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By Simon J. E. Taylor (eds.)
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Extra resources for Agent-Based Modeling and Simulation
Many agent-based software and toolkits have been developed and are widely used. A combination of several synergistic factors is moving ABMS forward rapidly. These factors include the continuing development of specialized agent-based modeling methods and toolkits, the widespread application of agent-based modeling, the mounting collective experience of the agent-based modeling community, the recognition that behaviour is an important missing element in existing models, the increasing availability of micro-data to support agent-based models, and advances in computer performance.
Schelling showed that housing segregation patterns can emerge that are not necessarily implied or consistent with the objectives of the individual agents. Epstein and Axtell (1996) extended the notion of modeling people to growing entire artiﬁcial societies through agent-based simulation in the grid-based Sugarscape model. Sugarscape agents emerged with a variety of characteristics and behaviours, highly suggestive of a realistic, although rudimentary and abstract, society. These early grid-based models with limited numbers of social agents are now being extended to large-scale simulations over realistic social spaces such as social networks and geographies through real-time linkages with GIS.
As it is often the case, examining history can lead to insightful views about the past, present, and the future. Thus, themes from cellular automata and complexity, cybernetics and chaos, and complex adaptive systems are examined and placed in historical context to better establish the application, capabilities, understanding, and future of ABM. 1 Introduction Over the years agent-based modeling (ABM) has become a popular tool used to model and understand the many complex, nonlinear systems seen in our world (Ferber, 1999).
Agent-Based Modeling and Simulation by Simon J. E. Taylor (eds.)