Score Design for Multi-Criteria Incentivization
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Abstract (arXiv)
We present a framework for designing scores to summarize performance metrics. Our design has two multi-criteria objectives: (1) improving on scores should improve all performance metrics, and (2) achieving pareto-optimal scores should achieve pareto-optimal metrics. We formulate our design to minimize the dimensionality of scores while satisfying the objectives. We give algorithms to design scores, which are provably minimal under mild assumptions on the structure of performance metrics. This framework draws motivation from real-world practices in hospital rating systems, where misaligned scores and performance metrics lead to unintended consequences.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 76 references that are in the map. First posted on arXiv 8 Oct 2024.
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References · 11
- 2021Convex optimizationcited by 2,479
- 2018How Do Classifiers Induce Agents To Invest Effort Strategically?cited by 82
- 2020Maximizing Welfare with Incentive-Aware Evaluation Mechanismscited by 46
- 2020Optimization of Scoring Rulescited by 28
- 2020Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learningcited by 17
- 2015Efficient Inverse Maintenance and Faster Algorithms for Linear Programmingcited by 13
- 2022Optimal Scoring Rules for Multi-dimensional Effortcited by 12
- 2023Counterfactual Prediction Under Outcome Measurement Errorcited by 10
- 2023On the Actionability of Outcome Predictioncited by 9
- 2022Setting Fair Incentives to Maximize Improvementcited by 7
- 2023Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetrycited by 2
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