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Truthful Aggregation of Budget Proposals with Proportionality Guarantees

AAAI 2022 · Learning theory and algorithms › Submodular and game algorithms · approach: Fair division of indivisible goods · 8 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use Uniform Phantom rather than proportional aggregationTruthful budget aggregation with 2 projects · Budget aggregation, 2 projectsworst-case ℓ1-loss: 1/2Helped
Use Piecewise Uniform moving phantomsTruthful budget aggregation with 3 projects · Budget aggregation, 3 projectsworst-case ℓ1-loss: (2/3+ε)Helped
Use Independent Markets instead of Piecewise UniformMoving phantom mechanisms · Budget aggregation, 3 projectsworst-case ℓ1-loss: 0.6862 vs. (2/3+ε)Hurt
Require truthfulnessBudget aggregation mechanisms · Budget aggregation, m projectsworst-case ℓ1-loss lower bound: 1/2Hurt
Restrict mechanisms to moving phantom mechanismsTruthful budget aggregation · Budget aggregation, m projectsworst-case ℓ1-loss lower bound: 1-1/mHurt
Use Piecewise Uniform or Independent Markets as the number of projects growsProportional moving phantom mechanisms · Budget aggregation, m projects, m ≥ 8worst-case ℓ1-loss lower bound: 2- \frac{8}{m^{1/3}}Hurt
Maximize utilitarian social welfareUtilitarian budget aggregation mechanisms · Budget aggregation, m projectsworst-case ℓ1-loss: 2-\frac{4}{m+1}Hurt

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