Score Design for Multi-Criteria Incentivization
Anmol Kabra, Mina Karzand, Tosca Lechner, Nathan Srebro, Serena Wang

TL;DR
This paper introduces a framework for designing multi-criteria scores that accurately reflect performance metrics, ensuring improvements in scores correspond to metric improvements and vice versa, inspired by hospital rating systems.
Contribution
The paper proposes a novel score design framework with algorithms that produce minimal-dimensional scores aligned with multiple performance objectives.
Findings
Algorithms for score design are provably minimal under mild assumptions.
Scores improve all metrics when scores improve, ensuring alignment.
Framework is motivated by real-world hospital rating practices.
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
