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Robust Approximation and Incremental Elicitation in Voting Protocols
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| Venue: | PROCEEDINGS OF THE TWENTY-SECOND INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE |
| Citations: | 29 - 13 self |
BibTeX
@MISC{Lu_robustapproximation,
author = {Tyler Lu and Craig Boutilier},
title = {Robust Approximation and Incremental Elicitation in Voting Protocols},
year = {}
}
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Abstract
While voting schemes provide an effective means for aggregating preferences, methods for the effective elicitation of voter preferences have received little attention. We address this problem by first considering approximate winner determination when incomplete voter preferences are provided. Exploiting natural scoring metrics, we use max regret to measure the quality or robustness of proposed winners, and develop polynomial time algorithms for computing the alternative with minimax regret for several popular voting rules. We then show how minimax regret can be used to effectively drive incremental preference/vote elicitation and devise several heuristics for this process. Despite worst-case theoretical results showing that most voting protocols require nearly complete voter preferences to determine winners, we demonstrate the practical effectiveness of regret-based elicitation for determining both approximate and exact winners on several real-world data sets.
Keyphrases
voting protocol incremental elicitation robust approximation minimax regret polynomial time algorithm several heuristic max regret complete voter preference worst-case theoretical result voter preference effective mean incomplete voter preference proposed winner effective elicitation little attention several real-world data set practical effectiveness incremental preference vote elicitation several popular voting rule regret-based elicitation exact winner approximate winner determination
