By Vincent Conitzer, Tuomas Sandholm (auth.), Peyman Faratin, Juan A. Rodríguez-Aguilar (eds.)
This e-book constitutes the completely refereed post-proceedings of the sixth overseas Workshop on Agent-Mediated digital trade, AMEC 2006, held in long island, manhattan, united states in July 2004 as a part of AAMAS 2004.
The 15 revised complete papers offered have been conscientiously chosen from 39 submissions in the course of rounds of reviewing and revision. The papers compile novel paintings from such various fields as machine technological know-how, Operations learn, synthetic Intelligence and dispensed platforms that target modeling, implementation and overview of computational buying and selling establishment and/or agent recommendations over a various set of products. they're equipped in topical sections on mechanism layout, buying and selling brokers, and tools.
Read or Download Agent-Mediated Electronic Commerce VI. Theories for and Engineering of Distributed Mechanisms and Systems: AAMAS 2004 Workshop, AMEC 2004, New York, NY, USA, July 19, 2004, Revised Selected Papers PDF
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Extra resources for Agent-Mediated Electronic Commerce VI. Theories for and Engineering of Distributed Mechanisms and Systems: AAMAS 2004 Workshop, AMEC 2004, New York, NY, USA, July 19, 2004, Revised Selected Papers
The process by which a seller sells options and leaves/returns to market can be made easier via a proxy, whereby a seller may tell a proxy her patience and good for sale, after which the proxy could then sell an option for that good at auction, observe the status of the sold option, and resell another option if the initial option is not exercised and the seller’s patience has yet to expire. Buyers can collect portfolios of options before deciding which to exercise.
The result lowers A3 ’s utility by 1 but increases A4 ’s utility by 6, so A4 can pay A3 3 units for its trouble and both will beneﬁt from the manipulation. A3 and A4 can impose this solution by each adding a ternary relation between all three variables that would give utility 100 to this value and 0 to all other combinations. Since the solution would remain the same if either A3 or A4 was removed, no agent would pay any taxes, so the manipulation comes for free. In auctions, collusion can be avoided by using mechanisms that make bidders pay their bid rather than the second highest bid.
For n = 10, this amounts to $2 000, signiﬁcantly less than with Mechanism 1. In fact, the VCG scheme will be better as long as n < 33. On randomly generated problems, we have observed that on average the VCG tax is much larger than the degradation in solution quality incurred by using a suboptimal solution. Figure 1 shows a comparison on randomly generated problems with 5 to 11 variables. The problems involve as many agents as variables. 14 Total optimal cost Total degradation Average VCG tax Total VCG tax 12 10 Cost 8 6 4 2 0 5 6 7 8 Number of Variables 9 10 11 Fig.