Score Design for Multi-Criteria Incentivization

Authors Anmol Kabra, Mina Karzand, Tosca Lechner, Nati Srebro, Serena Wang



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Author Details

Anmol Kabra
  • Toyota Technological Institute at Chicago, IL, USA
Mina Karzand
  • University of California at Davis, CA, USA
Tosca Lechner
  • University of Waterloo, Canada
Nati Srebro
  • Toyota Technological Institute at Chicago, IL, USA
Serena Wang
  • University of California at Berkeley, CA, USA
  • Google, Palo Alto, CA, USA

Acknowledgements

Anmol Kabra thanks Naren Sarayu Manoj and Max Ovsiankin for pointers on convex analysis and geometry.

Cite As Get BibTex

Anmol Kabra, Mina Karzand, Tosca Lechner, Nati Srebro, and Serena Wang. Score Design for Multi-Criteria Incentivization. In 5th Symposium on Foundations of Responsible Computing (FORC 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 295, pp. 8:1-8:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.FORC.2024.8

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.

Subject Classification

ACM Subject Classification
  • Theory of computation → Algorithmic game theory and mechanism design
  • Theory of computation → Computational geometry
Keywords
  • Multi-criteria incentives
  • Score-based incentives
  • Incentivizing improvement
  • Computational geometry

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