Voting in cooperative information agent scenarios: Use and abuse

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4149 LNAI, Issue , 2006, Pages 33-50

  Rosenschein, Jeffrey S ^a   Procaccia, Ariel D ^a  

^a HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; COMPUTER SCIENCE; KNOWLEDGE ENGINEERING; KNOWLEDGE REPRESENTATION; NETWORK PROTOCOLS; VOTING MACHINES;

AUTOMATED AGENTS; INFORMATION AGENTS; NP HARD;

INTELLIGENT AGENTS;

EID: 33750993189     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11839354_4     Document Type: Conference Paper

References (30)

1. Yoram Bachrach and Jeffrey S. Rosenschein. Achieving allocatively- efficient and strongly budget-balanced mechanisms in the network flow domain for bounded-rational agents. In The Nineteenth International Joint Conference on Artificial Intelligence, pages 1653-1654, Edinburgh, Scotland, August 2005.
2. Full version published in The Seventh International Workshop on Agent-Mediated Electronic Commerce: Designing Mechanisms and Systems (AMEC 2005), Utrecht, The Netherlands, July 2005.
3. J. Bartholdi and J. Orlin. Single transferable vote resists strategic voting. Social Choice and Welfare, 8(4):341-354, 1991.
4. J. Bartholdi, C. A. Tovey, and M. A. Trick. How hard is it to control an election. Mathematical and Computer Modelling, 16:27-40, 1992.
5. V. Conitzer, J. Lang, and T. Sandholm. How many candidates are needed to make elections hard to manipulate? In Proceedings of the International Conference on Theoretical Aspects of Reasoning about Knowledge, pages 201-214, Bloomington, Indiana, 2003.
6. V. Conitzer and T. Sandholm. Complexity of manipulating elections with few candidates. In Proceedings of the National Conference on Artificial Intelligence, pages 314-319, Edmonton, Canada, July 2002.
7. V. Conitzer and T. Sandholm. Complexity of manipulating elections with few candidates. In Proceedings of the National Conference on Artificial Intelligence, pages 314-319, Edmonton, Canada, July 2002.
8. V. Conitzer and T. Sandholm. Universal voting protocol tweaks to make manipulation hard. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 781-788, Acapulco, Mexico, August 2003.
9. E. Elkind and H. Lipmaa. Small coalitions cannot manipulate voting. In International Conference on Financial Cryptography. Lecture Notes in Computer Science. Springer-Verlag, Roseau, The Commonwealth of Dominica, 2005.
10. Eithan Ephrati and Jeffrey S. Rosenschein, The Clarke Tax as a consensus mechanism among automated agents. In Proceedings of the Ninth National Conference on Artificial Intelligence, pages 173-178, Anaheim, California, July 1991.
11. Eithan Ephrati and Jeffrey S. Rosenschein. A heuristic technique for multiagent planning. Annals of Mathematics and Artificial Intelligence, 20:13-67, Spring 1997.
12. Robert M. Gagné and Karen L. Medsker. The Conditions of Learning Training Applications. Harcourt Brace & Company, 1996.
13. S. Ghosh, M. Muudhe, K. Hernandez, and S. Sen. Voting for movies: the anatomy of a rocommender system. In Proceedings of the Third Annual Conference on Autonomous Agents, pages 434-435, 1999.
14. A. Gibbard. Manipulation of voting schemes. Econometrica, 41:587-602, 1973.
15. T. Haynes, S. Sen, N. Arora, and R. Nadella. An automated meeting scheduling system that utilizes user preferences. In Proceedings of the First International Conference on Autonomous Agents, pages 308-315, 1997.
16. E. Hernaspaandra, L. Hemaspaandra, and J. Rothe. Anyone but him: The complexity of precluding an alternative. In Proceedings of the 20th National Conference on Artificial Intelligence, Pittsburgh, July 2005.
17. E. Hemaspaandra and L. A. Hemaspaandra. Dichotomy for voting systems. University of Rochester Department of Computer Science Technical Report 861, 2005.
18. William G. Hoover. Computational Statistical Mechanics. Elsevier, 1991.
19. George Johnson. All science is computer science, 25 March 2001. The New York Times, Week in Review, pages 1, 5.
20. Noam Nisan and Amir Ronen. Algorithmic mechanism design. Games and Economic Behavior, 35:166-196, 2001.
21. Ariel D. Procaccia and Jeffrey S. Rosenschein. The distortion of cardinal preferences in voting. In The Tenth International Workshop on Cooperative Information Agents (CIA 2006), Edinburgh, September 2006.
22. Ariel D. Procaccia and Jeffrey S. Rosenschein. Junta distributions and the average-case complexity of manipulating elections. In The Fifth International Joint, Conference on Autonomous Agents and Multiagent Systems, pages 497-504, Hakodate, Japan, May 2006.
23. Ariel D. Procaccia, Jeffrey S. Rosenschein, and Aviv Zohar. Multi-winner elections: Complexity of manipulation, control and winner-determination. In The Eighth International Workshop on Agent-Mediated Electronic Commerce (AMEC 2006), pages 15-28, Hakodate, Japan, May 2006.
24. Jeffrey S. Rosenschein and Michael R. Genesereth. Deals among rational agents. In Proceedings of the Ninth International Joint Conference on Artificial Intelligence, pages 91-99, Los Angeles, California, August 1985.
25. T. Sandholm and V. Lesser. Issues in automated negotiation and electronic commerce: Extending the contract net framework. In Proceedings of the First International Conference on Multiagent Systems (ICMAS-95), pages 328-335, San Francisco, 1995.
26. M. Satterthwaite. Strategy-proofness and Arrow's conditions: Existence and correspondence theorems for voting procedures and social welfare functions. Journal of Economic Theory, 10:187-217, 1975.
27. Moshe Tennenholtz and Alon Altman. On the axiomatic foundations of ranking systems. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI'05), pages 917-922, Edinburgh, August 2005.
28. Jeffrey D. Thomas, Taesik Lee, and Nam P. Suh. A function-based framework for understanding biological systems. Annual Review of Biophysics and Biomolecular Structure, 33:75-93, 2004.
29. L. Trevisan. Lecture notes on computational complexity. Available from http://www.cs.berkeley.edu/~luca/notes/complexitynotes02.pdf, 2002. Lecture 12.
30. Michael P. Wellman. The economic approach to artificial intelligence. ACM Computing Surveys, 27:360-362, 1995.