By Vincent Conitzer, Tuomas Sandholm (auth.), Peyman Faratin, Juan A. Rodríguez-Aguilar (eds.)
This publication constitutes the completely refereed post-proceedings of the sixth overseas Workshop on Agent-Mediated digital trade, AMEC 2006, held in ny, manhattan, united states in July 2004 as a part of AAMAS 2004.
The 15 revised complete papers provided have been conscientiously chosen from 39 submissions in the course of rounds of reviewing and revision. The papers assemble novel paintings from such varied fields as desktop technological know-how, Operations learn, synthetic Intelligence and dispensed structures that concentrate on modeling, implementation and evaluate of computational buying and selling establishment and/or agent recommendations over a various set of products. they're geared up 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 info 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
However, in terms of calculating the payments to the agents, our mechanism does impose a larger computational load. In the case of the VCG mechanism, calculating the payment involves performing the winner determination problem |I| times over the reduced set of agents I \ i (see  for more details). However in our case, the centre needs to successively reduce the value of A Mechanism for Multiple Goods and Interdependent Valuations 27 the report from each agent (and calculate the optimal allocation at each stage) until it reaches an allocation which is the optimal one for the reduced set of agents I \ i (see equations 2 and 3).
Agent A4 has improved its own utility from -3 to +3, but the true overall utility has decreased from +4 to -2. Other agents may follow similar reasoning and perturb the result further. 34 3 B. Faltings Achieving Incentive-Compatibility The incentives of each agent can be aligned with those of the group by making each agent pay a tax reﬂecting the cost that their preferences are causing to others. A well-known mechanism is the Vickrey-Clarke-Groves (VCG) tax mechanism ([14,5,8]). Its application for multi-agent decision making has already been proposed in .
Note that while auctions are a special case of social choice, reverse and double auctions as the outcome space is not independent of the agents (an agent may not agree to give up an item). In an incentive-compatible (IC) mechanism, the incentives of each agent are aligned with those of the group: the behavior that optimizes the utility of an individual agent also optimizes the utility of the group. When utility optimization is left to the social choice mechanism, this often corresponds to each agent being best oﬀ declaring its preferences truthfully; this is called truthful or strategyproof.