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Modeling Cognitive Agents for Social Systems and a Simulation in Urban Dynamics
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Author(s): Yu Zhang (Trinity University, USA), Mark Lewis (Trinity University, USA), Christine Drennon (Trinity University, USA), Michael Pellon (Trinity University, USA), Phil Coleman (Trinity University, USA) and Jason Leezer (Trinity University, USA)
Copyright: 2010
Pages: 21
Source title:
Social Computing: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Subhasish Dasgupta (George Washington University, USA)
DOI: 10.4018/978-1-60566-984-7.ch138
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Abstract
Multi-agent systems have been used to model complex social systems in many domains. The entire movement of multi-agent paradigm was spawned, at least in part, by the perceived importance of fostering human-like adjustable autonomy and behaviors in social systems. But, efficient scalable and robust social systems are difficult to engineer. One difficulty exists in the design of how society and agents evolve and the other difficulties exist in how to capture the highly cognitive decision-making process that sometimes follows intuition and bounded rationality. We present a multi-agent architecture called CASE (Cognitive Agents for Social Environments). CASE provides a way to embed agent interactions in a three-dimensional social structure. It also presents a computational model for an individual agent’s intuitive and deliberative decision-making process. This chapter also presents our work on creating a multi-agent simulation which can help social and economic scientists use CASE agents to perform their tests. Finally, we test the system in an urban dynamic problem. Our experiment results suggest that intuitive decision-making allows the quick convergence of social strategies, and embedding agent interactions in a three-dimensional social structure speeds up this convergence as well as maintains the system’s stability.
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