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Towards a combinatorial auction protocol among experts and amateurs: the case of single-skilled experts

Published:14 July 2003Publication History

ABSTRACT

Auctions have recently commanded a great deal of attention in the field of multi-agent systems. Correctly judging the quality of auctioned goods is often difficult for amateurs, in particular, on the Internet auctions. We have formalized 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, we have succeeded in developing a single unit auction protocol in which truth telling is a dominant strategy for each expert. In this paper, we focus on a combinatorial auction protocol under asymmetric information on Nature's selection. Experts may have an interest in, and expert knowledge on, Nature's selection for several goods, i.e., experts are versatile. However, the case of versatile experts is very complicated. Thus, as a first step, we assume experts to have an interest in, and expert knowledge on, a single good. That is, experts are single-skilled. Under these assumptions, we develop an auction protocol in which the dominant strategy for experts is truth telling. Also, for amateurs, truth-telling is the best response when experts tell the truth. By making experts to elicit their information on the quality of the goods, the protocol can achieve a Pareto efficient allocation, if certain assumptions are satisfied.

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        cover image ACM Conferences
        AAMAS '03: Proceedings of the second international joint conference on Autonomous agents and multiagent systems
        July 2003
        1200 pages
        ISBN:1581136838
        DOI:10.1145/860575

        Copyright © 2003 ACM

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        Publication History

        • Published: 14 July 2003

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