Scheutz, Matthias. “Artificial Life Simulations: Discovering and Developing Agent-Based Models.” In Model-Based Approaches to Learning , pp. 261-292. Brill Sense, 2009.
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Agent-based simulations have become increasingly important over the recent past in a wide variety of fields, ranging from the simulation of complex physical systems, to the modeling of different kinds of biological and social systems, to various applications in game theory and artificial intelligence. They have been used, for example, to model bacterial chemotaxis signaling pathways (e.g., Le Novre & Shimizu, 2001; Andrews & Bray, 2004), population ecology (e.g., Railsback et al., 2002; Anderson, 2002; Grimm, 1999), social, economic, and political systems (e.g., Conte, 2002; Schermerhorn & Scheutz, 2003), software engineering (e.g., Gao, Madey, & Freeh, 2005), neural networks (e.g., Schoenharl & Madey, 2004), business and commerce (e.g., Bonabeau, 2002) and many other areas. In particular, in artificial life (Alife) research, simulation environments are a critical tool for advancing knowledge and understanding of the mechanisms and principles that govern the emergence or evolution of life or likelike processes.