How hard is bribery in elections?

Journal of Artificial Intelligence Research

Volumn 35, Issue , 2009, Pages 485-532

  Faliszewski, Piotr ^a   Hemaspaandra, Edith ^b   Hemaspaandra, Lane A ^c  

^a AGH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Poland)

^b Rochester Institute of Technology   (United States)

^c University of Rochester   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; ELECTRONICS ENGINEERING;

BOUNDED SIZE; CANDIDATE SETS; DICHOTOMY RESULTS; ELECTION SYSTEMS; NON-HOMOGENEOUS; NP COMPLETE; POLYNOMIAL-TIME;

POLYNOMIAL APPROXIMATION;

EID: 77949917931     PISSN: None     EISSN: 10769757     Source Type: Journal    
DOI: 10.1613/jair.2676     Document Type: Article

References (60)

1. Bartholdi, III, J., Tovey, C., & Trick, M. (1989a). The computational difficulty of manipulating an election. Social Choice and Welfare, 6(3), 227–241.
2. Bartholdi, III, J., Tovey, C., & Trick, M. (1989b). Voting schemes for which it can be difficult to tell who won the election. Social Choice and Welfare, 6(2), 157–165.
3. Bartholdi, III, J., Tovey, C., & Trick, M. (1992). How hard is it to control an election? Mathematical and Computer Modeling, 16(8/9), 27–40.
4. Betzler, N., Fellows, M., Guo, J., Niedermeier, R., & Rosamond, F. (2008a). Fixed-parameter algorithms for Kemeny scores. In Proceedings of the 4th International Conference on Algorithmic Aspects in Information and Management, pp. 60–71. Springer-Verlag Lecture Notes in Computer Science #5034.
5. Betzler, N., Guo, J., & Niedermeier, R. (2008b). Parameterized computational complexity of Dodgson and Young elections. In Proceedings of the 11th Scandinavian Workshop on Algorithm Theory, pp. 402–413. Springer-Verlag Lecture Notes in Computer Science #5124.
6. Black, D. (1958). The Theory of Committees and Elections. Cambridge University Press.
7. Brelsford, E. (2007). Approximation and elections. Master’s thesis, Rochester Institute of Technology, Rochester, NY.
8. Brelsford, E., Faliszewski, P., Hemaspaandra, E., Schnoor, H., & Schnoor, I. (2008). Approximability of manipulating elections. In Proceedings of the 23rd AAAI Conference on Artificial Intelligence, pp. 44–49. AAAI Press.
9. Christian, R., Fellows, M., Rosamond, F., & Slinko, A. (2007). On complexity of lobbying in multiple referenda. Review of Economic Design, 11(3), 217–224.
10. Condorcet, J. (1785). Essai sur l’Application de L’Analyse à la Probabilité des Décisions Rendues à la Pluralité des Voix. Facsimile reprint of original published in Paris, 1972, by the Imprimerie Royale.
11. Conitzer, V., & Sandholm, T. (2006). Nonexistence of voting rules that are usually hard to manipulate. In Proceedings of the 21st National Conference on Artificial Intelligence, pp. 627–634. AAAI Press.
12. Conitzer, V., Sandholm, T., & Lang, J. (2007). When are elections with few candidates hard to manipulate? Journal of the ACM, 54(3), Article 14.
13. Dobzinski, S., & Procaccia, A. (2008). Frequent manipulability of elections: The case of two voters. In Proceedings of the 4th International Workshop On Internet And Network Economics, pp. 653–664. Springer-Verlag Lecture Notes in Computer Science #5385.
14. Dodgson, C. (1876). A method of taking votes on more than two issues. Pamphlet printed by the Clarendon Press, Oxford, and headed “not yet published” (see the discussions in (McLean & Urken, 1995; Black, 1958), both of which reprint this paper).
15. Downey, R. (2003). Parameterized complexity for the skeptic. In Proceedings of the 18th Annual IEEE Conference on Computational Complexity, pp. 147–168. IEEE Computer Society Press.
16. Duggan, J., & Schwartz, T. (2000). Strategic manipulability without resoluteness or shared beliefs: Gibbard–Satterthwaite generalized. Social Choice and Welfare, 17(1), 85–93.
17. Dwork, C., Kumar, R., Naor, M., & Sivakumar, D. (2001). Rank aggregation methods for the web. In Proceedings of the 10th International World Wide Web Conference, pp. 613–622. ACM Press.
18. Elkind, E., & Lipmaa, H. (2005a). Hybrid voting protocols and hardness of manipulation. In Proceedings of the 16th Annual International Symposium on Algorithms and Computation, pp. 206–215. Springer-Verlag Lecture Notes in Computer Science #3872.
19. Elkind, E., & Lipmaa, H. (2005b). Small coalitions cannot manipulate voting. In Proceedings of the 9th International Conference on Financial Cryptography and Data Security, pp. 285–297. Springer-Verlag Lecture Notes in Computer Science #3570.
20. Ephrati, E., & Rosenschein, J. (1997). A heuristic technique for multi-agent planning. Annals of Mathematics and Artificial Intelligence, 20(1–4), 13–67.
21. Erdélyi, G., Hemaspaandra, L., Rothe, J., & Spakowski, H. Generalized juntas and NP-hard sets. Theoretical Computer Science. To appear.
22. Erdélyi, G., Hemaspaandra, L., Rothe, J., & Spakowski, H. (2007). On approximating optimal weighted lobbying, and frequency of correctness versus average-case polynomial time. In Proceedings of the 16th International Symposium on Fundamentals of Computation Theory, pp. 300–311. Springer-Verlag Lecture Notes in Computer Science #4639.
23. Erdélyi, G., Hemaspaandra, L., Rothe, J., & Spakowski, H. (2009). Frequency of correctness versus average polynomial time. Information Processing Letters, 109(16), 946–949.
24. Erdélyi, G., Nowak, M., & Rothe, J. (2008a). Sincere-strategy preference-based approval voting broadly resists control. In Proceedings of the 33rd International Symposium on Mathematical Foundations of Computer Science, pp. 311–322. Springer-Verlag Lecture Notes in Computer Science #5162.
25. Erdélyi, G., Nowak, M., & Rothe, J. (2008b). Sincere-strategy preference-based approval voting fully resists constructive control and broadly resists destructive control. Tech. rep. arXiv:0806.0535 [cs.GT], arXiv.org. A precursor appears as (Erdélyi et al., 2008a).
26. Faliszewski, P. (2008). Nonuniform bribery (short paper). In Proceedings of the 7th International Conference on Autonomous Agents and Multiagent Systems, pp. 1569–1572. International Foundation for Autonomous Agents and Multiagent Systems.
27. Faliszewski, P., Hemaspaandra, E., & Hemaspaandra, L. (2006). The complexity of bribery in elections. In Proceedings of the 21st National Conference on Artificial Intelligence, pp. 641–646. AAAI Press.
28. Faliszewski, P., Hemaspaandra, E., Hemaspaandra, L., & Rothe, J. (2007). Llull and Copeland voting broadly resist bribery and control. In Proceedings of the 22nd AAAI Conference on Artificial Intelligence, pp. 724–730. AAAI Press. Journal version available as (Faliszewski, Hemaspaandra, Hemaspaandra, & Rothe, 2009a).
29. Faliszewski, P., Hemaspaandra, E., Hemaspaandra, L., & Rothe, J. (2008). Copeland voting fully resists constructive control. In Proceedings of the 4th International Conference on Algorithmic Aspects in Information and Management, pp. 165–176. Springer-Verlag Lecture Notes in Computer Science #5034.
30. Faliszewski, P., Hemaspaandra, E., Hemaspaandra, L., & Rothe, J. (2009a). Llull and Copeland voting broadly resist bribery and control. Journal of Artificial Intelligence Research, 35, 275–341.
31. Faliszewski, P., Hemaspaandra, E., Hemaspaandra, L., & Rothe, J. (2009b). A richer understanding of the complexity of election systems. In Ravi, S., & Shukla, S. (Eds.), Fundamental Problems in Computing: Essays in Honor of Professor Daniel J. Rosenkrantz, pp. 375–406. Springer.
32. Faliszewski, P., Hemaspaandra, E., & Schnoor, H. (2008). Copeland voting: Ties matter. In Proceedings of the 7th International Conference on Autonomous Agents and Multiagent Systems, pp. 983–990. International Foundation for Autonomous Agents and Multiagent Systems.
33. Friedgut, E., Kalai, G., & Nisan, N. (2008). Elections can be manipulated often. In Proceedings of the 49rd IEEE Symposium on Foundations of Computer Science, pp. 243–249. IEEE Computer Society.
34. Garey, M., & Johnson, D. (1979). Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman and Company.
35. Gibbard, A. (1973). Manipulation of voting schemes. Econometrica, 41(4), 587–601.
36. Hemaspaandra, E., & Hemaspaandra, L. (2007). Dichotomy for voting systems. Journal of Computer and System Sciences, 73(1), 73–83.
37. Hemaspaandra, E., Hemaspaandra, L., & Rothe, J. (1997). Exact analysis of Dodgson elections: Lewis Carroll’s 1876 voting system is complete for parallel access to NP. Journal of the ACM, 44(6), 806–825.
38. Hemaspaandra, E., Hemaspaandra, L., & Rothe, J. (2007). Anyone but him: The complexity of precluding an alternative. Artificial Intelligence, 171(5-6), 255–285.
39. Hemaspaandra, E., Hemaspaandra, L., & Rothe, J. (2009). Hybrid elections broaden complexity-theoretic resistance to control. Mathematical Logic Quarterly, 55(4), 397–424.
40. Hemaspaandra, E., Spakowski, H., & Vogel, J. (2005). The complexity of Kemeny elections. Theoretical Computer Science, 349(3), 382–391.
41. Homan, C., & Hemaspaandra, L. (2009). Guarantees for the success frequency of an algorithm for finding Dodgson-election winners. Journal of Heuristics, 15(4), 403–423.
42. Kemeny, J. (1959). Mathematics without numbers. Daedalus, 88, 577–591.
43. Kemeny, J., & Snell, L. (1960). Mathematical Models in the Social Sciences. Ginn.
44. Ladner, R., Lynch, N., & Selman, A. (1975). A comparison of polynomial time reducibilities. Theoretical Computer Science, 1(2), 103–124.
45. Lenstra, Jr., H. (1983). Integer programming with a fixed number of variables. Mathematics of Operations Research, 8(4), 538–548.
46. Martello, S., & Toth, P. (1990). Knapsack Problems: Algorithms and Computer Implementations. John Wiley and Sons.
47. McCabe-Dansted, J., Pritchard, G., & Slinko, A. (2008). Approximability of Dodgson’s rule. Social Choice and Welfare, 31(2), 311–330.
48. McLean, I., & Urken, A. (1995). Classics of Social Choice. University of Michigan Press.
49. Meir, R., Procaccia, A., Rosenschein, J., & Zohar, A. (2008). The complexity of strategic behavior in multi-winner elections. Journal of Artificial Intelligence Research, 33, 149–178.
50. Niedermeier, R. (2002). Invitation to fixed-parameter algorithms. Habilitation thesis, University of Tübingen.
51. Niedermeier, R. (2006). Invitation to Fixed-Parameter Algorithms. Oxford University Press.
52. Papadimitriou, C. (1994). Computational Complexity. Addison-Wesley.
53. Procaccia, A., & Rosenschein, J. (2007). Junta distributions and the average-case complexity of manipulating elections. Journal of Artificial Intelligence Research, 28, 157–181.
54. Rothe, J. (2005). Complexity Theory and Cryptology: An Introduction to Cryptocomplexity. Springer-Verlag.
55. Rothe, J., Spakowski, H., & Vogel, J. (2003). Exact complexity of the winner problem for Young elections. Theory of Computing Systems, 36(4), 375–386.
56. Saari, D., & Merlin, V. (2000). A geometric examination of Kemeny’s rule. Social Choice and Welfare, 17(3), 403–438.
57. Satterthwaite, M. (1975). Strategy-proofness and Arrow’s conditions: Existence and correspondence theorems for voting procedures and social welfare functions. Journal of Economic Theory, 10(2), 187–217.
58. Xia, L., & Conitzer, V. (2008). A sufficient condition for voting rules to be frequently manipulable. In Proceedings of the 9th ACM Conference on Electronic Commerce, pp. 99–108. ACM Press.
59. Young, H. (1977). Extending Condorcet’s rule. Journal of Economic Theory, 16(2), 335–353.
60. Zuckerman, M., Procaccia, A., & Rosenschein, J. (2008). Algorithms for the coalitional manipulation problem. Artificial Intelligence, 173(2), 392–412.