Pub. Date:
SAGE Publications
Agent-Based Models / Edition 1

Agent-Based Models / Edition 1

by Nigel Gilbert


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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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Product Details

ISBN-13: 9781412949644
Publisher: SAGE Publications
Publication date: 09/14/2007
Series: Quantitative Applications in the Social Sciences , #153
Edition description: New Edition
Pages: 112
Product dimensions: 5.50(w) x 8.50(h) x (d)

About the Author

Nigel Gilbert read for a first degree in Engineering, intending to go into the computer industry. However, he was lured into sociology and obtained his doctorate on the sociology of scientific knowledge from the University of Cambridge, under the supervision of Michael Mulkay. His research and teaching interests have reflected his continuing interest in both sociology and computer science (and engineering more widely).

His main research interests are processual theories of social phenomena, the development of computational sociology and the methodology of computer simulation, especially agent-based modelling. He is Director of the Centre for Research in Social Simulation.

He is also Director of the University's Institute of Advanced Studies and responsible for its development as a leading centre for intellectual interchange.

He is the author or editor of several textbooks on sociological methods of research and statistics and editor of the Journal of Artificial Societies and Social Simulation.

Table of Contents

Series Editor's Introduction
1. The Idea of Agent-Based Modeling
1.1 Agent-Based Modeling
1.2 Some Examples
1.3 The Features of Agent-Based Modeling
1.4 Other Related Modeling Approaches
2. Agents, Environments, and Timescales
2.1 Agents
2.2 Environments
2.3 Randomness
2.4 Time
3. Using Agent-Based Models in Social Science Research
3.1 An Example of Developing an Agent-Based Model
3.2 Verification: Getting Rid of the Bugs
3.3 Validation
3.4 Techniques for Validation
3.5 Summary
4. Designing and Developing Agent-Based Models
4.1 Modeling Toolkits, Libraries, Languages, Frameworks, and Environments
4.2 Using NetLogo to Build Models
4.3 Building the Collectivities Model Step by Step
4.4 Planning an Agent-Based Model Project
4.5 Reporting Agent-Based Model Research
4.6 Summary
5. Advances in Agent-Based Modeling
5.1 Geographical Information Systems
5.2 Learning
5.3 Simulating Language
About the Author

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