Papers › Aggregating Quantitative Relative Judgments: From Social Choice to Ranking Prediction

Aggregating Quantitative Relative Judgments: From Social Choice to Ranking Prediction

7 Oct 2024arXiv:2410.05550links table onlyarchive 2025-07-28

Yixuan Even Xu, Hanrui Zhang, Yu Cheng, Vincent Conitzer

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Quantitative Relative Judgment Aggregation (QRJA) is a new research topic in (computational) social choice. In the QRJA model, agents provide judgments on the relative quality of different candidates, and the goal is to aggregate these judgments across all agents. In this work, our main conceptual contribution is to explore the interplay between QRJA in a social choice context and its application to ranking prediction. We observe that in QRJA, judges do not have to be people with subjective opinions; for example, a race can be viewed as a "judgment" on the contestants' relative abilities. This allows us to aggregate results from multiple races to evaluate the contestants' true qualities. At a technical level, we introduce new aggregation rules for QRJA and study their structural and computational properties. We evaluate the proposed methods on data from various real races and show that QRJA-based methods offer effective and interpretable ranking predictions.

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1ran · our draft was wrong
1ran · fixture could not drive it
2unverified

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QRJA YixuanEvenXu/quantitative-judgment-aggregation/code/qrja.py official repository ran · fixture could not drive it fingerprinted MIT (permissive) · 92c2ab23ff3b972b · report
QRJAL2 YixuanEvenXu/quantitative-judgment-aggregation/code/qrja.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 4891c6f2ede3be64 · report
QRJAL1 YixuanEvenXu/quantitative-judgment-aggregation/code/qrja.py official repository unverified MIT (permissive) · eb49038616b0cc18 · report
QRJALp YixuanEvenXu/quantitative-judgment-aggregation/code/qrja.py official repository unverified MIT (permissive) · f6ac67ed888e47d8 · report

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