Using Prediction Markets to Incentivize and Measure Collective Knowledge Production
Thomas Maillart, and Didier Sornette

TL;DR
This paper introduces a novel mechanism combining prediction markets with online collaboration tools to incentivize and measure collective knowledge creation, revealing insights into individual incentives and collective intelligence dynamics.
Contribution
It proposes a new incentive mechanism that effectively promotes knowledge production without additional governance and maintains intrinsic motivation.
Findings
Engages users effectively without extra governance
Filters irrelevant knowledge through creative destruction
Potential to reveal fundamental collective intelligence mechanisms
Abstract
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.
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Taxonomy
TopicsOpen Source Software Innovations · Experimental Behavioral Economics Studies · Auction Theory and Applications
