Aggregating Quantitative Relative Judgments: From Social Choice to Ranking Prediction

TL;DR

This paper introduces Quantitative Relative Judgment Aggregation (QRJA), a novel approach in social choice that combines judgment aggregation with ranking prediction, demonstrating its effectiveness on real race data.

Contribution

It explores the connection between QRJA and ranking prediction, introduces new aggregation rules, and evaluates their performance on real-world race data.

Findings

QRJA-based methods produce effective rankings

New aggregation rules have favorable computational properties

QRJA offers interpretable ranking predictions

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.