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Incentive Compatible Regression Learning

Authors Ofer Dekel, Felix Fischer, Ariel D. Procaccia



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Author Details

Ofer Dekel
Felix Fischer
Ariel D. Procaccia

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Ofer Dekel, Felix Fischer, and Ariel D. Procaccia. Incentive Compatible Regression Learning. In Computational Social Systems and the Internet. Dagstuhl Seminar Proceedings, Volume 7271, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2007)
https://doi.org/10.4230/DagSemProc.07271.6

Abstract

We initiate the study of incentives in a general machine learning framework. We focus on a game theoretic 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. In the restricted but important case when distributions are degenerate, and under mild assumptions, we show that 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.
Keywords
  • Machine learning
  • regression
  • mechanism design

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