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International Conference on Artificial Intelligence: Methodology, Systems, and Applications

AIMSA 2016: Artificial Intelligence: Methodology, Systems, and Applications pp 299–309Cite as

Possible Bribery in k-Approval and k-Veto Under Partial Information

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9883))

Abstract

We study the complexity of possible bribery under nine different notions of partial information for k-\({\mathsf {Approval}}\) and k-\({\mathsf {Veto}}\). In bribery an external agent tries to change the outcome of an election by changing some voters’ votes. Usually in voting theory, full information is assumed, i.e., the manipulative agent knows the set of candidates, the complete ranking of each voter about the candidates and the voting rule used. In this paper, we assume that the briber only has partial information about the voters’ votes and ask whether the briber can change some voters’ votes such that there is a completion of the partial profile to a full profile such that the briber’s preferred candidate (or most despised candidate in the destructive case) is a winner (not a winner) of the resulting election.

This work is supported in part by the DFG under grant ER 738/2-1.

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References

  1. Baumeister, D., Faliszewski, P., Lang, J., Rothe, J.: Campaigns for lazy voters: truncated ballots. In: Proceedings of the 11th International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 577–584. IFAAMAS, June 2012

    Google Scholar 

  2. Baumeister, D., Rothe, J.: Taking the final step to a full dichotomy of the possible winner problem in pure scoring rules. Inf. Process. Lett. 112(5), 186–190 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  3. Betzler, N., Bredereck, R., Niedermeier, R.: Partial kernelization for rank aggregation: theory and experiments. In: Proceedings of the 3rd International Workshop on Computational Social Choice, pp. 31–42 (2010)

    Google Scholar 

  4. Betzler, N., Dorn, B.: Towards a dichotomy for the possible winner problem in elections based on scoring rules. J. Comput. Syst. Sci. 76(8), 812–836 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  5. Briskorn, D., Erdélyi, G., Reger, C.: Bribery in k-approval and k-veto under partial information (extended abstract). In: Proceedings of The 15th International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1299–1301. IFAAMAS, May 2016

    Google Scholar 

  6. Chevaleyre, Y., Lang, J., Maudet, N., Monnot, J.: Possible winners when new candidates are added: the case of scoring rules. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence, pp. 762–767. AAAI Press, July 2010

    Google Scholar 

  7. Conitzer, V., Walsh, T., Xia, L.: Dominating manipulations in voting with partial information. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence, pp. 638–643. AAAI Press, August 2011

    Google Scholar 

  8. Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: Proceedings of the 10th International World Wide Web Conference, pp. 613–622. ACM Press, May 2001

    Google Scholar 

  9. Ephrati, E., Rosenschein, J.: The Clarke Tax as a consensus mechanism among automated agents. In: Proceedings of the 9th National Conference on Artificial Intelligence, pp. 173–178. AAAI Press (1991)

    Google Scholar 

  10. Ephrati, E., Rosenschein, J.: Multi-agent planning as a dynamic search for social consensus. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp. 423–429. Morgan Kaufmann (1993)

    Google Scholar 

  11. Ephrati, E., Rosenschein, J.: A heuristic technique for multi-agent planning. Ann. Math. Artif. Intell. 20(1–4), 13–67 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  12. Fagin, R., Kumar, R., Sivakumar, D.: Efficient similarity search and classification via rank aggregation. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, pp. 301–312. ACM Press (2003)

    Google Scholar 

  13. Faliszewski, P., Hemaspaandra, E., Hemaspaandra, L.A.: The complexity of bribery in elections. In: Proceedings of the 20th AAAI Conference on Artificial Intelligence, pp. 641–646. AAAI Press (2006)

    Google Scholar 

  14. Faliszewski, P., Hemaspaandra, E., Hemaspaandra, L.A.: How hard is bribery in elections? J. Artif. Intell. Res. 35, 485–532 (2009)

    MathSciNet  MATH  Google Scholar 

  15. Faliszewski, P., Hemaspaandra, E., Hemaspaandra, L.A., Rothe, J.: Llull and Copeland voting computationally resist bribery and constructive control. J. Artif. Intell. Res. 35, 275–341 (2009)

    MathSciNet  MATH  Google Scholar 

  16. Faliszewski, P., Reisch, Y., Rothe, J., Schend, L.: Complexity of manipulation, bribery, and campaign management in Bucklin and fallback voting. Auton. Agents Multi-Agent Syst. 29(6), 1091–1124 (2015)

    Article  Google Scholar 

  17. Ghosh, S., Mundhe, M., Hernandez, K., Sen, S.: Voting for movies: the anatomy of recommender systems. In: Proceedings of the 3rd Annual Conference on Autonomous Agents, pp. 434–435. ACM Press, May 1999

    Google Scholar 

  18. Grötschel, M., Lovász, L., Schrijver, A.: Geometric Algorithms and Combinatorial Optimization. Springer, Heidelberg (1988)

    Book  MATH  Google Scholar 

  19. Konczak, K., Lang, J.: Voting procedures with incomplete preferences. In: Proceedings of IJCAI-05 Multidisciplinary Workshop on Advances in Preference Handling, pp. 124–129 (2005)

    Google Scholar 

  20. Lin, A.: The complexity of manipulating \(k\)-approval elections. In: Proceedings of the 3rd International Conference on Agents and Artificial Intelligence, pp. 212–218 (2011)

    Google Scholar 

  21. Parkes, D.C., Xia, L.: A complexity-of-strategic-behavior comparison between schulze’s rule and ranked pairs. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence. AAAI Press (2012)

    Google Scholar 

  22. Reger, C.: Voter control in k-approval and k-veto under partial information. In: Proceedings of the 14th International on Artificial Intelligence and Mathematics, January 2016

    Google Scholar 

  23. Reisch, Y., Rothe, J., Schend, L.: The margin of victory in Schulze, cup, and copeland elections: complexity of the regular and exact variants. In: Proceedings of the 7th European Starting AI Research Symposium, pp. 250–259. IOS Press (2014)

    Google Scholar 

  24. Xia, L.: Computing the margin of victory for various voting rules. In: Proceedings of the 13th ACM Conference on Electronic Commerce, pp. 982–999. ACM Press, June 2012

    Google Scholar 

  25. Xia, L.: Designing social choice mechanisms using machine learning. In: Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems, pp. 471–474. IFAAMAS, May 2013

    Google Scholar 

  26. Xia, L., Conitzer, V.: Determining possible and necessary winners given partial orders. J. Artif. Intell. Res. 41, 25–67 (2011)

    MATH  Google Scholar 

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Authors and Affiliations

  1. University of Siegen, Unteres Schloß 3, 57072, Siegen, Germany

    Gábor Erdélyi & Christian Reger

Authors
  1. Gábor Erdélyi
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  2. Christian Reger
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Correspondence to Christian Reger .

Editor information

Editors and Affiliations

  1. Winston-Salem State University, Winston Salem, North Carolina, USA

    Christo Dichev

  2. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Gennady Agre

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Erdélyi, G., Reger, C. (2016). Possible Bribery in k-Approval and k-Veto Under Partial Information. In: Dichev, C., Agre, G. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016. Lecture Notes in Computer Science(), vol 9883. Springer, Cham. https://doi.org/10.1007/978-3-319-44748-3_29

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  • DOI: https://doi.org/10.1007/978-3-319-44748-3_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44747-6

  • Online ISBN: 978-3-319-44748-3

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