Most Expected Winner: An Interpretation of Winners over Uncertain Voter Preferences
Summary: Introduces MEW for uncertain voters, separating generation from observation to yield a unified incomplete+probabilistic voting profile. Proves MEW hardness; tractable RIM/Mallows cases with solvers and pruning; validated on real and synthetic benchmarks. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Haoyue Ping (New York University)
- 2. Julia Stoyanovich (New York University)
BibTeX Citation
@inproceedings{ping_sigmod23,
title = {{Most Expected Winner: An Interpretation of Winners over Uncertain Voter Preferences}},
author = {Ping, Haoyue and Stoyanovich, Julia},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588702},
url = {https://dl.acm.org/doi/10.1145/3588702},
year = {2023}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,795 | A System for Management and Analysis of Preference Data | 2014 | VLDB | 5.4514671e-05 |
| 8,908 | Querying Probabilistic Preferences in Databases | 2017 | PODS | 5.2559789e-05 |
| 13,797 | Supporting Hard Queries over Probabilistic Preferences | 2020 | VLDB | - |
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