Manipulation and bribery when aggregating ranked preferences

Ying Zhu; Miroslaw Truszczynski

doi:10.1007/978-3-319-23114-3_6

Manipulation and bribery when aggregating ranked preferences

Ying Zhu, Miroslaw Truszczynski

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Manipulation and bribery have received much attention from the social choice community. We study these concepts for preference formalisms that identify a set of optimal outcomes rather than a single winning outcome. We assume that preferences may be ranked (differ in importance), and we use the Pareto principle adjusted to the case of ranked preferences as the preference aggregation rule. For two important classes of preferences, representing the extreme ends of the spectrum, we provide characterizations of situations when manipulation and bribery is possible, and establish the complexity of the problems to decide that.

Original language	English
Title of host publication	Algorithmic Decision Theory - 4th International Conference, ADT 2015, Proceedings
Editors	Toby Walsh
Pages	86-102
Number of pages	17
DOIs	https://doi.org/10.1007/978-3-319-23114-3_6
State	Published - 2015
Event	4th International Conference on Algorithmic Decision Theory, ADT 2015 - Lexington, United States Duration: Sep 27 2015 → Sep 30 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9346
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International Conference on Algorithmic Decision Theory, ADT 2015
Country/Territory	United States
City	Lexington
Period	9/27/15 → 9/30/15

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-23114-3_6

Cite this

Zhu, Y., & Truszczynski, M. (2015). Manipulation and bribery when aggregating ranked preferences. In T. Walsh (Ed.), Algorithmic Decision Theory - 4th International Conference, ADT 2015, Proceedings (pp. 86-102). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9346). https://doi.org/10.1007/978-3-319-23114-3_6

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author = "Ying Zhu and Miroslaw Truszczynski",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015. Copyright: Copyright 2015 Elsevier B.V., All rights reserved.; 4th International Conference on Algorithmic Decision Theory, ADT 2015 ; Conference date: 27-09-2015 Through 30-09-2015",

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Zhu, Y & Truszczynski, M 2015, Manipulation and bribery when aggregating ranked preferences. in T Walsh (ed.), Algorithmic Decision Theory - 4th International Conference, ADT 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9346, pp. 86-102, 4th International Conference on Algorithmic Decision Theory, ADT 2015, Lexington, United States, 9/27/15. https://doi.org/10.1007/978-3-319-23114-3_6

Manipulation and bribery when aggregating ranked preferences. / Zhu, Ying; Truszczynski, Miroslaw.
Algorithmic Decision Theory - 4th International Conference, ADT 2015, Proceedings. ed. / Toby Walsh. 2015. p. 86-102 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9346).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Manipulation and bribery have received much attention from the social choice community. We study these concepts for preference formalisms that identify a set of optimal outcomes rather than a single winning outcome. We assume that preferences may be ranked (differ in importance), and we use the Pareto principle adjusted to the case of ranked preferences as the preference aggregation rule. For two important classes of preferences, representing the extreme ends of the spectrum, we provide characterizations of situations when manipulation and bribery is possible, and establish the complexity of the problems to decide that.

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Zhu Y, Truszczynski M. Manipulation and bribery when aggregating ranked preferences. In Walsh T, editor, Algorithmic Decision Theory - 4th International Conference, ADT 2015, Proceedings. 2015. p. 86-102. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-23114-3_6

Manipulation and bribery when aggregating ranked preferences

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