Research map / Learning theory and algorithms / Submodular and game algorithms
Balancing Lexicographic Fairness and a Utilitarian Objective With Application to Kidney Exchange
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use hybrid-lexicographic fairness to cap efficiency loss | Kidney exchange clearing on UNOS data · 314 UNOS kidney exchanges | worst-case price of fairness: 0.2 | Helped |
| Use hybrid-lexicographic fairness rather than α-lexicographic fairness | Kidney exchange clearing · Real exchanges with small edge success probability | fraction of maximum highly sensitized utility | Mixed |
| Decrease edge success probability | α-lexicographic and weighted fairness · Failure-aware kidney exchange | worst-case price of fairness | Hurt |
| Include chains and NDDs in the random graph model | Lexicographic fairness · Random graph model | price of fairness: 2/33; β>1/8 | Helped |
| Use α-lexicographic fairness instead of a utilitarian objective | Kidney exchange clearing · Real kidney exchanges | fraction of maximum highly sensitized utility: 0.1 | Helped |
| Use α-lexicographic fairness instead of a utilitarian objective | Kidney exchange clearing · Real kidney exchanges | worst-case price of fairness | Hurt |
| Use weighted fairness instead of a utilitarian objective | Kidney exchange clearing · Real kidney exchanges | worst-case price of fairness | Hurt |
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