Research map / Learning theory and algorithms / Submodular and game algorithms
Temporal Fairness in Multiwinner Voting
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A position/survey paper that proposes a unified conceptual framework for temporal multiwinner voting (outcomes, preferences, observability, sequentiality, solution concepts) and organizes existing work and open research directions.
Problem
Existing work on multiwinner voting over multiple timesteps models repeated elections in very different ways, making results hard to compare and leaving fairness across time poorly systematized.
Approach
Proposes a unified multidimensional framework (outcome structure and constraints, ballot types, static vs dynamic preferences, online vs offline observability, sequentiality, and global/local/frequency-based solution concepts) that positions existing temporal voting models and identifies gaps.
Key results
- No experiments; the paper's contribution is a framework that classifies existing temporal multiwinner voting models along several dimensions.
- Identifies research directions such as temporal extensions of justified representation and structural restrictions on temporal preferences.
- Notes that in perpetual voting with static preferences proportional justified representation is achieved by a simple greedy algorithm, whereas single-shot PJR requires more sophisticated algorithms.
Limitations
- Non-exhaustive survey; the authors acknowledge there may be further dimensions worth exploring.
- No empirical evaluation of the proposed framework or proposed directions.
Experimental setup
Builds on: perpetual voting justified representation sequential committee elections fair scheduling
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- Proportional Aggregation of Preferences for Sequential Decision Making (AAAI 2024 · similarity 0.86)
- Proportional Fairness in Clustering: A Social Choice Perspective (NeurIPS 2024 · similarity 0.86)
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