By Kurumatani K. (ed.), Chen S.-H. (ed.), Ohuchi A. (ed.)

This e-book originates from the IJCAI 2003 overseas Workshop on Multi-Agents for Mass consumer aid, MAMUS 2003, held in Acapulco, Mexico in August 2003. in addition to revised chosen workshop papers, the quantity editors invited contributions by way of prime researchers to be able to whole insurance of vital aspects.The papers deal with significant present problems with multi-agent know-how and its functions to aid mass clients and society extra usually through the use of social coordination mechanisms. The papers are equipped into topical sections at the theoretical history, source allocation algorithms, mass person aid in site visitors structures, video game theoretic research, and architectures for social coordination mechanisms.

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Multi-Agent for Mass User Support

This publication originates from the IJCAI 2003 overseas Workshop on Multi-Agents for Mass consumer help, MAMUS 2003, held in Acapulco, Mexico in August 2003. in addition to revised chosen workshop papers, the amount editors invited contributions by means of best researchers on the way to whole insurance of vital points.

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Only when replictability is guaranteed, can one ground the foundation of agent engineering in experiments involving human agents. Leaving aside ACE, the failure to incorporate these computational intelligence techniques into the current experimental economics naturally limits the computational complexity of human decision rules. This limitation can be a real concern when the human behavior which experimental economics tries to study is largely inspired by a computationally-rich environment. Specifically, in an era of electronic commerce, when more and more automated trading techniques are being made available to human agents, questions regarding the dynamics and the efficiency of different auction designs may no longer be properly answered by the experiments conducted in the conventional lab.

This is because most labs in which experiments involving human subjects are conducted do not provide subjects with high-performance computing facilities. Consequently, many adaptive schemes studied in agent-based computational economics are virtually impossible for human agents to compute, and the empirical relevance of adaptive schemes such as fuzzy logic, neural networks, 30 Shu-Heng Chen and Chung-Ching Tai and genetic algorithms is beyond the current research capacity of experimental economics.

It is quite common in behavioral economics for human choices to not necessarily be rational or scientific. Various biases have been well established empirically in behavioral economics. In summary, the relevance of computational intelligence to human adaptive behavior cannot be appropriately studied without a CE laboratory (a lab that satisfies the computational equivalence condition). 3 Complex Systems Comprising Human and Software Agents The CE lab described above not only serves as a starting point toward a foundation of agent engineering but also function as a platform to study the behavior of electronic markets.

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