Using Prediction Markets to Incentivize and Measure Collective Knowledge   Production
  We present a mechanism design, coupling an online collaboration software and a prediction market, which allows tracking down the very roots of individual incentives, actions and how these behaviors influence collective intelligence in terms of knowledge production as a public good. We show that the incentive mechanism efficiently engages users without further governance structure, and doesn't crowd out intrinsic motivation. Furthermore, it enables a powerful and robust creative destruction process, which helps quickly filter out irrelevant knowledge. While still at an early stage, this mechanism design can not only bring insights for knowledge production organization design, but also has the potential to illuminate the fundamental mechanisms underlying the emergence of collective intelligence. 
Thomas Maillart, and Didier Sornette
cs.GT