53 research outputs found
A Collaborative Mechanism for Crowdsourcing Prediction Problems
Machine Learning competitions such as the Netflix Prize have proven
reasonably successful as a method of "crowdsourcing" prediction tasks. But
these competitions 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 participants can modify this hypothesis by wagering on an
update. The critical incentive property is that a participant will profit an
amount that scales according to how much her update improves performance on a
released test set.Comment: Full version of the extended abstract which appeared in NIPS 201
Partial Verification as a Substitute for Money
Recent work shows that we can use partial verification instead of money to
implement truthful mechanisms. In this paper we develop tools to answer the
following question. Given an allocation rule that can be made truthful with
payments, what is the minimal verification needed to make it truthful without
them? Our techniques leverage the geometric relationship between the type space
and the set of possible allocations.Comment: Extended Version of 'Partial Verification as a Substitute for Money',
AAAI 201
- …