用改进反馈计算投票规则，发现多数规则仍不可行
Computing Voting Rules with Improvement Feedback
- 通过增量调整反馈学习投票规则，突破传统配对比较局限
- 多数规则在改进反馈下仍无法计算，仅多数票制可学
- 适合研究偏好聚合与社会选择的理论学者参考
在不完全或受限反馈下的偏好聚合是社会选择及相关领域中的基础问题。尽管已有研究在配对比较中建立了强不可能性结果，本文将探究扩展至改进反馈场景，即选民表达增量调整而非完整偏好。我们给出了可在改进反馈下计算的位置评分规则的完整刻画。有趣的是，虽然多数票制在改进反馈下可学习（而配对反馈中不可），但许多其他位置评分规则仍面临强不可能性结果。此外，我们证明改进反馈不足以计算任何满足康多塞一致性的规则。实验结果进一步揭示了改进反馈在偏好聚合中的实际意义。
原文摘要 · Abstract (English)
Aggregating preferences under incomplete or constrained feedback is a fundamental problem in social choice and related domains. While prior work has established strong impossibility results for pairwise comparisons, this paper extends the inquiry to improvement feedback, where voters express incremental adjustments rather than complete preferences. We provide a complete characterization of the positional scoring rules that can be computed given improvement feedback. Interestingly, while plurality is learnable under improvement feedback--unlike with pairwise feedback--strong impossibility results persist for many other positional scoring rules. Furthermore, we show that improvement feedback, unlike pairwise feedback, does not suffice for the computation of any Condorcet-consistent rule. We complement our theoretical findings with experimental results, providing further insights into the practical implications of improvement feedback for preference aggregation.
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