Skip to main content
Log in

The complexity of election problems with group-separable preferences

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

Abstract

We analyze the complexity of several \({{\mathrm {NP}}}\)-hard election-related problems under the assumptions that the voters have group-separable preferences. We show that under this assumption our problems typically remain \({{\mathrm {NP}}}\)-hard, but we provide more efficient algorithms if additionally the clone decomposition tree is of moderate height. We also show a polynomial-time algorithm for sampling group-separable elections uniformly at random.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. Yet, we mention that occasionally tie-breaking may affect the complexity of election problems; see, e.g., the works of Obraztsova et al. [33, 34].

  2. We mention that there is also a notion of single-crossing width of an election, which can be quite useful algorithmically [16]. The notions of single-peaked width and single-crossing width are particularly relevant to our work because, as in the case of group-separable elections, they rely on clone decomposition trees. However, their use of these trees is quite different than the one for group-separable elections.

  3. CCAC stands for constructive control by adding candidates.

  4. CCAV stands for constructive control by adding voters.

  5. The fact that every election has an (in essence) unique clone decomposition tree is included as part of a discussion in the work of Elkind et al. [19], but it is not stated as a theorem there. Karpov [28] provides a formal statement of this result, together with a proof. Based on that, he shows his characterization of group-separable elections.

  6. Note that the core path in this tree is, e.g., \(P_1, \ldots , P_{m-1},c_{m}\).

  7. The reader may wonder why we speak of a size-k committee here, and not of an up-to-size-k committee. The reason is that we minimize the dissatisfaction, so if there is a committee of size smaller than k with a given dissatisfaction, then there certainly is a committee of size exactly k with at least as small a dissatisfaction.

  8. We note that during this greedy process we might end up deleting all the candidates in some set \(L_i\) or \(R_i\). If this happens then we terminate our algorithm for this strategy of increasing the score of p because, effectively, we have moved into another one, which we will consider later (or, which we have considered already).

References

  1. Alonso, L., Rémy, J.-L., & Schott, R. (1997). A linear time algorithm for the generation of trees. Algorithmica, 17, 162–182.

    Article  MathSciNet  MATH  Google Scholar 

  2. Alonso, L., Rémy, J.-L., & Schott, R. (1997). Uniform generation of a Schröder tree. Information Processing Letters, 64, 305–308.

    Article  MathSciNet  MATH  Google Scholar 

  3. 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-15) (pp.107–115).

  4. Ballester, M., & Haeringer, G. (2011). A characterization of the single-peaked domain. Social Choice and Welfare, 36(2), 305–322.

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  6. Betzler, N., Slinko, A., & Uhlmann, J. (2013). On the computation of fully proportional representation. Journal of Artificial Intelligence Research, 47, 475–519.

    Article  MathSciNet  MATH  Google Scholar 

  7. Black, D. (1958). The theory of committees and elections. Cambridge University Press.

    MATH  Google Scholar 

  8. Booth, K., & Lueker, G. (1976). Testing for the consecutive ones property, interval graphs, and graph planarity using PQ-tree algorithms. Journal of Computer and System Sciences, 13(3), 335–379.

    Article  MathSciNet  MATH  Google Scholar 

  9. Brandt, F., Brill, M., Hemaspaandra, E., & Hemaspaandra, L. (2015). Bypassing combinatorial protections: Polynomial-time algorithms for single-peaked electorates. Journal of Artificial Intelligence Research, 53, 439–496.

    Article  MathSciNet  MATH  Google Scholar 

  10. Bredereck, R., Chen, J., & Woeginger, G. (2013). A characterization of the single-crossing domain. Social Choice and Welfare, 41(4), 989–998.

    Article  MathSciNet  MATH  Google Scholar 

  11. Bredereck, R., Chen, J., & Woeginger, G. (2016). Are there any nicely structured preference profiles nearby? Mathematical Social Sciences, 79, 61–73.

    Article  MathSciNet  MATH  Google Scholar 

  12. Caragiannis, I., Hemaspaandra, E., & Hemaspaandra, L. (2016). Dodgson’s rule and Young’s rule. Handbook of Computational Social Choice (pp. 103–126). Cambridge University Press.

    Chapter  Google Scholar 

  13. Chamberlin, B., & Courant, P. (1983). Representative deliberations and representative decisions: Proportional representation and the Borda rule. American Political Science Review, 77(3), 718–733.

    Article  Google Scholar 

  14. Conitzer, V. (2009). Eliciting single-peaked preferences using comparison queries. Journal of Artificial Intelligence Research, 35, 161–191.

    Article  MathSciNet  MATH  Google Scholar 

  15. Cornaz, D., Galand, L., & Spanjaard, O. (2012). Bounded single-peaked width and proportional representation. In Proceedings of the 20th European conference on artificial intelligence (pp. 270–275).

  16. Cornaz, D., Galand, L., & Spanjaard, O. (2013). Kemeny elections with bounded single-peaked or single-crossing width. In Proceedings of the 23rd international joint conference on artificial Iitelligence (IJCAI-13) (pp. 76–82).

  17. Cygan, M., Fomin, F., Kowalik, Ł.,  Lokshtanov, D., Marx, D., Pilipczuk, M., Pilipczuk, M., & Saurabh, S. (2015). Parameterized algorithms. Springer.

  18. Eisenbrand, F., & Weismantel, R. (2018). Proximity results and faster algorithms for integer programming using the Steinitz lemma. In Proceedings of the 29th annual ACM-SIAM symposium on discrete algorithms (SODA-18) (pp. 808–816).

  19. Elkind, E., Faliszewski, P., & Slinko, A. (2012). Clone structures in voters’ preferences. In Proceedings of the 13th ACM conference on electronic commerce (EC-12) (pp. 496–513).

  20. Elkind, E., Lackner, M., & Peters, D. (2016). Preference restrictions in computational social choice: Recent progress. In Proceedings of the 25th international joint conference on artificial intelligence (IJCAI-16) (pp. 4062–4065).

  21. Faliszewski, P., Hemaspaandra, E., Hemaspaandra, L., & Rothe, J. (2011). The shield that never was: Societies with single-peaked preferences are more open to manipulation and control. Information and Computation, 209(2), 89–107.

    Article  MathSciNet  MATH  Google Scholar 

  22. Ferrari, L. (2020). Enhancing the connections between patterns in permutations and forbidden configurations in restricted elections. Journal of Discrete Mathematical Sciences and Cryptography. https://doi.org/10.1080/09720529.2020.1776932.

  23. Gavenčiak, T., Knop, D., & Koutecký, M. (2021). Integer programming in parameterized complexity: Five miniatures. Discrete Optimization, 100596.

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

    Article  MathSciNet  MATH  Google Scholar 

  25. Inada, K. (1964). A note on the simple majority decision rule. Econometrica, 32(32), 525–531.

    Article  MathSciNet  Google Scholar 

  26. Inada, K. (1969). The simple majority decision rule. Econometrica, 37(3), 490–506.

    Article  MATH  Google Scholar 

  27. Karp, R. (1972). Reducibilities among combinatorial problems. In R. Miller and J. Thatcher, editors, Complexity of Computer Computations (pp. 85–103).

  28. Karpov, A. (2019). On the number of group-separable preference profiles. Group Decision and Negotiation, 28(3), 501–517.

    Article  Google Scholar 

  29. Liu, P. (2020). Random assignments on sequentially dichotomous domains. Games and Economic Behavior, 121, 565–584.

    Article  MathSciNet  MATH  Google Scholar 

  30. Lu, T., & Boutilier, C. (2011). Budgeted social choice: From consensus to personalized decision making. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI-11), pages 280–286.

  31. Magiera, K., & Faliszewski, P. (2017). How hard is control in single-crossing elections? Autonomous Agents and Multiagent Systems, 31(3), 606–627.

    Article  MATH  Google Scholar 

  32. Mirrlees, J. (1971). An exploration in the theory of optimal income taxation. Review of Economic Studies, 38, 175–208.

    Article  MATH  Google Scholar 

  33. Obraztsova, S., & Elkind, E. (2011). On the complexity of voting manipulation under randomized tie-breaking. In Proceedings of the 22nd international joint conference on artificial intelligence (IJCAI-11) (pp. 319–324).

  34. Obraztsova, S., Elkind, E., & Hazon, N. (2011). Ties matter: Complexity of voting manipulation revisited. In Proceedings of the 10th international conference on autonomous agents and multiagent systems (AAMAS-11) (pp. 71–78).

  35. Peters, D., & Elkind, E. (2016). Preferences single-peaked on nice trees. In Proceedings of the 30th AAAI conference on artificial intelligence (AAAI-16) (pp. 594–600).

  36. Peters, D., & Lackner, M. (2020). Preferences single-peaked on a circle. Journal of Artificial Intelligence Research, 68, 463–502.

    Article  MathSciNet  MATH  Google Scholar 

  37. Procaccia, A., Rosenschein, J., & Zohar, A. (2008). On the complexity of achieving proportional representation. Social Choice and Welfare, 30(3), 353–362.

    Article  MathSciNet  MATH  Google Scholar 

  38. Roberts, K. (1977). Voting over income tax schedules. Journal of Public Economics, 8(3), 329–340.

    Article  Google Scholar 

  39. Rothe, J., Spakowski, H., & Vogel, J. (2003). Exact complexity of the winner problem for Young elections. Theory of Computing Systems, 36(4), 375–386.

    Article  MathSciNet  MATH  Google Scholar 

  40. Sen, A. (1966). A possibility theorem on majority decisions. Econometrica, 34(2), 491–499.

    Article  MATH  Google Scholar 

  41. Skowron, P., Faliszewski, P., & Lang, J. (2016). Finding a collective set of items: From proportional multirepresentation to group recommendation. Artificial Intelligence, 241, 191–216.

    Google Scholar 

  42. Skowron, P., Faliszewski, P., & Slinko, A. (2015). Achieving fully proportional representation: Approximability result. Artificial Intelligence, 222, 67–103.

    Article  MathSciNet  Google Scholar 

  43. Skowron, P., Yu, L., Faliszewski, P., & Elkind, E. (2015). The complexity of fully proportional representation for single-crossing electorates. Theoretical Computer Science, 569, 43–57.

    Article  MathSciNet  MATH  Google Scholar 

  44. Szufa, S., Faliszewski, P., Skowron, P., Slinko, A., & Talmon, N. (2020). Drawing a map of elections in the space of statistical cultures. In Proceedings of the 19th international conference on autonomous agents and multiagent systems (AAMAS-20) (pp. 1341–1349).

  45. Thiele, T. (1895). Om flerfoldsvalg. In Oversigt over det Kongelige Danske Videnskabernes Selskabs Forhandlinger (pp. 415–441).

  46. Walsh, T. (2015). Generating single peaked votes. Technical Report arXiv:1503.02766 [cs.GT], arXiv.org, March.

  47. Yang, Y. (2015). Manipulation with bounded single-peaked width: A parameterized study. In Proceedings of the 14th international conference on autonomous agents and multiagent systems (AAMAS-15) (pp. 77–85).

  48. Yang, Y., & Guo, J. (2014). Controlling elections with bounded single-peaked width. In Proceedings of the 13th International conference on autonomous agents and multiagent systems (pp. 629–636).

  49. Young, H. (1977). Extending condorcet’s rule. Journal of Economic Theory, 16(2), 335–353.

    Article  MathSciNet  MATH  Google Scholar 

  50. Yu, L., Chan, H., & Elkind, E. (2013). Multiwinner elections under preferences that are single-peaked on a tree. In Proceedings of the 23rd international joint conference on artificial intelligence (IJCAI-13) (Vol. 13, pp. 425–431).

Download references

Acknowledgements

This work was supported by the MOE (Singapore) under Tier-1 Grant 2018-T1-001-118 and by the NTU SUG M4081985 Grant. Alexander Karpov was supported by the Basic Research Program of the National Research University Higher School of Economics.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Piotr Faliszewski.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Faliszewski, P., Karpov, A. & Obraztsova, S. The complexity of election problems with group-separable preferences. Auton Agent Multi-Agent Syst 36, 18 (2022). https://doi.org/10.1007/s10458-022-09549-7

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10458-022-09549-7

Keywords

Navigation