction market. The study covers nearly three years of the operation of an exchange which handled over 70,000 transactions – each conveying a degree of belief on one of almost 300 particular questions, on behalf of 1500 active employees (although nearly 6500 held accounts that were not used.) [...]
All in all, the paper is well worth reading and carries with it quite a bit of food for thought to sustain those debating the utility and applications of prediction markets within the intelligence community. We admit to a growing skepticism regarding the value of the methodology that this study has only served to reinforce. Given the total time, resources, and intellectual energies required to support such an endeavor, these kinds of outcome do not in our view necessarily justify the effort. However, we remain open to the potential that such mechanisms capture effort which might otherwise be entirely undirected, and therefore may create insight where other techniques would not. These remain in our minds open questions, and worthy of further research.
[UPGRADE: "Kent's Imperative" has follow-up post.]
Using Prediction Markets to Track Information Flows: Evidence From Google – (PDF file) – by Bo Cowgill (Google economic analyst), Justin Wolfers (University of Pennsylvania) and Eric Zitzewitz (Dartmouth College)
Read the this post on the same blog from the next day: “This is perhaps the best model of the potential utility of such efforts we have ever seen.”
Thanks, Bo.