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Ariel D. Procaccia, Jeffrey S. Rosenschein, and
Gal A. Kaminka. On the Robustness of Preference Aggregation in Noisy Environments. In Proceedings of the Sixth
International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-07), 2007.
A previous version
of this paper appeared in Proceedings of the 1st International Workshop on Computational Social Choice, December 2006
In an election held in a noisy environment, agents may unintentionally perturb the outcome by communicating faulty preferences. We investigate this setting by introducing a theoretical model of noisy preference aggregation and formally defining the (worst-case) robustness of a voting rule. We use our model to analytically bound the robustness of various prominent rules. The results show that the robustness of voting rules is diverse, with some rules positioned at both ends of the spectrum. These results allow selection of voting rules that support preference aggregation in the face of noise.
@InProceedings{aamas07ariel,
author = {Ariel D. Procaccia and Jeffrey S. Rosenschein and Gal A. Kaminka},
title = {On the Robustness of Preference Aggregation in Noisy Environments},
OPTcrossref = {},
OPTkey = {},
booktitle = AAMAS-07,
OPTpages = {In press},
year = {2007},
abstract = {In an election held in a noisy environment, agents may unintentionally
perturb the outcome by communicating faulty preferences. We investigate this
setting by introducing a theoretical model of noisy preference aggregation and
formally defining the (worst-case) robustness of a voting rule. We use our model
to analytically bound the robustness of various prominent rules. The results
show that the robustness of voting rules is diverse, with some rules positioned
at both ends of the spectrum. These results allow selection of voting rules that
support preference aggregation in the face of noise.},
wwwnote = {A previous version of this paper appeared in Proceedings of the 1st International Workshop on Computational Social Choice, December 2006},
OPTeditor = {},
OPTvolume = {},
OPTnumber = {},
OPTseries = {},
OPTaddress = {},
OPTmonth = {},
OPTorganization = {},
OPTpublisher = {},
OPTannote = {}
}
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