Automated design of scoring rules by learning from examples
- Ariel D. Procaccia ,
- Aviv Zohar ,
- Jeffrey S. Rosenschein
AAMAS '08: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems |
Published by International Foundation for Autonomous Agents and Multiagent Systems
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.
(also appeared in COMSOC 2006).