Agent-based modeling (ABM) is a technique increasingly used in a broad range of social sciences. It involves building a computational model consisting of "agents," each of which represents an actor in the social world, and an "environment" in which the agents act. Agents are able to interact with each other and are programmed to be pro-active, autonomous and able to perceive their virtual world. The techniques of ABM are derived from artificial intelligence and computer science, but are now being developed independently in research centers throughout the world.
In Agent-Based Models, Nigel Gilbert reviews a range of examples of agent-based modeling, describes how to design and build your own models, and considers practical issues such as verification, validation, planning a modeling project, and how to structure a scholarly article reporting the results of agent-based modeling. It includes a glossary, an annotated list of resources, advice on which programming environment to use when creating agent-based models, and a worked, step-by-step example of the development of an ABM.
This latest volume in the SAGE Quantitative Applications in the Social Sciences series will have wide appeal in the social sciences, including the disciplines of sociology, economics, social psychology, geography, economic history, science studies, and environmental studies. It is appropriate for graduate students, researchers and academics in these fields, for both those wanting to keep up with new developments in their fields and those who are considering using ABM for their research.
Key Features
Aimed at readers who are new to ABM
Offers a brief, but thorough, treatment of a cutting-edge technique
Offers practical advice about how to design and create ABM
Includes carefully chosen examples from different disciplines
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Nigel Gilbert is Professor of Sociology at the University of Surrey, Guildford, England. He is the author or editor of 34 books and many academic papers and was the founding editor of the Journal of Artificial Societies and Social Simulation. His current research focuses on the application of agent-based models to understanding social and economic phenomena, especially the emergence of norms, culture, and innovation. He obtained a doctorate in the sociology of scientific knowledge in 1974 from the University of Cambridge and has subsequently taught at the universities of York and Surrey in England. He is one of the pioneers in the field of social simulation and is past president of the European Social Simulation Association. He is a Fellow of the UK Academy of Social Sciences and of the Royal Academy of Engineering.
