Research map / Learning theory and algorithms / Fairness
Fair Sortition Made Transparent
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| quantize the maximally fair panel distribution into a uniform lottery of m=1000 panels (IP-Maximin) | Maximin-optimal panel distribution · 11 real-world panel instances | Maximin: at most 2.1/m | No clear effect |
| use Pipage or Beck-Fiala rounding instead of IP to build the lottery | uniform lottery construction · 11 real-world panel instances | Maximin: sf(a): -1.1/m -> -3.1/m (Pipage), -6.1/m (Beck-Fiala) | Hurt |
| quantize into a uniform lottery (Pipage, Beck-Fiala, IP-Marginals) | Leximin-optimal marginals · sf(a), m=1000 | marginal discrepancy | No clear effect |
| increase lottery size m | uniform lottery · theory | marginal discrepancy bound | Helped |
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