Agent-Mediated Electronic Commerce and Trading Agent Design by Florin Constantin, David C. Parkes (auth.), John Collins,

By Florin Constantin, David C. Parkes (auth.), John Collins, Peyman Faratin, Simon Parsons, Juan A. Rodriguez-Aguilar, Norman M. Sadeh, Onn Shehory, Elizabeth Sklar (eds.)

This booklet constitutes the completely refereed post-conference complaints of the ninth overseas Workshop on Agent-Mediated digital trade, AMEC IX, co-located with the 6th foreign Joint convention on independent brokers and Multiagent platforms, AAMAS 2007, held in Honolulu, Hawai, in may perhaps 2007, and the fifth Workshop on buying and selling Agent layout and research, TADA 2007, co-located with the Twenty-Second AAAI convention on man made Intelligence, AAAI 2007, held in Vancouver, Canada, in July 2007.

This quantity provides 15 rigorously revised and chosen papers from those workshops. the first and complementary objective of either workshops was once to proceed to compile novel paintings from different fields on modeling, implementation and assessment of computational buying and selling associations and/or agent suggestions. The papers originating from AMEC concentrate on a wide number of concerns on auctions, negotiation, and strategic habit in digital marketplaces. The papers originating from TADA mirror the hassle of the group to layout eventualities the place buying and selling agent designers and industry designers could be pitched opposed to one another.

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Extra resources for Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis: AAMAS 2007 Workshop, AMEC 2007, Honolulu, Hawaii, May 14, 2007, and AAAI 2007 Workshop, TADA 2007, Vancouver, Canada, July 23, 2007, Selected and Revised Papers

Example text

First, the information available during a game about opponents is extremely limited. An opponent’s behavior is likely to be heavily dependent on information that cannot be observed, such as the opponent’s inventory. Second, the behavior of an agent may be dependent on the mix of opponents in a game and the market conditions resulting from this mix. We were able to observe this fact clearly from the results of the 2005 competition. Table 1 shows the scores of the top 12 (out of 25) agents in the seeding round.

Edward Elgar, Cheltenham (2000) 11. : Models of competitive bidding under uncertainty. Technical Report 8, Stanford University (1968) 12. : BOB: Improved winner determination in combinatorial auctions and generalizations. Artificial Intelligence 145, 33–58 (2003) 13. : Counterspeculation, auctions and competitive sealed tenders. Journal of Finance 16, 8–37 (1961) 14. : Multiple-object auctions. M. ) Auctions, bidding, and contracting: Uses and theory, pp. 165–191. New York University Press (1983) 15.

Vj for auction j) is his private information. As before, the equilibrium bids for an auction are obtained using backward reasoning. However, for this setting, a bidder’s ex-ante probability of winning auction y in the series from j to m (denoted β2 (y, j, m, n)) depends on the probability that a given auction is the last one. Thus, we first find β2 (y, j, m, n). To begin, consider the case where m = 2. For this case, 0 ≤ P L1 ≤ 1 and P L2 = 1. , the second auction is known by all to be the last one), β2 (1, 1, 2, n) = 1/n, and β2 (2, 1, 2, n) = (1 − 1/n)(1 − P L1 )(1/(n − 1)).

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