By A. Rogers, E. David, J. Schiff, S. Kraus, N. R. Jennings (auth.), Han La Poutré, Norman M. Sadeh, Sverker Janson (eds.)
This publication 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, during the IJCAI 2005 convention meetings.
The seven revised complete AMEC 2005 papers provided have been rigorously chosen. They handle a mixture of either theoretical and functional matters, taking a look at 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 awarded at AMEC 2004 has additionally been incorporated.
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 in addition to theoretical analyses and empirical reviews of agent suggestions in several buying and selling contexts.
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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
Study of Figure 5(b) learns that more aggressive overbidding, as expected from Figure 5(a), is a proﬁtable strategy. Additionally, a better choice in the learning rate (in this case higher ), results in higher aggregate proﬁts as agents more quickly adapt towards the equilibrium strategy. The above two results are of great importance in real-life models or more stochastic domains. J. A. 2 0 0 1 2 3 4 5 6 7 agents 8 9 10 0 500 1000 1500 2000 epochs Fig. 5. More speculative bidders (a) and better learning (b) outcome for smart agents competing in scenarios where resources are scarce.
2 3 Note that i’s true surplus is V1 − ci1 which is equal to vi1 /n − ci1 + j=i vj1 /n. But since vi1 /n − ci1 depends on i’s signals while j=i vj1 /n depends on the other bidders’ signals, the term ‘i’s surplus’ is also used to mean vi1 /n − ci1 . Log concavity means that the natural log of the densities is concave. This restriction is met by many commonly used densities like uniform, normal, chi-square, and exponential, and it ensures that optimal bids are increasing in surplus. Again see  for more details.
P. Benoit and V. Krishna. Multiple-object auctions with budget constrained bidders. Review of Economic Studies, 68:155–179, 2001. 3. D. Bernhardt and D. Scoones. A note on sequential auctions. The American Economic Review, 84(3):653–657, 1994. 4. P. Dasgupta and E. Maskin. Efficient auctions. Quarterly Journal of Economics, 115:341– 388, 2000. 5. H. David. Order Statistics. Wiley, New York, 1969. 6. M. Dwass. Probability and Statistics. W. A. , California, 1970. 7. W. Elmaghraby. The importance of ordering in sequential auctions.
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 by A. Rogers, E. David, J. Schiff, S. Kraus, N. R. Jennings (auth.), Han La Poutré, Norman M. Sadeh, Sverker Janson (eds.)