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On the Randomized Metric Distortion Conjecture
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In the single winner determination problem, we have n voters and m candidates and each voter j incurs a cost c(i, j) if candidate i is chosen. Our objective is to choose a candidate that minimizes the expected total cost incurred by the voters; however as we only have access to the agents' preference rankings over the outcomes, a loss of efficiency is inevitable. This loss of efficiency is quantified by distortion. We give an instance of the metric single winner determination problem for which any randomized social choice function has distortion at least 2.063164. This disproves the long-standing conjecture that there exists a randomized social choice function that has a worst-case distortion of at most 2.
Forward citations
Cited by 4 Pith papers
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Metric Distortion for Tournament Voting and Beyond
Deterministic tournament rules have metric distortion between 3.1128 and 3.9312, and deterministic k-tournament rules approach distortion 3 while randomized 3-tournament rules can beat 3.
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Constant-Factor Distortion Mechanisms for $k$-Committee Election
First constant-factor distortion mechanisms for Top-l k-committee election using only O(log k log n) value queries per agent or polylog queries in total.
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Distortion of Metric Voting with Bounded Randomness
Constant-randomness voting can achieve metric distortion below 3, breaking the deterministic barrier with only a fixed-size uniform lottery.
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Bi-Criteria Metric Distortion
In line metrics, a constant-size committee can achieve the same cost as the optimal single winner, bypassing the factor-3 barrier that applies to any single winner.
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