Thursday, September 30, 2004
Agent model yields leadership
Agent model yields leadership
Agent model yields leadership. By Kimberly Patch. Technology Research News (September 22/29, 2004). "Complicated systems that involve many agents making independent decisions -- like the stock market -- are difficult to predict. ... One way to gain at least a moderate ability to predict is to start in the middle -- construct a system using quantitative representations for agent-level behavior and interactions observed from real life, let the simulation evolve according to a set of rules, then compare the system to qualitative observations from real life to see how close the model has come to representing the behavior of a real system. Researchers from Los Alamos National Laboratory, the University of Houston, and Rensselaer Polytechnic Institute have developed a quantitative model of software agents competing for limited resources that is representative of more complex systems. The model is a simple, expandable framework that accounts for social behavior in agent-based markets, said Marion Anghel, a technical research staff member at Los Alamos National Laboratory. It could eventually be used to study financial markets, behavioral economics, and quantitative sociology, and to optimize agent communications networks, including robot collectives, said Anghel. The researchers based their system on an existing multi-agent-competition model dubbed the minority game.... The researchers added a network of acquaintances that give advice to each other based on the agents' predictions about the best move in the game at any given moment. ... The researchers are aiming to eventually produce artificial agent systems that perform optimally as a collective, said [Zoltan] Toroczkai."
Agent model yields leadership. By Kimberly Patch. Technology Research News (September 22/29, 2004). "Complicated systems that involve many agents making independent decisions -- like the stock market -- are difficult to predict. ... One way to gain at least a moderate ability to predict is to start in the middle -- construct a system using quantitative representations for agent-level behavior and interactions observed from real life, let the simulation evolve according to a set of rules, then compare the system to qualitative observations from real life to see how close the model has come to representing the behavior of a real system. Researchers from Los Alamos National Laboratory, the University of Houston, and Rensselaer Polytechnic Institute have developed a quantitative model of software agents competing for limited resources that is representative of more complex systems. The model is a simple, expandable framework that accounts for social behavior in agent-based markets, said Marion Anghel, a technical research staff member at Los Alamos National Laboratory. It could eventually be used to study financial markets, behavioral economics, and quantitative sociology, and to optimize agent communications networks, including robot collectives, said Anghel. The researchers based their system on an existing multi-agent-competition model dubbed the minority game.... The researchers added a network of acquaintances that give advice to each other based on the agents' predictions about the best move in the game at any given moment. ... The researchers are aiming to eventually produce artificial agent systems that perform optimally as a collective, said [Zoltan] Toroczkai."
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