Aggregating Quantitative Relative Judgments: From Social Choice to Ranking Prediction

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

Keywords: Algorithmic Game Theory, Ranking and Preference Learning

TL;DR: We propose new rules for aggregating quantitative relative judgments, study their computational complexity, and evaluate their empirical performance.

Abstract: 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.

Primary Area: Algorithmic game theory

Submission Number: 1223