English

Score Design for Multi-Criteria Incentivization

Computers and Society 2024-10-10 v1 Computational Geometry Machine Learning

Abstract

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.

Keywords

Cite

@article{arxiv.2410.06290,
  title  = {Score Design for Multi-Criteria Incentivization},
  author = {Anmol Kabra and Mina Karzand and Tosca Lechner and Nathan Srebro and Serena Wang},
  journal= {arXiv preprint arXiv:2410.06290},
  year   = {2024}
}

Comments

A condensed version of this paper appeared at Foundations of Responsible Computing (FORC) 2024

R2 v1 2026-06-28T19:13:25.268Z