Research map / Learning theory and algorithms / Submodular and game algorithms
A Model of Winners Allocation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use weakly additive utility functions for both communities | USWO winners allocation · USWO-WA-(f1, f2) | computational complexity | Helped |
| Use AV or CSAV for one community and a weakly additive utility for the other | ESWO/NSWO winners allocation · ESWO/NSWO-WA-(f1, f2) | computational complexity | Helped |
| Use SAV or MSAV utilities for the welfare objectives | ESWO/NSWO winners allocation · ESWO/NSWO-WA-(f1, f2) | computational complexity | Hurt |
| Restrict both elections to voter-interval and candidate-interval domains | SAV/MSAV ESWO/NSWO winners allocation · VI and CI elections | computational complexity | No clear effect |
| Compute PEF, REF, or PO allocations with weakly additive utilities | Winners allocation · PEF/REF/PO-WA-(f1, f2) | computational complexity | Helped |
| Use weakly additive utilities and require envy-freeness | EF winners allocation · EF-WA-(f1, f2) | computational complexity | Helped |
| Use a utility for which committee selection is NP-hard | REF winners allocation · REF-WA-(f1, f2) | computational complexity | Hurt |
| Parameterize egalitarian or Nash optimization by shared candidates | Weakly additive winners allocation · ESWO/NSWO-WA-(f1, f2) | parameterized complexity | Helped |
| Parameterize utilitarian optimization by total votes | Winners allocation with utility functions from Table 1 · USWO-WA-(f1, f2) | parameterized complexity | Helped |
| Parameterize egalitarian or Nash optimization by allocated candidates | Weakly additive winners allocation · ESWO/NSWO-WA-(f1, f2) | parameterized complexity | Helped |
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