Online mechanisms

Algorithmic Game Theory

Volumn 9780521872829, Issue , 2007, Pages 411-440

  Parkes, David C ^a  

^a HARVARD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MACHINE DESIGN; MULTI AGENT SYSTEMS; SALES; SEARCH ENGINES;

COMPUTATIONAL RESOURCES; DYNAMIC ENVIRONMENTS; FUTURE RESEARCH DIRECTIONS; INCENTIVE COMPATIBLE; MULTI-AGENT PROBLEMS; ON-LINE ALGORITHMS; PRIVATE INFORMATION; PROBABILISTIC MODELING;

DYNAMICS;

EID: 84926103783     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1017/CBO9780511800481.018     Document Type: Chapter

References (36)

1. S. Athey and I. Segal. An efficient dynamic mechanism. Technical report, Harvard University and Stanford University, 2007.
2. B. Awerbuch, Y. Azar, and A. Meyerson. Reducing truth-telling online mechanisms to online optimization. In Proc. 35th Symp. on Theory of Computing, 503–510, 2003.
3. M. Babaioff, N. Immorlica, and R. Kleinberg. Matroids, secretary problems, and online mechanisms. In Proc. 18th Symp. Discrete Algorithms, 434–443, 2007.
4. M. Babaioff, R. Lavi, and E. Pavlov. Mechanism design for single-value domains. In Proc. 20th Natl. Conf. on Artificial Intelligence, pp. 241–247, 2005.
5. A. Bapna and T.A. Weber. Efficient dynamic allocation with uncertain valuations. Technical report, Stanford University, 2006.
6. Z. Bar-Yossef, K. Hildrum, and F. Wu. Incentive-compatible online auctions for digital goods. In Proc. 13th ACM-SIAM Symp. Discrete Algorithms (SODA’02), 964–970, 2002.
7. D. Bergemann and J. Välimäki. Dynamic common agency. J. Econ. Theory, 11:23–48, 2003.
8. D. Bergemann and J. Välimäki. Dynamic price competition. J. Econ. Theory, 127:232–263, 2006a.
9. D. Bergemann and J. Välimäki. Efficient dynamic auctions. Cowles Foundation Discussion Paper No. 1584, Yale University, 2006.
10. S. Bikhchandani, S. Chatterji, R. Lavi, A. Mu’alem, N. Nisan, and A. Sen. Weak monotonicity characterizes deterministic dominant strategy implementation. Econometrica, 74:1109–1132, 2006.
11. A. Blum and J. Hartline. Near-optimal online auctions. In Proc. 16th Symp. on Discrete Algorithms, 1156–1163, 2005.
12. A. Blum, V. Kumar, A. Rudra, and F. Wu. Online learning in online auctions. In Proc. 14th Symp. Discrete Algorithms, 137–143, 2003.
13. A. Blum, T. Sandholm, and M. Zinkevich. Online algorithms for market clearing. J. ACM, 53:845–875, 2006.
14. A. Borodin and R. El-Yaniv. Online Computation and Competitive Analysis. Cambridge University Press, 1998.
15. J. Bredin and D.C. Parkes. Models for truthful online double auctions. In Proc. 21st Conf. on Uncertainty in Artificial Intelligence, pp. 50–59, 2005.
16. R. Cavallo, D.C. Parkes, and S. Singh. Optimal coordinated learning among self-interested agents in the multi-armed bandit problem. In Proc. 22nd Conf. Uncertainty in Artificial Intelligence (UAI’2006), pp. 55–62, Cambridge, MA, 2006.
17. F. Constantin, T. Ito, and D.C. Parkes. Online auctions for bidders with interdependent values. In Proc. 6th Int. Conf. on Autonomus Agents and Multiagent Systems (AAMAS 07) poster paper, 2007.
18. E.B. Dynkin. The optimum choice of the instant for stopping a Markov process. Sov. Math. Dokl., 4:627–629, 1963.
19. E. Friedman and D.C. Parkes. Pricing WiFi at Starbucks – Issues in online mechanism design. In Proc. 4th ACM Conf. on Electronic Commerce (EC’03), pp. 240–241, 2003.
20. J. Gallien. Dynamic mechanism design for online commerce. Oper. Res., 54:291–310, 2006.
21. J. Gilbert and F. Mosteller. Recognizing the maximum of a sequence. J. Amer. Statist. Assoc., 61(313):35–73, 1966.
22. M.T. Hajiaghayi, R. Kleinberg, M. Mahdian, and D.C. Parkes. Online auctions with re-usable goods. In Proc. 6th ACM Conf. on Electronic Commerce (EC’05), pp. 165–174, 2005.
23. M.T. Hajiaghayi, R. Kleinberg, and D.C. Parkes. Adaptive limited-supply online auctions. In Proc. 5th ACM Conf. on Electronic Commerce (EC’04), pp. 71–80, 2004.
24. P. Van Hentenryck and R. Bent. Online Stochastic Combinatorial Optimization. MIT Press, 2006.
25. A. Juda and D. Parkes. The sequential auction problem on eBay: An empirical analysis and a solution. In Proc. 7th ACM Conf. on Electronic Commerce (EC’06), pp. 180–189, 2006.
26. M. Kearns, Y. Mansour, and A.Y. Ng. A sparse sampling algorithm for near-optimal planning in large Markov Decision Processes. In Proc. 16th Int. Joint Conf. on Artificial Intelligence (IJCAI’99), pp. 1324–1331, 1999.
27. R. Lavi and N. Nisan. Competitive analysis of incentive compatible on-line auctions. In Proc. 2nd ACMConf. on Electronic Commerce (EC-00), 233–241, 2000.
28. R. Lavi and N. Nisan. Online ascending auctions for gradually expiring goods. In Proc. 16th Annual ACM-SIAM Symp. on Discrete Algorithms (SODA’05), 2005.
29. R.B. Myerson. Optimal auction design. Math. of Oper. Res., 6:58–73, 1981.
30. C. Ng, D.C. Parkes, and M. Seltzer. Virtual Worlds: Fast and Strategyproof auctions for dynamic resource allocation. In Proc. 4th ACM Conf. on Electronic Commerce (EC’03)short paper, pp. 238–239, 2003.
31. M. Pai and R. Vohra. Notes on optimal dynamic auctions. Kellogg School of Management, Northwestern University, 2006. Available from the authors.
32. D.C. Parkes and Q. Duong. An ironing-based approach to adaptive online mechanism design in single-valued domains. In Proc. 22nd Annual Conf. on Artificial Intelligence, 2007.
33. D.C. Parkes and S. Singh. An MDP-based approach to online mechanism design. In Proc. 17th Annual Conf. on Neural Information Processing Systems (NIPS’03), 2003.
34. D.C. Parkes, S. Singh, and D. Yanovsky. Approximately efficient online mechanism design. In Proc. 18th Annual Conf. on Neural Information Processing Systems (NIPS’04), 2004.
35. R. Porter. Mechanism design for online real-time scheduling. In Proc. 5th ACM Conf. on Electronic Commerce (EC’04), 61–70, 2004.
36. M.L. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. JohnWiley & Sons, New York, 1994.