Adaptive mechanism design: A metalearning approach

Proceedings of the ACM Conference on Electronic Commerce

Volumn , Issue , 2006, Pages 92-102

  Pardoe, David ^a   Stone, Peter ^a   Saar Tsechansky, Maytal ^b   Tomak, Kerem ^b  

^a University of Texas at Austin   (United States)

^b UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

Auctions; Machine learning; Mechanism design; Metalearning

Indexed keywords

AUCTION MECHANISMS; EMPIRICAL BIDDERS; METALEARNING PROCESSES;

LEARNING SYSTEMS; PARAMETER ESTIMATION; PROCESS DESIGN;

ELECTRONIC COMMERCE;

EID: 34547301235     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1151454.1151480     Document Type: Conference Paper

References (21)

1. C. G. Atkeson, A. W. Moore, and S. Schaal. Locally weighted learning. Artificial Intelligence Review, 11(1-5):11-73, 1997.
2. A. Blum and J. D. Hartline. Near-optimal online auctions. In SODA '05: Proceedings of the sixteenth annual ACM-SIAM Symposium on Discrete Algorithms, pages 1156-1163, 2005.
3. A. Blum, V. Kumar, A. Rudra, and F. Wu. Online learning in online auctions. In SODA '03: Proceedings of the fourteenth annual ACM-SIAM Symposium on Discrete Algorithms, 2003.
4. A. Byde. Applying evolutionary game theory to auction mechanism design. In Proceedings of the 4th ACM conference on Electronic commerce, pages 192-193. ACM Press, 2003.
5. D. Cliff. Evolution of market mechanism through a continuous space of auction types. Technical Report HPL-2001-326, HP Labs, 2001.
6. P. C. Cramton. The FCC spectrum auctions: An early assessment. Journal of Economics and Management Strategy, 6(3):431-495, 1997.
7. D. A. V. Dittrich, W. Guth, M. Kocher, and P. Pezanis-Christou. Loss aversion and learning to bid. Technical Report 2005-03, Max Planck Institute for Research into Economic Systems, Strategic Interaction Group, 2005.
8. A. Dodonova and Y. Khoroshilov. Optimal auction design when bidders are loss averse. Working Paper. University of Ottawa.
9. D. Kahneman and A. Tversky. Prospect theory: An analysis of decision under risk. Econometrica, 47(2):263-291, 1979.
10. R. B. Myerson. Optimal auction design. Mathematics of Operations Research, 6(1):58-73, feb 1981.
11. D. Pardoe and P. Stone. Developing adaptive auction mechanisms. SIGecom Exchanges, 5(3):1-10, 2005.
12. D. C. Parkes. Iterative Combinatorial Auctions: Achieving Economic and Computational Efficiency. PhD thesis, Department of Computer and Information Science, University of Pennsylvania, May 2001.
13. S. Phelps, P. Me Burnley, S. Parsons, and E. Sklar. Co-evolutionary auction mechanism design. In Agent Mediated Electronic Commerce IV, volume 2531 of Lecture Notes in Artificial Intelligence. Springer Verlag, 2002.
14. A. Rogers, E. David, J. Schiff, S. Kraus, and N. R. Jennings. Learning environmental parameters for the design of optimal english auctions with discrete bid levels. In AAMAS 2005 Workshop on Agent Mediated Electronic Commerce VII, 2005.
15. M. Saar-Tsechansky and F. Provost. Active learning for class probability estimation and ranking. Machine Learning, 2004.
16. G. Shmueli, W. Jank, A. Aris, C. Plaisant, and B. Shneiderman. Exploring auction databases through interactive visualization. In Decision Support Systems, 2005. To appear.
17. J. C. Spall. An overview of the simultaneous perturbation method for efficient optimization. Johns Hopkins APL Technical Digest, 19:482-492, 1998.
18. R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA, 1998.
19. R. Vilalta and Y. Drissi. A perspective view and survey of meta-learning. Artificial Intelligence Review, 18(2):77-95, 2002.
20. R. J. Weber. Making more from less: Strategic demand reduction in the FCC spectrum auctions. Journal of Economics and Management Strategy, 6(3):529-548, 1997.
21. P. R. Wurman, M. P. Wellman, and W. E. Walsh. A parameterization of the auction design space. Journal of Games of Economic Behavior, 35:304-338, 2001.