Computing Voting Rules with Improvement Feedback
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Abstract (arXiv)
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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References · 8
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2017Deep reinforcement learning from human preferencescited by 3,846
- 2022PEER: A Collaborative Language Modelcited by 86
- 2023Data-Efficient Alignment of Large Language Models with Human Feedback Through Natural Languagecited by 22
- 2024Trajectory Improvement and Reward Learning from Comparative Language Feedbackcited by 17
- 2015Sample Complexity for Winner Prediction in Electionscited by 7
- 2024Computing Voting Rules with Elicited Incomplete Votescited by 5
- 2019Comparing Election Methods Where Each Voter Ranks Only Few Candidatescited by 4
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