Communications of the ACM
Mixtures of distance-based models for ranking data
Computational Statistics & Data Analysis
Vote elicitation: complexity and strategy-proofness
Eighteenth national conference on Artificial intelligence
Communication complexity of common voting rules
Proceedings of the 6th ACM conference on Electronic commerce
Supervised Ordering — An Empirical Survey
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A Short Introduction to Computational Social Choice
SOFSEM '07 Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science
Determining possible and necessary winners under common voting rules given partial orders
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Vote and aggregation in combinatorial domains with structured preferences
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Practical voting rules with partial information
Autonomous Agents and Multi-Agent Systems
Robust approximation and incremental elicitation in voting protocols
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Communication complexity of approximating voting rules
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Multi-winner social choice with incomplete preferences
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Efficient vote elicitation under candidate uncertainty
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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A variety of preference aggregation schemes and voting rules have been developed in social choice to support group decision making. However, the requirement that participants provide full preference information in the form of a complete ranking of alternatives is a severe impediment to their practical deployment. Only recently have incremental elicitation schemes been proposed that allow winners to be determined with partial preferences; however, while minimizing the amount of information provided, these tend to require repeated rounds of interaction from participants. We propose a probabilistic analysis of vote elicitation that combines the advantages of incremental elicitation schemes--namely, minimizing the amount of information revealed--with those of full information schemes--single (or few) rounds of elicitation. We exploit distributional models of preferences to derive the ideal ranking threshold k, or number of top candidates each voter should provide, to ensure that either a winning or a high quality candidate (as measured by max regret) can be found with high probability. Our main contribution is a general empirical methodology, which uses preference profile samples to determine the ideal ranking threshold for many common voting rules. We develop probably approximately correct (PAC) sample complexity results for one-round protocols with any voting rule and demonstrate the efficacy of our approach empirically on one-round protocols with Borda scoring.