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Incentive compatible regression learning

By Ofer Dekel, Felix Fischer and Ariel D. Procaccia

Abstract

We initiate the study of incentives in a general machine learning framework. We focus on a gametheoretic regression learning setting where private information is elicited from multiple agents, which are interested in different distributions over the sample space; this conflict potentially gives rise to untruthfulness on the part of the agents. We show that in the restricted but important case when distributions are degenerate, and under mild assumptions, agents are motivated to tell the truth. In a more general setting, we study the power and limitations of mechanisms without payments. We finally establish that, in the general setting, the VCG mechanism goes a long way in guaranteeing truthfulness and efficiency

Topics: machine learning, regression, mechanism design
Year: 2008
OAI identifier: oai:CiteSeerX.psu:10.1.1.134.1543
Provided by: CiteSeerX
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