By A. Rogers, E. David, J. Schiff, S. Kraus, N. R. Jennings (auth.), Han La Poutré, Norman M. Sadeh, Sverker Janson (eds.)
This e-book constitutes the completely refereed post-proceedings of the seventh overseas Workshop on Agent-Mediated digital trade, AMEC VII 2005, held in Utrecht, Netherlands in July 2005, as a part of AAMAS 2005, and the 3rd Workshop on buying and selling Agent layout and research, TADA 2005, held in Edinburgh, united kingdom in August 2005, throughout the IJCAI 2005 convention meetings.
The seven revised complete AMEC 2005 papers awarded have been rigorously chosen. They tackle a mixture of either theoretical and functional matters, behavioral and organizational dimensions of agent-mediated digital trade in addition to at complicated computational, details and system-level demanding situations. a longer model of an editorial initially offered at AMEC 2004 has additionally been integrated.
The moment a part of the publication contains eight revised complete papers of TADA 2005 that target buying and selling agent applied sciences and mechanism layout, together with discussions of agent architectures and decision-making algorithms besides theoretical analyses and empirical reviews of agent ideas in several buying and selling contexts.
Read or Download Agent-Mediated Electronic Commerce. Designing Trading Agents and Mechanisms: AAMAS 2005 Workshop, AMEC 2005, Utrecht, Netherlands, July 25, 2005, and IJCAI 2005 Workshop, TADA 2005, Edinburgh, UK, August 1, 2005, Selected and Revised Papers PDF
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Extra resources for Agent-Mediated Electronic Commerce. Designing Trading Agents and Mechanisms: AAMAS 2005 Workshop, AMEC 2005, Utrecht, Netherlands, July 25, 2005, and IJCAI 2005 Workshop, TADA 2005, Edinburgh, UK, August 1, 2005, Selected and Revised Papers
12. J. Laffont. Game theory and empirical economics: The case of auction data. European Economic Review, 41:1–35, 1997. 13. R. P. McAfee and D. Vincent. The declining price anomaly. Journal of Economic Theory, 60:191–212, 1993. An Analysis of Sequential Auctions for Common and Private Value Objects 41 14. P. Milgrom and R. J. Weber. A theory of auctions and competitive bidding II. In P. Klemperer, editor, The Economic Theory of Auctions. K, 2000. 15. A. Ortega-Reichert. Models of competitive bidding under uncertainty.
To begin, consider the mth auction for which there are (n − m + 1) bidders. Since this is the last auction, an agent’s bidding behaviour is the same as that for the single object case. Hence, the equilibrium for this auction is the same as that in Equation 3 with n replaced with (n − m + 1). For j = 1, . . , m, let αij denote an agent’s cumulative ex-ante expected profit from auctions j to m. Recall that although the bidders know the distribution (from which the cost and value signals are drawn) before the first auction begins, they draw the signals for the jth auction only after the (j − 1) earlier auctions end.
M − 1)th auction. Consider the (m − 1)th auction. During this auction, a bidder bids b if (Vm−1 −cm−1 −b ≥ αm ) or (b ≤ Vm−1 −cm−1 −αm ). S. Fatima, M. R. Jennings Equation 8. We know from Equation 4, that the expected revenue for the single object case is the second order statistic of the surplus. The difference between the equilibrium bids for the single object case and the (m − 1)th auction of the m objects case is αm (see Equations 3 and 8). Hence, the expected revenue for the (m − 1)th auction is E(sn−m+2 ) − αm .