A data-driven framework for counting axiom violations across preference distributions, with the claim that trained neural-network rules minimize violations better than traditional multi-winner rules.
Properties of the M allows model depending on the number of alternatives: A warning for an experimentalist
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What Voting Rules Actually Do: A Data-Driven Analysis of Multi-Winner Voting
A data-driven framework for counting axiom violations across preference distributions, with the claim that trained neural-network rules minimize violations better than traditional multi-winner rules.