2026-07-23T15:48:58Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:20091 2024-06-10T05:20:53Z ddc:004 open_access

Score Design for Multi-Criteria Incentivization Kabra, Anmol Karzand, Mina Lechner, Tosca Srebro, Nati Wang, Serena Multi-criteria incentives Score-based incentives Incentivizing improvement Computational geometry 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Anmol Kabra and Mina Karzand and Tosca Lechner and Nati Srebro and Serena Wang 2024 Is Part Of LIPIcs, Volume 295, 5th Symposium on Foundations of Responsible Computing (FORC 2024) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.FORC.2024.8 urn:nbn:de:0030-drops-200919 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2024.8 eng https://creativecommons.org/licenses/by/4.0/legalcode