Skip to main content

Advertisement

Log in

The complexity of bribery and control in group identification

  • Published:
Autonomous Agents and Multi-Agent Systems Aims and scope Submit manuscript

Abstract

The goal of this paper is to analyze the complexity of constructive/destructive bribery and destructive control in the framework of group identification. Group identification applies to situations where a group of individuals determine who among them are socially qualified. We consider consent rules, the consensus-start-respecting rule, and the liberal-start-respecting rule. Each consent rule is characterized by two positive integers s and t, and the socially qualified individuals are determined as follows. If an individual qualifies herself, then she is socially qualified if and only if there are in total at least s individuals qualifying her. Otherwise, she is NOT socially qualified if and only if there are in total at least t individuals disqualifying her. The liberal (resp. consensus)-start-respecting rule determines the socially qualified individuals recursively. In the first step, all individuals qualifying themselves (resp. qualified by all individuals) are socially qualified. Then, the procedure recursively adds individuals who are not socially qualified but are qualified by at least one socially qualified individual into the set of socially qualified individuals until no one can be added this way.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Notes

  1. A preliminary version of this paper appeared in the 6th International Workshop on Computational Social Choice (COMSOC 2016).

  2. In fact, the Vertex Cover instance constructed in the proof of the NP-hardness of the problem fulfills these assumptions (see pages 54 and 55 in [35]).

References

  1. Alcantud, J. C. R., & Laruelle, A. (2014). Dis&approval voting: A characterization. Social Choice and Welfare, 43(1), 1–10.

    MathSciNet  MATH  Google Scholar 

  2. Alon, N., Fischer, F. A., Procaccia, A. D., & Tennenholtz, M. (2011). Sum of us: Strategyproof selection from the selectors. In Proceedings of the 13th conference on theoretical aspects of rationality and knowledge (TARK) (pp. 101–110).

  3. Arora, S., & Barak, B. (2009). Computational complexity: A modern approach. Cambridge: Cambridge University Press.

    MATH  Google Scholar 

  4. Aziz, H., Gaspers, S., Gudmundsson, J., Mackenzie, S., Mattei, N., & Walsh, T. (2015). Computational aspects of multi-winner approval voting. In Proceedings of the 14th international conference on autonomous agents and multiagent systems (AAMAS) (pp. 107–115).

  5. Aziz, H., Lev, O., Mattei, N., Rosenschein, J. S., & Walsh, T. (2016). Strategyproof peer selection: Mechanisms, analyses, and experiments. In Proceedings of the 30th AAAI conference on artificial intelligence (AAAI) (pp. 397–403).

  6. Bang-Jensen, J., & Gutin, G. (2008). Digraphs—Theory. Algorithms and applications. Berlin: Springer.

    MATH  Google Scholar 

  7. Bartholdi, J, I. I. I., Tovey, C., & Trick, M. (1989). The computational difficulty of manipulating an election. Social Choice and Welfare, 6(3), 227–241.

    MathSciNet  MATH  Google Scholar 

  8. Bartholdi, J, I. I. I., Tovey, C., & Trick, M. (1989). Voting schemes for which it can be difficult to tell who won the election. Social Choice and Welfare, 6(2), 157–165.

    MathSciNet  MATH  Google Scholar 

  9. Bartholdi, J, I. I. I., Tovey, C., & Trick, M. (1992). How hard is it to control an election? Mathematical and Computer Modeling, 16(8/9), 27–40.

    MathSciNet  MATH  Google Scholar 

  10. Baumeister, D., & Dennisen, S. (2015). Voter dissatisfaction in committee elections. In Proceedings of the 14th international conference on autonomous agents and multiagent systems (AAMAS) (pp. 1707–1708).

  11. Baumeister, D., Erdélyi, G., Erdélyi, O., & Rothe, J. (2015). Complexity of manipulation and bribery in judgment aggregation for uniform premise-based quota rules. Mathematical Social Sciences, 76, 19–30.

    MathSciNet  MATH  Google Scholar 

  12. Baumeister, D., Erdélyi, G., Erdélyi, O., & Rothe, J. (2015). Judgment Aggregation. Economics and Computation, 6, 361–391.

    MATH  Google Scholar 

  13. Baumeister, D., Hogrebe, T., & Rey, L. (2019). Generalized distance bribery. In Proceedings of the 33rd AAAI conference on artificial intelligence (AAAI) (pp. 1764–1771).

  14. Berga, D., Bergantiños, G., Massó, J., & Neme, A. (2004). Stability and voting by committees with exit. Social Choice and Welfare, 23(2), 229–247.

    MathSciNet  MATH  Google Scholar 

  15. Binkele-Raible, D., Erdélyi, G., Fernau, H., Goldsmith, J., Mattei, N., & Rothe, J. (2014). The complexity of probabilistic lobbying. Discrete Optimization, 11, 1–21.

    MathSciNet  MATH  Google Scholar 

  16. Brams, S., & Fishburn, P. (1978). Approval voting. American Political Science Review, 72(3), 831–847.

    MATH  Google Scholar 

  17. Brandt, F., Conitzer, V., Endriss, U., Lang, J., & Procaccia, A. (Eds.). (2016). Handbook of computational social choice. Cambridge: Cambridge University Press.

    MATH  Google Scholar 

  18. Bredereck, R., Chen, J., Hartung, S., Kratsch, S., Niedermeier, R., Suchý, O., et al. (2014). A multivariate complexity analysis of lobbying in multiple referenda. Journal of Artificial Intelligence Research, 50, 409–446.

    MathSciNet  MATH  Google Scholar 

  19. Bredereck, R., Kaczmarczyk, A., & Niedermeier, R. (2017). On coalitional manipulation for multiwinner elections: Shortlisting. In Proceedings of the 26th international joint conference on artificial intelligence (IJCAI) (pp. 887–893).

  20. Cengelci, M. A., & Sanver, M. R. (2010). Simple collective identity functions. Theory and Decision, 68, 417–443.

    MathSciNet  MATH  Google Scholar 

  21. Christian, R., Fellows, M., Rosamond, F., & Slinko, A. (2007). On complexity of lobbying in multiple referenda. Review of Economic Design, 11(3), 217–224.

    MathSciNet  MATH  Google Scholar 

  22. Dimitrov, D. (2011). The social choice approach to group identification. Consensual processes (pp. 123–134). Berlin: Springer.

    Google Scholar 

  23. Dimitrov, D., Sung, S. C., & Xu, Y. (2007). Procedural group identification. Mathematical Social Sciences, 54(2), 137–146.

    MathSciNet  MATH  Google Scholar 

  24. Elkind, E., Faliszewski, P., Skowron, P., & Slinko, A. (2017). Properties of multiwinner voting rules. Social Choice and Welfare, 48(3), 599–632.

    MathSciNet  MATH  Google Scholar 

  25. Elkind, E., Faliszewski, P., & Slinko, A. M. (2009). Swap bribery. In Proceedings of the 2nd international symposim on algorithmic game theory (SAGT) (pp. 299–310).

  26. Elkind, E., Lang, J., & Saffidine, A. (2011). Choosing collectively optimal sets of alternatives based on the condorcet criterion. In Proceedings of the 22nd international joint conference on artificial intelligence (IJCAI) (pp. 186–191).

  27. Erdélyi, G., Hemaspaandra, E., & Hemaspaandra, L. A. (2015). More natural models of electoral control by partition. In Proceedings of the 4th international conference on algorithmic decision theory (ADT) (pp. 396–413).

  28. Erdélyi, G., Reger, C., & Yang, Y. (2017). The complexity of bribery and control in group identification. In Proceedings of the 16th international conference on autonomous agents and multiagent systems (AAMAS) (pp. 1142–1150).

  29. Even, S. (1975). An algorithm for determining whether the connectivity of a graph is at least \(k\). SIAM Journal on Computing, 4(3), 393–396.

    MathSciNet  MATH  Google Scholar 

  30. Even, S., & Tarjan, R. E. (1975). Network flow and testing graph connectivity. SIAM Journal on Computing, 4(4), 507–518.

    MathSciNet  MATH  Google Scholar 

  31. Faliszewski, P., Hemaspaandra, E., & Hemaspaandra, L. A. (2009). How hard is bribery in elections? Journal of Artificial Intelligence Research, 35, 485–532.

    MathSciNet  MATH  Google Scholar 

  32. Faliszewski, P., Slinko, A., & Talmon, N. (2017). Bribery as a measure of candidate success: Complexity results for approval-based multiwinner rules. In Proceedings of the 16th international conference on autonomous agents and multiagent systems (AAMAS) (pp. 6–14).

  33. Faliszewski, P., Slinko, A., & Talmon, N. (2017). The complexity of multiwinner voting rules with variable number of winners. arXiv:1711.06641.

  34. Fishburn, P. (1981). An analysis of simple voting systems for electing committees. SIAM Journal on Applied Mathematics, 41(3), 499–502.

    MathSciNet  MATH  Google Scholar 

  35. Garey, M. R., & Johnson, D. S. (1979). Computers and intractability: A guide to the theory of NP-completeness. New York: W. H. Freeman.

    MATH  Google Scholar 

  36. Gehrlein, W. (1985). The condorcet criterion and committee selection. Mathematical Social Sciences, 10(3), 199–209.

    MathSciNet  MATH  Google Scholar 

  37. Gonzalez, S., Laruelle, A., & Solal, P. (2019). Dilemma with approval and disapproval votes. Social Choice and Welfare, 53(3), 497–517.

    MathSciNet  MATH  Google Scholar 

  38. Gonzalez, T. F. (1985). Clustering to minimize the maximum intercluster distance. Theoretical Computer Science, 38, 293–306.

    MathSciNet  MATH  Google Scholar 

  39. Hemaspaandra, E., Hemaspaandra, L. A., & Rothe, J. (2007). Anyone but him: The complexity of precluding an alternative. Artificial Intelligence, 171(5–6), 255–285.

    MathSciNet  MATH  Google Scholar 

  40. Kaczmarczyk, A., & Faliszewski, P. (2019). Algorithms for destructive shift bribery. Autonomous Agents and Multi-Agent Systems, 33(3), 275–297.

    Google Scholar 

  41. Kasher, A. (1993). Jewish collective identity. In D. T. Goldberg & M. Krausz (Eds.), Jewish identity (Vol. 4, pp. 56–78). Philadelphia: Temple University Press.

    Google Scholar 

  42. Kasher, A., & Rubinstein, A. (1997). On the question “Who is a J?”, a social choice approach. Logique et Analyse, 60, 385–395.

    MathSciNet  MATH  Google Scholar 

  43. Kilgour, D., Brams, S., & Sanver, R. (2006). How to elect a representative committee using approval balloting. In F. Pukelsheim & B. Simeone (Eds.), Mathematics and democracy: Voting systems and collective choice (pp. 83–95). Berlin: Springer.

    Google Scholar 

  44. Kilgour, D. M. (2016). Approval elections with a variable number of winners. Theory and Decision, 81(2), 199–211.

    MathSciNet  MATH  Google Scholar 

  45. Lewis, J. M., & Yannakakis, M. (1980). The node-deletion problem for hereditary properties is NP-complete. Journal of Computer and System Sciences, 20(2), 219–230.

    MathSciNet  MATH  Google Scholar 

  46. Meir, R., Procaccia, A. D., Rosenschein, J. S., & Zohar, A. (2008). Complexity of strategic behavior in multi-winner elections. Journal of Artificial Intelligence Research, 33, 149–178.

    MathSciNet  MATH  Google Scholar 

  47. Miller, A. D. (2008). Group identification. Games and Economic Behavior, 63(1), 188–202.

    MathSciNet  MATH  Google Scholar 

  48. Nehama, I. (2015). Complexity of optimal lobbying in threshold aggregation. In Proceedings of the 4th international conference on algorithmic decision theory (ADT) (pp. 379–395).

  49. Obraztsova, S., Zick, Y., & Elkind, E. (2013). On manipulation in multi-winner elections based on scoring rules. In Proceedings of the 12th international conference on autonomous agents and multiagent systems (AAMAS) (pp. 359–366).

  50. Ratliff, T. (2003). Some startling inconsistencies when electing committees. Social Choice and Welfare, 21(3), 433–454.

    MathSciNet  MATH  Google Scholar 

  51. Samet, D., & Schmeidler, D. (2003). Between liberalism and democracy. Journal of Economic Theory, 110(2), 213–233.

    MathSciNet  MATH  Google Scholar 

  52. Tovey, C. A. (2002). Tutorial on computational complexity. Interfaces, 32(3), 30–61.

    Google Scholar 

  53. West, D. B. (2000). Introduction to graph theory. Upper Saddle River: Prentice-Hall.

    Google Scholar 

  54. Xia, L. (2012). Computing the margin of victory for various voting rules. In Proceedings of the 13th acm conference on electronic commerce (ACM-EC) (pp. 982–999).

  55. Xia, L., Zuckerman, M., Procaccia, A., Conitzer, V., & Rosenschein, J. (2009). Complexity of unweighted coalitional manipulation under some common voting rules. In Proceedings of the 21st international joint conference on artificial intelligence (IJCAI) (pp. 348–353).

  56. Yang, Y., & Dimitrov, D. (2018). How hard is it to control a group? Autonomous Agents and Multi-Agent Systems, 32(5), 672–692.

    Google Scholar 

  57. Yang, Y., Shrestha, Y. R., & Guo, J. (2019). On the complexity of bribery with distance restrictions. Theoretical Computer Science, 760, 55–71.

    MathSciNet  MATH  Google Scholar 

  58. Yang, Y., & Wang, J. (2018). Multiwinner voting with restricted admissible sets: Complexity and strategyproofness. In Proceedings of the 27th international joint conference on artificial intelligence (IJCAI) (pp. 576–582).

  59. Yang, Y. (2019). Complexity of manipulating and controlling approval-based multiwinner voting. In Proceedings of the 28th international joint conference on artificial intelligence (IJCAI) (pp. 637–643).

  60. Zhou, A., Yang, Y., & Guo, J. (2019). Parameterized complexity of committee elections with dichotomous and trichotomous votes. In Proceedings of the 18th international conference on autonomous agents and multiagent systems (AAMAS) (pp. 503–510).

Download references

Acknowledgements

This paper was supported by the DFG (Grant No. ER 738/2-1, ER 738/2-2), the National Natural Science Foundation of China (Grant No. 61702557), and the Postdoctoral Science Foundation of China (Grant No. 2017M612584). The paper was written in part while the first and second authors were affiliated at University of Siegen.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yongjie Yang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

A preliminary version of this paper appeared in Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017) [28].

Authors are ordered alphabetically.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Erdélyi, G., Reger, C. & Yang, Y. The complexity of bribery and control in group identification. Auton Agent Multi-Agent Syst 34, 8 (2020). https://doi.org/10.1007/s10458-019-09427-9

Download citation

  • Published:

  • DOI: https://doi.org/10.1007/s10458-019-09427-9

Keywords

Navigation