The unavailable candidate model: A decision-theoretic view of social choice

Proceedings of the ACM Conference on Electronic Commerce

Volumn , Issue , 2010, Pages 263-273

  Lu, Tyler ^a   Boutilier, Craig E ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

preferences; rank aggregation; social choice; voting

Indexed keywords

AGGREGATION METHODS; CANDIDATE MODELS; CONSENSUS RANKING; DECISION POLICY; DECISION-THEORETIC; FUNDAMENTAL PROBLEM; GREEDY SCHEME; RANK AGGREGATION; SOCIAL CHOICE; VOTING PROTOCOLS;

COMPUTATION THEORY; DECISION THEORY; ELECTRONIC COMMERCE; OPTIMIZATION;

APPROXIMATION ALGORITHMS;

EID: 77954753610     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1807342.1807385     Document Type: Conference Paper

References (25)

1. N. Ailon. Aggregation of partial rankings, p-ratings and top-m lists. In 18th Annual Symposium on Discrete Algorithms (SODA), pp.415-424, New Orleans, 2007.
2. F. Aleskerov. Categories of Arrovian voting schemes. In Handbook of Social Choice and Welfare, Vol. 1, K. J. Arrow, A. K. Sen, K. Suzumura (eds), Elsevier, Amsterdam, 2002.
3. K. J. Arrow. A di culty in the concept of social welfare. Journal of Political Economy, 58:328-346, 1950.
4. J. Bartholdi III, C. Tovey, and M. Trick. Voting schemes for which it can be di cult to tell who won the election. Social Choice and Welfare, 6:57-165, 1989.
5. W. Cohen, R. E. Schapire, and Y. Singer. Learning to order things. Journal of Artificial Intelligence Research, 10:243-270, 1998.
6. V. Conitzer, A. J. Davenport, and J. Kalagnanam. Improved bounds for computing Kemeny rankings. In 21st National Conf. on Artificial Intelligence (AAAI), pp.620-626, Boston, 2006.
7. V. Conitzer, M. Rognlie, and L. Xia. Preference functions that score rankings and maximum likelihood estimation. In 21st International Joint Conf. on Artificial Intelligence (IJCAI), pp.109-115, Pasadena, CA, 2009.
8. V. Conitzer and T. Sandholm. Common voting rules as maximum likelihood estimators. In 21st Conf. on Uncertainty in Artificial Intelligence (UAI), Edinburgh, pp.145-152, 2005.
9. P. Diaconis. Group Representations in Probability and Statistics. Inst. Math. Statistics, Hayward, CA, 1988.
10. C. Dwork, R. Kumar, M. Naor, and D. Sivakumar. Rank aggregation methods for the web. In 10th International World Wide Web Conf. (WWW), pp.613-622, Hong Kong, 2001.
11. M. A. Fligner and J. S. Verducci (eds.). Probability Models and Statistical Analysis for Ranking Data. Springer, NY, 1993.
12. W. Gaertner. A Primer in Social Choice Theory. Oxford, 2006.
13. D. Gale and L. S. Shapley. College admissions and the stability of marriage. American Mathematical Monthly, 69:9-14, 1962.
14. E. Hüllermeier, J. Fürnkranz, W. Cheng, and K. Brinker. Label ranking by learning pairwise preferences. Artificial Intelligence, 172(16-17):1897-1916, 2008.
15. J. G. Kemeny. Mathematics without numbers. Daedalus, 88(4):577-591, 1959.
16. C. Kenyon-Mathieu and W. Schudy. How to rank with few errors. In 39th Symposium on Theory of Computing (STOC), pp.95-103, San Diego, 2007.
17. A. Klementiev, D. Roth, K. Small, and I. Titov. Unsupervised prediction aggregation. In NIPS 2009 Workshop: Learning with Orderings, Whistler, BC, 2009.
18. G. Lebanon and J. D. Lafferty. Cranking: Combining rankings using conditional probability models on permutations. In 19th International Conf. on Machine Learning, pp.363-370, Sydney, 2002.
19. R. D. Luce. Individual Choice Behavior: A Theoretical Analysis. Wiley, 1959.
20. C. L. Mallows. Non-null ranking models. Biometrika, 44:114-130, 1957.
21. R. Plackett. The analysis of permutations. Applied Statistics, 24:193-202, 1975.
22. F. Radlinski and T. Joachims. Active exploration for learning rankings from clickthrough data. In 13th International Conf. on Knowledge Discovery and Data Mining (KDD), pp.570-579, San Jose, CA, 2007.
23. A. E. Roth. The National Residency Matching Program as a Labor Market Journal of the American Medical Association, 275(13):1054-1056
24. A. K. Sen. Collective Choice and Social Welfare. Holden-Day, San Francisco, 1970.
25. P. Young. Optimal voting rules. Journal of Economic Perspectives, 9:51-64, 1995.