Preference elicitation and robust winner determination for single- and multi-winner social choice

DOI10.1016/j.artint.2019.103203zbMath1482.91084OpenAlexW2987977793WikidataQ126812904 ScholiaQ126812904MaRDI QIDQ2287202

Craig Boutilier, Tyler Lu

Publication date: 20 January 2020

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.artint.2019.103203

zbMATH Keywords

decision theoryminimax regretvotingsocial choicerobust optimizationpreference elicitationpartial preferences

Mathematics Subject Classification ID

Voting theory (91B12) Social choice (91B14)

Uses Software

• PrefLib
• PRIME Decisions

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Finding a collective set of items: from proportional multirepresentation to group recommendation
• Taking the final step to a full dichotomy of the possible winner problem in pure scoring rules
• Reaching a joint decision with minimal elicitation of voter preferences
• A characterization of the maximin rule in the context of voting
• The generalized maximum coverage problem
• The threshold aggregation
• Min-max and min-max regret versions of combinatorial optimization problems: A survey
• Robust discrete optimization and its applications
• Minmax regret solutions for minimax optimization problems with uncertainty
• On the evaluation of election outcomes under uncertainty
• Towards a dichotomy for the possible winner problem in elections based on scoring rules
• Justified representation in approval-based committee voting
• On the complexity of achieving proportional representation
• Constraint-based optimization and utility elicitation using the minimax decision criterion
• Properties of multiwinner voting rules
• On the Computation of Fully Proportional Representation
• Determining Possible and Necessary Winners Given Partial Orders
• Vote Elicitation with Probabilistic Preference Models: Empirical Estimation and Cost Tradeoffs
• NON-NULL RANKING MODELS. I
• Aggregating Partially Ordered Preferences
• Eliciting Single-Peaked Preferences Using Comparison Queries
• The facility location problem with general cost functions
• Chamberlin--Courant Rule with Approval Ballots: Approximating the MaxCover Problem with Bounded Frequencies in FPT Time
• Incomplete Information and Communication in Voting
• Handbook of Computational Social Choice
• A Short Introduction to Computational Social Choice
• Segmentation problems

This page was built for publication: Preference elicitation and robust winner determination for single- and multi-winner social choice