Research map / Learning theory and algorithms / Submodular and game algorithms
Truthful Aggregation of Budget Proposals with Proportionality Guarantees
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Uniform Phantom rather than proportional aggregation | Truthful budget aggregation with 2 projects · Budget aggregation, 2 projects | worst-case ℓ1-loss: 1/2 | Helped |
| Use Piecewise Uniform moving phantoms | Truthful budget aggregation with 3 projects · Budget aggregation, 3 projects | worst-case ℓ1-loss: (2/3+ε) | Helped |
| Use Independent Markets instead of Piecewise Uniform | Moving phantom mechanisms · Budget aggregation, 3 projects | worst-case ℓ1-loss: 0.6862 vs. (2/3+ε) | Hurt |
| Require truthfulness | Budget aggregation mechanisms · Budget aggregation, m projects | worst-case ℓ1-loss lower bound: 1/2 | Hurt |
| Restrict mechanisms to moving phantom mechanisms | Truthful budget aggregation · Budget aggregation, m projects | worst-case ℓ1-loss lower bound: 1-1/m | Hurt |
| Use Piecewise Uniform or Independent Markets as the number of projects grows | Proportional moving phantom mechanisms · Budget aggregation, m projects, m ≥ 8 | worst-case ℓ1-loss lower bound: 2- \frac{8}{m^{1/3}} | Hurt |
| Maximize utilitarian social welfare | Utilitarian budget aggregation mechanisms · Budget aggregation, m projects | worst-case ℓ1-loss: 2-\frac{4}{m+1} | Hurt |
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