Eliciting Single-Peaked Preferences Using Comparison Queries
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Abstract (arXiv)
Voting is a general method for aggregating the preferences of multiple agents. Each agent ranks all the possible alternatives, and based on this, an aggregate ranking of the alternatives (or at least a winning alternative) is produced. However, when there are many alternatives, it is impractical to simply ask agents to report their complete preferences. Rather, the agents preferences, or at least the relevant parts thereof, need to be elicited. This is done by asking the agents a (hopefully small) number of simple queries about their preferences, such as comparison queries, which ask an agent to compare two of the alternatives. Prior work on preference elicitation in voting has focused on the case of unrestricted preferences. It has been shown that in this setting, it is sometimes necessary to ask each agent (almost) as many queries as would be required to determine an arbitrary ranking of the alternatives. In contrast, in this paper, we focus on single-peaked preferences. We show that such preferences can be elicited using only a linear number of comparison queries, if either the order with respect to which preferences are single-peaked is known, or at least one other agents complete preferences are known. We show that using a sublinear number of queries does not suffice. We also consider the case of cardinally single-peaked preferences. For this case, we show that if the alternatives cardinal positions are known, then an agents preferences can be elicited using only a logarithmic number of queries; however, we also show that if the cardinal positions are not known, then a sublinear number of queries does not suffice. We present experimental results for all elicitation algorithms. We also consider the problem of only eliciting enough information to determine the aggregate ranking, and show that even for this more modest objective, a sublinear number of queries per agent does not suffice for known ordinal or unknown cardinal positions. Finally, we discuss whether and how these techniques can be applied when preferences are almost single-peaked.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jan 2014.
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Citing papers per year: 2022: 3; 2023: 0; 2024: 2; 2025: 5; 2026: 2.
12 papers.
- 2025LLM-Powered Preference Elicitation in Combinatorial Assignmentcited by 7
- 2022Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learningcited by 5
- 2025Drawing a Map of Electionscited by 4
- 2022Machine Learning-Powered Course Allocationcited by 3
- 2022Sampling-Based Winner Prediction in District-Based Electionscited by 2
- 2025Diversity of Structured Domains via k-Kemeny Scorescited by 2
- 2024FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers' Preference Elicitationcited by 1
- 2025What Voting Rules Actually Do: A Data-Driven Analysis of Multi-Winner Votingcited by 1
- 2024Efficient Inference and Computation of Optimal Alternatives for Preference Languages Based On Lexicographic Modelscited by 0
- 2025Preference Elicitation for Multi-objective Combinatorial Optimization with Active Learning and Maximum Likelihood Estimationcited by 0
- 2026Procedural Fairness in Multi-Agent Banditscited by 0
- 2026Preference Elicitation for Policy Optimization and Application to Aligning Heart Transplantation with Human Valuescited by 0
- 2026Preference Elicitation for Policy Optimization and Application to Aligning Heart Transplantation with Human Values29 Jul 2026
- 2026Procedural Fairness in Multi-Agent Bandits15 Jan 2026
- 2025Diversity of Structured Domains via k-Kemeny Scores19 Sep 2025
- 2025What Voting Rules Actually Do: A Data-Driven Analysis of Multi-Winner Voting8 Aug 2025
- 2025Drawing a Map of Elections4 Apr 2025
- 2025Preference Elicitation for Multi-objective Combinatorial Optimization with Active Learning and Maximum Likelihood Estimation14 Mar 2025
- 2025LLM-Powered Preference Elicitation in Combinatorial Assignment14 Feb 2025
- 2024Efficient Inference and Computation of Optimal Alternatives for Preference Languages Based On Lexicographic Models31 Oct 2024
- 2024FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers' Preference Elicitation10 Mar 2024
- 2022Machine Learning-Powered Course Allocation3 Oct 2022
- 2022Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning16 May 2022
- 2022Sampling-Based Winner Prediction in District-Based Elections28 Feb 2022