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 ebook constitutes the completely refereed post-conference court cases of the ninth overseas Workshop on Agent-Mediated digital trade, AMEC IX, co-located with the 6th overseas Joint convention on self reliant brokers and Multiagent platforms, AAMAS 2007, held in Honolulu, Hawai, in could 2007, and the fifth Workshop on buying and selling Agent layout and research, TADA 2007, co-located with the Twenty-Second AAAI convention on synthetic Intelligence, AAAI 2007, held in Vancouver, Canada, in July 2007.

This quantity offers 15 rigorously revised and chosen papers from those workshops. the first and complementary target of either workshops was once to proceed to compile novel paintings from varied fields on modeling, implementation and overview of computational buying and selling associations and/or agent options. The papers originating from AMEC specialise in a wide number of matters on auctions, negotiation, and strategic habit in digital marketplaces. The papers originating from TADA mirror the hassle of the group to layout situations the place buying and selling agent designers and industry designers might be pitched opposed to one another.

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Additional 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

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Let β¯3 (y, j, m, N ) be a bidder’s ex-ante probability of winning auction y in the series of auctions from j to m, if the number of bidders in each auction is as given in N . For the case where the number of bidders is not known for the individual auctions, we let α3 (j, m) denote a bidder’s ex-ante expected profit from winning an auction in the series from j to m. Then we get the following equations: 1 × β¯3 (y, j, m, N ) = Ny m α ¯ 3 (j, m, N ) = y−1 (1 − k=j 1 ) Nk ¯ 3 (y, m, N ) β¯3 (y, j, m, N )EP y=j ¯ 3 (y, m, N ) is: where α ¯3 (m + 1, m, N ) = 0 and EP y ¯ 3 (y, m, N ) = E(fyNy ) − E(sN EP ¯ 3 (y + 1, m, N ) y )+α Since the number of bidders for each auction lies between 1 and n, it follows that α3 (m − 1, m) is: n n P N (m − 1, Nm−1 ) × P N (m, Nm ) × β¯3 (m − 1, m − 1, m, N ) Nm−1 =1 Nm =1 ¯ 3 (m − 1, m, N ) ×EP Sequential Auctions in Uncertain Information Settings 25 and, in general, α3 (j, m) is: n α3 (j, m) = n m ...

Hence given α1 (y, m, n) for j + 1 ≤ y ≤ m, we can find α1 (j, m, n) using Equation 2. 22 S. Fatima, M. R. Jennings Between Case 1, Case 2, and Case 3, if we assume it is Case 3 (note that under this assumption, the equilibrium bids are as given in Equation 7; so EP1 , ES1 , and ER1 are as given in Equations 8, 9, and 10 respectively) then the expressions for finding EP1 are easier to deal with because we do not have conditional expectations. Moreover, this case is important because, in general, for a large number of bidders, it is quite likely that P2 = 1.

All the bidders know that there are no more than m objects for sale. f. 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.

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