TY - GEN
T1 - Automated design of scoring rules by learning from examples
AU - Procaccia, Ariel D.
AU - Zohar, Aviv
AU - Rosenschein, Jeffrey S.
PY - 2008
Y1 - 2008
N2 - Scoring rules are a broad and concisely-representable class of voting rules which includes, for example, Plurality and Borda. Our main result asserts that the class of scoring rules, as functions from preferences into candidates, is efficiently learnable in the PAC model. We discuss the applications of this result to automated design of scoring rules. We also investigate possible extensions of our approach, and (along the way) we establish a lemma of independent interest regarding the number of distinct scoring rules.
AB - Scoring rules are a broad and concisely-representable class of voting rules which includes, for example, Plurality and Borda. Our main result asserts that the class of scoring rules, as functions from preferences into candidates, is efficiently learnable in the PAC model. We discuss the applications of this result to automated design of scoring rules. We also investigate possible extensions of our approach, and (along the way) we establish a lemma of independent interest regarding the number of distinct scoring rules.
KW - PAC learning
KW - Voting
UR - http://www.scopus.com/inward/record.url?scp=84899921512&partnerID=8YFLogxK
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AN - SCOPUS:84899921512
SN - 9781605604701
T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
SP - 933
EP - 940
BT - 7th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2008
PB - International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
T2 - 7th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2008
Y2 - 12 May 2008 through 16 May 2008
ER -