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Autonomous Agents and Multi-Agent Systems

, Volume 33, Issue 4, pp 383–402 | Cite as

High-multiplicity election problems

  • Zack FitzsimmonsEmail author
  • Edith Hemaspaandra
Article

Abstract

The computational study of elections generally assumes that the preferences of the electorate come in as a list of votes. Depending on the context, it may be much more natural to represent the list succinctly, as the distinct votes of the electorate and their counts, i.e., high-multiplicity representation. We consider how this representation affects the complexity of election problems. High-multiplicity representation may be exponentially smaller than standard representation, and so many polynomial-time algorithms for election problems in standard representation become exponential-time. Surprisingly, for polynomial-time election problems, we are often able to either adapt the same approach or provide new algorithms to show that these problems remain polynomial-time in the high-multiplicity case; this is in sharp contrast to the case where each voter has a weight, where the complexity usually increases. In the process we explore the relationship between high-multiplicity scheduling and manipulation of high-multiplicity elections. And we show that for any fixed set of job lengths, high-multiplicity scheduling on uniform parallel machines is in P, which was previously known for only two job lengths. We did not find any natural case where a polynomial-time election problem does not remain in P when moving to high-multiplicity representation. However, we found one natural NP-hard election problem where the complexity does increase, namely winner determination for Kemeny elections.

Keywords

Computational social choice Elections Manipulative actions High-multiplicity representation Scheduling 

Notes

Acknowledgements

We thank the referees for their many helpful comments and suggestions. And we thank the AAAI-17 Student Abstract referees for their helpful comments and suggestions on our preliminary work on this topic [16]. This work was supported in part by a National Science Foundation Graduate Research Fellowship under NSF Grant No. DGE-1102937.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceCollege of the Holy CrossWorcesterUSA
  2. 2.Department of Computer ScienceRochester Institute of TechnologyRochesterUSA

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