By Simon J. E. Taylor (eds.)
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Extra info for Agent-Based Modeling and Simulation
Acknowledgements This work was supported by the US Department of Energy under contract number DEAC02-06CH11357. Tutorial on agent-based modeling and simulation 29 References Alber MS, Kiskowski MA, Glazier JA and Jiang Y (2003). On cellular automaton approaches to modeling biological cells. In: Rosenthal J and Gilliam DS (eds). Mathematical Systems Theory in Biology, Communication, and Finance, IMA Vol. 134, Springer: New York, pp 1À39. Alpaydin E (2004). Introduction to Machine Learning. MIT Press: Cambridge, MA.
Several of the papers covered here make the case that agent-based modeling, versus other modeling techniques is necessary because agent-based models can explicitly model the complexity arising from individual actions and interactions that exist in the real world. Agent-based model structure spans a continuum, from elegant, minimalist academic models to large-scale decision support systems. Minimalist models are based on a set of idealized assumptions, designed to capture only the most salient features of a system.
Major analysis mechanisms include visualization, data mining, statistics, and report generation. Most implementation environments allow modelers to produce output text or binary ﬁles during execution, primarily using programming. These output ﬁles can then be manually read into separate external analysis tools. Some implementation environments such as Repast Simphony (North et al, 2007; ROAD, 2009) and AnyLogic (XJ Technologies, 2009) include either builtin analysis tools or point-and-click mechanisms to create output ﬁles and directly invoke external analysis tools.