Research map / Learning theory and algorithms / Submodular and game algorithms
Comparing Election Methods Where Each Voter Ranks Only Few Candidates
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use randomized rather than deterministic elicitation | Borda approximation · 1D and MMM preference distributions | approximation ratio | Helped |
| Use randomized rather than deterministic elicitation | Borda approximation · IC and SPIC preference distributions | approximation ratio | Hurt |
| Use randomized rather than deterministic elicitation | Minimax approximation · Preference distributions tested | approximation ratio | Mixed |
| Increase the number of randomly ranked candidates per voter | Randomized Minimax approximation · Minimax, m=50 | approximation ratio | Helped |
| Use Deter-avg rather than the other two deterministic heuristics (ablation) | Borda approximation · Preference distributions tested | approximation ratio | No clear effect |
| Use the deterministic algorithm rather than either tested natural heuristic (ablation) | Minimax approximation · Preference distributions tested | approximation ratio | Helped |
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