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

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 e-book constitutes the completely refereed post-conference court cases of the ninth foreign Workshop on Agent-Mediated digital trade, AMEC IX, co-located with the 6th foreign Joint convention on self sustaining 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 conscientiously revised and chosen papers from those workshops. the first and complementary target of either workshops was once to proceed to assemble novel paintings from assorted fields on modeling, implementation and assessment of computational buying and selling associations and/or agent recommendations. The papers originating from AMEC specialize 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 neighborhood to layout eventualities the place buying and selling agent designers and marketplace designers will 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

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Stone Table 5. 06 fixed model learned model 0 2 4 6 8 10 12 14 16 Number of Games Fig. 4. Comparison between the fixed predictor and learning from games third of each game, TacTex-06 had a higher average sales price than any opponent for every type of computer. 3 that was trained on all games from Groups 1, 2, and 3, as Group 4 is very similar to the actual agents competing in the finals) and the results that would have been obtained by learning only from completed games. To determine the latter for N completed games, we averaged the results of 20 runs in which we randomly chose N games for training and used the remaining 16 − N games as the test set, except in the cases of N = 1 and N = 15, for which we performed 15 runs by using each game once as the training (N = 1) or testing (N = 15) set.

This work can be broadly divided into two categories [7,6]: that which deals with homogeneous objects and that which deals with heterogeneous objects. The analysis of sequential auctions for homogeneous objects is very well developed for the special case where no bidder is interested in more than one unit. Work in this category deals primarily with the study of sale price dynamics and shows that even when identical objects are sold in a series, the sale price varies from auction to auction. For instance, Weber [14] showed that in sequential auctions of identical private value objects, the expected sale price is the same for each auction.

Let C denote the condition α1 (j + 1, m, n) < min{vj }. We first analyze the case where C is true and then the case where C is false2 . C True: For this case, vj − α1 (j + 1, m, n) is always positive, so the equilibrium bids are: Bj1 (vj ) = vj − α(j + 1, m, n) (7) Since the equilibrium bid for auction j decreases by α1 (j + 1, m, n) (relative to a single object auction), the auctioneer’s revenue decreases by the same amount. But the surplus for an auction (which is the sum of the winner’s profit and the auctioneer’s revenue) remains the same as that for a single object auction.

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