Score Design for Multi-Criteria Incentivization
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916428582486016 |
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| author | Kabra, Anmol Karzand, Mina Lechner, Tosca Srebro, Nathan Wang, Serena |
| author_facet | Kabra, Anmol Karzand, Mina Lechner, Tosca Srebro, Nathan Wang, Serena |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_06290 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Score Design for Multi-Criteria Incentivization Kabra, Anmol Karzand, Mina Lechner, Tosca Srebro, Nathan Wang, Serena Computers and Society Computational Geometry Machine Learning 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. |
| title | Score Design for Multi-Criteria Incentivization |
| topic | Computers and Society Computational Geometry Machine Learning |
| url | https://arxiv.org/abs/2410.06290 |