An algorithm for the coalitional manipulation problem under maximin

Michael Zuckerman; Omer Lev; Jeffrey S. Rosenschein

An algorithm for the coalitional manipulation problem under maximin

Michael Zuckerman, Omer Lev, Jeffrey S. Rosenschein

The Rachel and Selim Benin School of Engineering and Computer Science

The Hebrew University of Jerusalem

Research output: Contribution to conference › Paper › peer-review

Abstract

We introduce a new algorithm for the Unweighted Coalitional Manipulation problem under the Maximin voting rule. We prove that the algorithm gives an approximation ratio of 1 2/3 to the corresponding optimization problem. This is an improvement over the previously known algorithm that gave a 2-approximation. We also prove that its approximation ratio is no better than 1 1/2, i.e., there are instances on which a 1 1/2 -approximation is the best the algorithm can achieve. Finally, we prove that no algorithm can approximate the problem better than to the factor of 1 1/2, unless P = NV.

Original language	American English
Pages	793-800
Number of pages	8
State	Published - 1 Jan 2011
Event	10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011 - Taipei, Taiwan, Province of China Duration: 2 May 2011 → 6 May 2011

Conference

Conference	10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011
Country/Territory	Taiwan, Province of China
City	Taipei
Period	2/05/11 → 6/05/11

Keywords

Algorithms
Approximation
Social choice theory

All Science Journal Classification (ASJC) codes

Artificial Intelligence

Cite this

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title = "An algorithm for the coalitional manipulation problem under maximin",

abstract = "We introduce a new algorithm for the Unweighted Coalitional Manipulation problem under the Maximin voting rule. We prove that the algorithm gives an approximation ratio of 1 2/3 to the corresponding optimization problem. This is an improvement over the previously known algorithm that gave a 2-approximation. We also prove that its approximation ratio is no better than 1 1/2, i.e., there are instances on which a 1 1/2 -approximation is the best the algorithm can achieve. Finally, we prove that no algorithm can approximate the problem better than to the factor of 1 1/2, unless P = NV.",

keywords = "Algorithms, Approximation, Social choice theory",

author = "Michael Zuckerman and Omer Lev and Rosenschein, \{Jeffrey S.\}",

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AU - Zuckerman, Michael

AU - Lev, Omer

AU - Rosenschein, Jeffrey S.

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N2 - We introduce a new algorithm for the Unweighted Coalitional Manipulation problem under the Maximin voting rule. We prove that the algorithm gives an approximation ratio of 1 2/3 to the corresponding optimization problem. This is an improvement over the previously known algorithm that gave a 2-approximation. We also prove that its approximation ratio is no better than 1 1/2, i.e., there are instances on which a 1 1/2 -approximation is the best the algorithm can achieve. Finally, we prove that no algorithm can approximate the problem better than to the factor of 1 1/2, unless P = NV.

AB - We introduce a new algorithm for the Unweighted Coalitional Manipulation problem under the Maximin voting rule. We prove that the algorithm gives an approximation ratio of 1 2/3 to the corresponding optimization problem. This is an improvement over the previously known algorithm that gave a 2-approximation. We also prove that its approximation ratio is no better than 1 1/2, i.e., there are instances on which a 1 1/2 -approximation is the best the algorithm can achieve. Finally, we prove that no algorithm can approximate the problem better than to the factor of 1 1/2, unless P = NV.

KW - Algorithms

KW - Approximation

KW - Social choice theory

UR - http://www.scopus.com/inward/record.url?scp=84899409757&partnerID=8YFLogxK

M3 - Paper

SP - 793

EP - 800

T2 - 10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011

Y2 - 2 May 2011 through 6 May 2011

ER -

An algorithm for the coalitional manipulation problem under maximin

Abstract

Conference

Keywords

All Science Journal Classification (ASJC) codes

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