Learning voting trees

Authors:
Ariel D. Procaccia;Aviv Zohar;Yoni Peleg;Jeffrey S. Rosenschein
Affiliations:
School of Engineering and Computer Science, The Hebrew University of Jerusalem, Jerusalem, Israel;School of Engineering and Computer Science, The Hebrew University of Jerusalem, Jerusalem, Israel;School of Engineering and Computer Science, The Hebrew University of Jerusalem, Jerusalem, Israel;School of Engineering and Computer Science, The Hebrew University of Jerusalem, Jerusalem, Israel
Venue:
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Year:
2007

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Abstract

Binary voting trees provide a succinct representation for a large and prominent class of voting rules. In this paper, we investigate the PAC-learnability of this class of rules. We show that, while in general a learning algorithm would require an exponential number of samples, if the number of leaves is polynomial in the size of the set of alternatives then a polynomial training set suffices. We apply these results in an emerging theory: automated design of voting rules by learning.