Oral
A Collaborative Mechanism for Crowdsourcing Prediction Problems
Jacob D Abernethy · Rafael Frongillo

Wed Dec 14th 05:10 -- 05:30 PM @ None

Machine Learning competitions such as the Netflix Prize have proven reasonably successful as a method of “crowdsourcing” prediction tasks. But these compe- titions have a number of weaknesses, particularly in the incentive structure they create for the participants. We propose a new approach, called a Crowdsourced Learning Mechanism, in which participants collaboratively “learn” a hypothesis for a given prediction task. The approach draws heavily from the concept of a prediction market, where traders bet on the likelihood of a future event. In our framework, the mechanism continues to publish the current hypothesis, and par- ticipants can modify this hypothesis by wagering on an update. The critical in- centive property is that a participant will profit an amount that scales according to how much her update improves performance on a released test set.

Author Information

Jacob D Abernethy (University of Michigan)
Rafael Frongillo (CU Boulder)

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