Modal Ranking: A Uniquely Robust Voting Rule

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2014, Pages 616-622

  Caragiannis, Ioannis ^a   Procaccia, Ariel D ^b   Shah, Nisarg ^b  

^a UNIVERSITY OF PATRAS   (Greece)

^b CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

VOTING RULES;

EID: 84908206322     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. Caragiannis, I.; Procaccia, A. D.; and Shah, N. 2013. When do noisy votes reveal the truth? In Proceedings of the 14th ACM Conference on Electronic Commerce (EC), 143-160.
2. Conitzer, V., and Sandholm, T. 2005. Common voting rules as maximum likelihood estimators. In Proceedings of the 21st Annual Conference on Uncertainty in Artificial Intelligence (UAI), 145-152.
3. Conitzer, V.; Rognlie, M.; and Xia, L. 2009. Preference functions that score rankings and maximum likelihood estimation. In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI), 109-115.
4. de Condorcet, M. 1785. Essai sur l'application de l'analyse a la probability de decisions rendues i la pluralite de voix. Imprimerie Royal. Facsimile published in 1972 by Chelsea Publishing Company, New York.
5. Desario, D., and Robins, S. 2011. Generalized solid-angle theory for real polytopes. The Quarterly Journal of Mathematics 62(4): 1003-1015.
6. Dodgson, C. L. 1876. A Method for Taking Votes on More than Two Issues. Clarendon Press.
7. Elkind, E.; Faliszewski, P.; and Slinko, A. 2010. Good rationalizations of voting rules. In Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI), 774-779.
8. Law, E., and von Ahn, L. 2011. Human Computation. Morgan & Claypool.
9. Little, G.; Chilton, L. B.; Goldman, M.; and Miller, R. C. 2010. Turkit: Human computation algorithms on Mechanical Turk. In Proceedings of the 23rd Annual ACM symposium on User interface software and technology (UIST), 57-66.
10. Lu, T., and Boutilier, C. 2011. Learning Mallows models with pairwise preferences. In Proceedings of the 28th International Conference on Machine Learning (ICML), 145- 152.
11. Mallows, C. L. 1957. Non-null ranking models. Biometrika 44:114-130.
12. Mao, A.; Procaccia, A. D.; and Chen, Y. 2013. Better human computation through principled voting. In Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI), 1142-1148.
13. Mossel, E.; Procaccia, A. D.; and Racz, M. Z. 2013. A smooth transition from powerlessness to absolute power. Journal of Artificial Intelligence Research 48:923-951.
14. Moulin, H. 1983. The Strategy of Social Choice, volume 18 of Advanced Textbooks in Economics. North-Holland.
15. Obraztsova, S.; Elkind, E.; Faliszewski, P.; and Slinko, A. 2013. On swap-distance geometry of voting rules. In Proceedings of the 12th International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 383- 390.
16. Procaccia, A. D.; Reddi, S. J.; and Shah, N. 2012. A maximum likelihood approach for selecting sets of alternatives. In Proceedings of the 28th Annual Conference on Uncertainty in Artificial Intelligence (UAI), 695-704.
17. Saari, D. G. 1995. Basic geometry of voting. Springer.
18. Saari, D. G. 2008. Complexity and the geometry of voting. Mathematical and Computer Modelling 48(9-10): 1335- 1356.
19. Xia, L., and Conitzer, V. 2008. Generalized scoring rules and the frequency of coalitional manipulability. In Proceedings of the 9th ACM Conference on Electronic Commerce (EC), 109-118.
20. Xia, L., and Conitzer, V. 2009. Finite local consistency characterizes generalized scoring rules. In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI), 336-341.
21. Xia, L., and Conitzer, V. 2011. A maximum likelihood approach towards aggregating partial orders. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI), 446-451.
22. Xia, L.; Conitzer, V.; and Lang, J. 2010. Aggregating preferences in multi-issue domains by using maximum likelihood estimators. In Proceedings of the 9th International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 399-408.
23. Xia, L. 2013. Generalized scoring rules: A framework that reconciles Borda and Condorcet. SIGecom Exchanges 12(1):42-48.
24. Young, H. P. 1975. Social choice scoring functions. SIAM Journal of Applied Mathematics 28(4):824-838.
25. Young, H. P. 1988. Condorcet's theory of voting. The American Political Science Review 82(4): 1231-1244.