Designing an auction protocol under asymmetric information on nature's selection

ABSTRACT

Internet auctions are becoming an especially popular part of Electronic Commerce and auction protocols have been studied very widely in the field of multi-agent systems and AI. However, correctly judging the quality of auctioned goods is often difficult for non-experts (amateurs), in particular, on the Internet auctions. In this paper, we formalize such a situation so that Nature selects the quality of the auctioned good. Experts can observe Nature's selection (i.e., the quality of the good) correctly, while amateurs including the auctioneer cannot. In other words, the information on Nature's selection is asymmetric between experts and amateurs. In this situation, it is difficult to attain an efficient allocation, since experts have a clear advantage over amateurs and they would not reveal their valuable information without some reward. Thus, in this paper, we develop a new auction protocol in which truth-telling is a dominant strategy for each expert. This can be done by putting these experts in a situation similar to Prisoner's Dilemma. If they cooperate and tell lies, they can exclude amateurs, but betraying is a dominant strategy. By making experts to elicit their information on the quality of the good, the protocol can achieve a socially desirable, i.e., Pareto efficient allocation if certain assumptions are satisfied.

REFERENCES

• 1: Craig Boutilier , Moisés Goldszmidt , Bikash Sabata, Sequential Auctions for the Allocation of Resources with Complementarities, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, p.527-523, July 31-August 06, 1999
• 2: Robert H. Guttman , Alexandros G. Moukas , Pattie Maes, Agent-mediated electronic commerce: a survey, The Knowledge Engineering Review, v.13 n.2, p.147-159, July 1998 [doi>10.1017/S0269888998002082]
• 3: A Combinatorial Auction for Collaborative Planning, Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000), p.151, July 10-12, 2000
• 4: [4] R. D. Luce and H. Raiffa. Games and Decisions. Dover Publications, 1989.
• 5: [5] A. Mas-Colell, M. D. Whinston, and J. R. Green. Microeconomic Theory. Oxford University Press, 2nd edition, 1995.
• 6: Shigeo Matsubara, Accelerating information revelation in ascending-bid auctions: avoiding last minute bidding, Proceedings of the 3rd ACM conference on Electronic Commerce, p.29-37, October 14-17, 2001, Tampa, Florida, USA [doi>10.1145/501158.501162]
• 7: [7] P. R. Milgrom and R. J. Weber. A theory of auctions and competitive bidding. Econometrica, 50(5):1089-1122, 1982.
• 8: [8] E. Rasmusen. Games and Information. Blackwell Publishers Ltd, 2nd edition, 1994.
• 9: Tuomas Sandholm, eMediator: a next generation electronic commerce server, Proceedings of the fourth international conference on Autonomous agents, p.341-348, June 03-07, 2000, Barcelona, Spain [doi>10.1145/336595.337522]
• 10: Peter R. Wurman , Michael P. Wellman , William E. Walsh, The Michigan Internet AuctionBot: a configurable auction server for human and software agents, Proceedings of the second international conference on Autonomous agents, p.301-308, May 10-13, 1998, Minneapolis, Minnesota, United States [doi>10.1145/280765.280847]
• 11: Junichi Yamamoto , Katia Sycara, A stable and efficient buyer coalition formation scheme for e-marketplaces, Proceedings of the fifth international conference on Autonomous agents, p.576-583, May 2001, Montreal, Quebec, Canada [doi>10.1145/375735.376452]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence
            Subjects: Multiagent systems

Additional Classification:
  K. Computing Milieux
  K.4 COMPUTERS AND SOCIETY
      K.4.4 Electronic Commerce

General Terms:
Economics

Keywords:
auctions, mechanism design, multiagent systems