Research map / Learning theory and algorithms / Submodular and game algorithms
An Analysis of Approval-Based Committee Rules for 2D-Euclidean Elections
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Require each voter to approve a fixed number of nearest candidates (10-nearest) instead of using a fixed approval radius | Approval-based rules · 2D-Euclidean simulated elections | spatial distribution of selected candidates | Hurt |
| Use a fixed approval radius (0.7 or 1) rather than requiring a fixed number of approvals | Approval-based rules · 2D-Euclidean simulated elections | spatial distribution of selected candidates | Helped |
| Use radius-[0, 3] rather than [1, 100]-nearest approval radii | Approval-based rules · Overlapping squares model | selection of candidates near voters | Helped |
| Compare PAV with Phragmén’s Sequential rule | PAV and Phragmén’s Sequential rule · All simulated settings | spatial distribution of selected candidates | No clear effect |
| Use CC to select a committee | Approval Chamberlin–Courant (CC) · Simulated elections | coverage of candidate and voter views | Helped |
| Use MAV rather than a rule with less diverse outcomes | Minimax Approval Voting (MAV) · Uniform square model | coverage of candidate and voter views | Helped |
| Use AV for individual excellence under fixed-radius approval models | Multiwinner Approval Voting (AV) · Uniform square model | selection of central, similar candidates | Hurt |
| Use ordinal-like approval protocols (nearest-[1, 100] or radius-[0, 3]) rather than other approval protocols for AV | Multiwinner Approval Voting (AV) · Uniform square model | selection of central, similar candidates | Helped |
| Use the first phase of Rule X rather than Phragmén’s Sequential rule | Rule X (first phase) · Asymmetric Gaussians model | similarity to AV’s spatial distribution | Mixed |
| Give candidates in the smaller Gaussian radius 1 and candidates in the larger Gaussian radius 1.5 | PAV and Phragmén’s Sequential rule · Asymmetric Gaussians model, radius-{1, 1.5} | selection of candidates between Gaussian centers | Hurt |
| Swap the candidate radii to 1.5 for the larger Gaussian and 1 for the smaller Gaussian | PAV and Phragmén’s Sequential rule · Asymmetric Gaussians model, radius-{1.5, 1} | selection of candidates between Gaussian centers | Helped |
Closest papers by meaning
- Participation Incentives in Approval-Based Committee Elections (AAAI 2024 · similarity 0.90)
- On the Distortion of Committee Election with 1-Euclidean Preferences and Few Distance Queries (AAAI 2025 · similarity 0.88)
- Spatial Voting with Incomplete Voter Information (AAAI 2024 · similarity 0.88)
- Refined Characterizations of Approval-Based Committee Scoring Rules (AAAI 2024 · similarity 0.87)
- Maintaining Proportional Committees with Dynamic Candidate Sets (ICML 2025 · similarity 0.87)
- Verifying Proportionality in Temporal Voting (AAAI 2025 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.