Research map / Learning theory and algorithms / Submodular and game algorithms
Communication, Distortion, and Randomness in Metric Voting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Restrict deterministic reports to candidates at k ranking positions | Deterministic k-entry voting rules · Metric voting (n candidates) | worst-case distortion: distortion at least \frac{2n-k}{k} | Hurt |
| Restrict each voter's report to b bits | Deterministic communication-bounded voting rules · Metric voting (n candidates) | worst-case distortion: distortion at least \frac{2n-4}{b} - 1 | Hurt |
| Use the proposed top-k Copeland-style comparison-graph rule | Deterministic top-k voting · Metric voting (n candidates) | worst-case distortion: distortion at most \frac{79n}{k} | Helped |
| Mix Proportional to Squares with Random Dictatorship at the stated probabilities | Randomized first-choice voting · Metric voting (n candidates) | expected distortion: 3-\frac{2}{n} | Helped |
Closest papers by meaning
- An Analysis Framework for Metric Voting based on LP Duality (AAAI 2020 · similarity 0.94)
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- On the Distortion of Committee Election with 1-Euclidean Preferences and Few Distance Queries (AAAI 2025 · similarity 0.91)
- Improved Metric Distortion via Threshold Approvals (AAAI 2024 · similarity 0.90)
- Constant-Factor Distortion Mechanisms for k-Committee Election (AAAI 2025 · similarity 0.88)
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