Communication, Distortion, and Randomness in Metric Voting
David Kempe
摘要
In distortion-based analysis of social choice rules over metric spaces, one assumes that all voters and candidates are jointly embedded in a common metric space. Voters rank candidates by non-decreasing distance. The mechanism, receiving only this ordinal (comparison) information, aims to nonetheless select a candidate approximately minimizing the sum of distances from all voters to the chosen candidate. It is known that while the Copeland rule and related rules guarantee distortion at most 5, many other standard voting rules, such as Plurality, Veto, or k-approval, have distortion growing unboundedly in the number n of candidates. An advantage of Plurality, Veto, or k-approval with small k is that they require less communication from the voters; all deterministic social choice rules known to achieve constant distortion require voters to transmit their complete ranking of all candidates. This motivates our study of the tradeoff between the distortion and the amount of communication in deterministic social choice rules. We show that any one-round deterministic voting mechanism in which each voter communicates only the candidates she ranks in a given set of k positions must have distortion at least 2n-k k ; we give a mechanism achieving an upper bound of O(n/k), which matches the lower bound up to a constant. For more general communication-bounded voting mechanisms, in which each voter communicates b bits of information about her ranking, we show a slightly weaker lower bound of Ω(n/b) on the distortion. For randomized mechanisms, the situation looks much brighter: it is known that Random Dictatorship achieves expected distortion strictly smaller than 3, almost matching a lower bound of 3 -2 n for any randomized mechanism that only receives each voter's top choice. We close this gap, by giving a simple randomized social choice rule which only uses each voter's first choice, and achieves expected distortion 3 -2 n .
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引用它的顶会 Paper14
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- The Smoothed Possibility of Social ChoiceLirong XiaNeurIPS 2020 · 被引用 36 次
- Metric Distortion Bounds for Randomized Social ChoiceMoses Charikar, Prasanna RamakrishnanSODA 2022 · 被引用 18 次
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