Approximating optimal social choice under metric preferences
- Submitting institution
-
University of Oxford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2058
- Type
- D - Journal article
- DOI
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10.1016/j.artint.2018.07.006
- Title of journal
- Artificial Intelligence
- Article number
- -
- First page
- 27
- Volume
- 264
- Issue
- -
- ISSN
- 1872-7921
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 11
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper, which first appeared in AAAI�15, introduces a novel approach to evaluating voting rules, by analysing their performance in settings where voters and alternatives are located in a metric space, and we want to select an alternative that minimises the total distance from the voters. The paper asks whether common voting rules (which operate on rankings rather than location data) output approximately optimal alternatives. It established the strongest known approximation guarantees for classic voting rules. The paper popularised the idea of measuring loss using ordinal rather than cardinal information, subsequently explored, e.g., in WINE�16 (https://doi.org/10.1007/978-3-662-54110-4_19), WINE�18 (https://doi.org/10.1007/978-3-030-04612-5_1), IJCAI�19 (https://doi.org/10.24963/ijcai.2019/77).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -