ISEM: a multiagent Simulator for testing agent market strategies

With the increasing importance of e-commerce across the Internet, the need for software agents to support customers and suppliers in buying and selling goods/services is growing rapidly. It is becoming increasingly evident that in a few years the Internet will host a large number of interacting soft...

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Bibliographic Details
Published in:IEEE transactions on human-machine systems 2006-01, Vol.36 (1), p.107-113
Main Authors: Viamonte, M.J., Ramos, C., Rodrigues, F., Cardoso, J.C.
Format: Article
Language:English
Subjects:
agent risk preferences
Agent-based simulation
Algorithm design and analysis
Algorithms
Analytical models
Computer simulation
Data analysis
Data mining
dynamic agent strategic behavior
Economics
electronic markets
Feedback
Internet
Markets
Multiagent systems
Pricing
Risk analysis
Software
Software agents
Strategy
Studies
Testing
Web and internet services
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Summary:With the increasing importance of e-commerce across the Internet, the need for software agents to support customers and suppliers in buying and selling goods/services is growing rapidly. It is becoming increasingly evident that in a few years the Internet will host a large number of interacting software agents. Most of them will be economically motivated, and will negotiate a variety of goods and services. It is therefore important to consider the economic incentives and behaviors of economic software agents, and to use all available means to anticipate their collective interactions. This paper addresses this concern by presenting a multiagent market simulator designed for analyzing agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, and considering risk preferences. The system includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions. The results of the negotiations between agents are analyzed by data mining algorithms to extract rules that give agents feedback to improve their strategies
ISSN:1094-6977
2168-2291
1558-2442
2168-2305
DOI:10.1109/TSMCC.2005.860592