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Approximation and Hardness of Shift-Bribery

AAAI 2019 · Learning theory and algorithms › Submodular and game algorithms · approach: Committee voting rules

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Apply LP-based approximation and preprocessing to arbitrary positional scoring rulesShift-Bribery with positional scoring rules and arbitrary prices · Positional scoring rulesapproximation ratio: $(1 + \varepsilon)$Helped
Use an EPTAS for unit-price Borda shift briberyBorda Shift-Bribery · Borda, unit pricesapproximation ratio: $(1 + \varepsilon)$Helped
Use a combinatorial PTAS that targets only the most over-scoring opponentsBorda Shift-Bribery · Borda, unit pricesapproximation ratio: $(1 + \varepsilon)$Helped
Use the improved parameterized algorithmBorda Shift-Bribery · Borda, arbitrary price functionsrunning time: $2^{O(K)}\text{poly}(|I|)$ versus $2^{O(K^2)}\text{poly}(|I|)$Helped
Round an LP basic solution and complete it with arbitrary shiftsBorda Shift-Bribery · Borda, unit pricessolution cost: $\opt(I) + \sqrt{\opt(I)}$Helped
Bribe voters who rank the preferred candidate lowest firstBorda Shift-Bribery · Borda, uniform-all-or-nothing pricessolution cost: $(1.5 \cdot \opt(I) + 1)$Helped
Reduce Densest-$k$-Subgraph to Copeland shift briberyCopeland$^\alpha$-Shift-Bribery · Copeland, unit or all-or-nothing pricesapproximability: $O(\tau(|V_G|^{O(1)})^2)$Hurt
Apply the Gap-ETH reduction for the unit-shift parameterCopeland$^\alpha$-Shift-Bribery · Copeland, unit pricesapproximability: $(2 - \varepsilon) \opt(I)$Hurt
Apply parameterized hardness for the affected-voter parameterCopeland$^\alpha$-Shift-Bribery · Copeland, affected-voter parameterapproximability: any constant factorHurt
Reduce Cubic Vertex Cover to uniform-all-or-nothing Borda shift briberyBorda Shift-Bribery · Borda, uniform-all-or-nothing pricescomputational complexity: \NP-hardHurt

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