Improved Metric Distortion for Deterministic Social Choice Rules
Improved Metric Distortion for Deterministic Social Choice Rules
复制标题
改进确定性社会选择规则的度量失真
DOI:
10.1145/3328526.3329550
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发表时间:
2019
期刊:
影响因子:
--
通讯作者:
Wang, Kangning
中科院分区:
文献类型:
--
作者:
Munagala, Kamesh;Wang, Kangning
In this paper, we study the metric distortion of deterministic social choice rules that choose a winning candidate from a set of candidates based on voter preferences. Voters and candidates are located in an underlying metric space. A voter has cost equal to her distance to the winning candidate. Ordinal social choice rules only have access to the ordinal preferences of the voters that are assumed to be consistent with the metric distances. Our goal is to design an ordinal social choice rule with minimum distortion, which is the worst-case ratio, over all consistent metrics, between the social cost of the rule and that of the optimal omniscient rule with knowledge of the underlying metric space. The distortion of the best deterministic social choice rule was known to be between 3 and 5. It had been conjectured that any rule that only looks at the weighted tournament graph on the candidates cannot have distortion better than 5. In our paper, we disprove it by presenting a weighted tournament rule with distortion of 4.236. We design this rule by generalizing the classic notion of uncovered sets, and further show that this class of rules cannot have distortion better than 4.236. We then propose a new voting rule, via an alternative generalization of uncovered sets. We show that if a candidate satisfying the criterion of this voting rule exists, then choosing such a candidate yields a distortion bound of 3, matching the lower bound. We present a combinatorial conjecture that implies distortion of, and verify it for small numbers of candidates and voters by computer experiments. Using our framework, we also show that selecting any candidate guarantees distortion of at most 3 when the weighted tournament graph is cyclically symmetric.
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DOI:
--
发表时间:
2017
期刊:
AAAI Conference on Artificial Intelligence
影响因子:
--
作者:
Stephen Gross;Elliot Anshelevich;Lirong Xia
通讯作者:
Lirong Xia
DOI:
10.1609/aaai.v32i1.11469
发表时间:
2017
期刊:
SIAM J. Comput.
影响因子:
--
作者:
Yu Cheng;S. Dughmi;D. Kempe
通讯作者:
D. Kempe
影响因子:
14.4
作者:
I. Caragiannis;Ariel D. Procaccia
通讯作者:
Ariel D. Procaccia
DOI:
10.1145/3033274.3085155
发表时间:
2017
期刊:
Proceedings of the 2017 ACM Conference on Economics and Computation
影响因子:
--
作者:
Yu Cheng;S. Dughmi;D. Kempe
通讯作者:
D. Kempe
DOI:
10.1145/3230654.3230658
发表时间:
2018
期刊:
Systems and Computation
影响因子:
--
作者:
Goel, Ashish;Hulett, Reyna;Krishnaswamy, Anilesh K.
通讯作者:
Krishnaswamy, Anilesh K.