How Should We Score Athletes and Candidates: Geometric Scoring Rules

• 成果类型: Article
• 署名作者: Kondratev, Aleksei Y.; Ianovski, Egor; Nesterov, Alexander S.
• 署名单位: HSE University (National Research University Higher School of Economics)
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2023.2473
• 发表日期: 2024
• 页码: 2507-2525
• 关键词: Social choice; condorcet efficiency; Borda Count; aggregation; selection; performance; Consistency; majority; rankings

摘要

Scoring rules are widely used to rank athletes in sports and candidates in elections. Each position in each individual ranking is worth a certain number of points; the total sum of points determines the aggregate ranking. The question is how to choose a scoring rule for a specific application. First, we derive a one-parameter family with geometric scores that satisfies two principles of independence: once an extremely strong or weak candidate is removed, the aggregate ranking ought to remain intact. This family includes Borda count, generalized plurality (medal count), and generalized antiplurality (threshold rule) as edge cases, and we find which additional axioms characterize these rules. Second, we introduce a one-parameter family with optimal scores: the athletes should be ranked according to their expected overall quality. Finally, using historical data from biathlon, golf, and athletics, we demonstrate how the geometric and optimal scores can simplify the selection of suitable scoring rules, show that these scores closely resemble the actual scores used by the organizers, and provide an explanation for empirical phenomena observed in biathlon and golf tournaments. We see that geometric scores approximate the optimal scores well in events in which the distribution of athletes' performances is roughly uniform.