Agent-Based Modeling and Simulation by Simon J. E. Taylor (eds.)

By Simon J. E. Taylor (eds.)

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Hill RR, Carl RG and Champagne LE (2006). Using agent simulation models to examine and investigate search theory against a historical case study. J Simul 1(1): 29À38. Holland J (1995). Hidden Order: How Adaptation Builds Complexity. Addison-Wesley: Reading, MA. Holland JH et al (2000). What is a learning classifier system? In: Lanzi PL, Stolzmann W and Wilson SW (eds). Learning Classifier Systems, from Foundations to Applications. Springer-Verlag: London, UK, pp 3À32. Jennings NR (2000). On agent-based software engineering.

This special issue adds to the growing list of agent-based model applications. Qu et al use their model of egg plant growth to promote understanding of the Tutorial on agent-based modeling and simulation 23 interactions between plant architecture and physiological processes. Chen and Hardoon use their model to examine cell division and migration in the colonic crypt to better understand the mechanisms of tumorigenesis. 1 Methods for agent-based modeling Agent model design When developing an agent-based model, it is useful to ask a series of questions, the answers to which will lead to an initial model design: 1.

Installers usually have graphical wizard-based interfaces that make installation more reliable than for the other distribution approaches because of the ability of the 28 CM Macal and MJ North installation software to automatically fix common configuration issues. Webbased execution embeds a packaged model into a web page for execution from within a browser. Web-based execution is differentiated from simply making raw files or an installer available from a website in that it requires models to execute from within a browser or browser plug-in rather than simply being downloaded and installed from an online source.

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