"Agent-Based Models is a short, straightforward primer that introduces an increasingly popular form of modeling in the social sciences. Reviewing a range of examples, author Nigel Gilbert gives practical advice on how to design and build agent-based models. The book also considers practical issues such as planning a modeling project, verification, validation, and how to structure a scholarly article reporting the results of agent-based modeling."--BOOK JACKET.
Series Statement
Quantitative applications in the social sciences ; no. 07-153
Uniform Title
Quantitative applications in the social sciences ; no. 07-153.
Includes bibliographical references (p. 81-90) and index.
Processing Action (note)
committed to retain
Contents
1. The Idea of Agent-Based Modeling -- 1.1 Agent-Based Modeling -- 1.1.1 A Computational Method -- 1.1.2 Experiments -- 1.1.3 Models -- 1.1.4 Agents -- 1.1.5 The Environment -- 1.2 Some Examples -- 1.2.1 Urban Models -- 1.2.2 Opinion Dynamics -- 1.2.3 Consumer Behavior -- 1.2.4 Industrial Networks -- 1.2.5 Supply Chain Management -- 1.2.6 Electricity Markets -- 1.2.7 Participative and Companion Modeling -- 1.3 The Features of Agent-Based Modeling -- 1.3.1 Ontological Correspondence -- 1.3.2 Heterogeneous Agents -- 1.3.3 Representation of the Environment -- 1.3.4 Agent Interactions -- 1.3.5 Bounded Rationality -- 1.3.6 Learning -- 1.4 Other Related Modeling Approaches -- 1.4.1 Microsimulation -- 1.4.2 System Dynamics -- 2. Agents, Environments, and Timescales -- 2.1 Agents -- 2.1.1 Ad-Hoc Programming -- 2.1.2 Production Rule Systems -- 2.1.3 Neural Networks -- 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 -- 3.1.1 Macro-Level Regularities -- 3.1.2 Micro-Level Behavior -- 3.1.3 Designing a Model -- 3.1.4 Verification -- 3.2 Verification: Getting Rid of the Bugs -- 3.3 Validation -- 3.3.1 Abstract Models -- 3.3.2 Middle Range Models -- 3.3.3 Facsimile Models -- 3.4 Techniques for Validation -- 3.4.1 Comparing Theory and the Model: Sensitivity Analysis -- 3.4.2 Comparing the Model and Empirical Data -- 3.5 Summary -- 4. Designing and Developing Agent-Based Models -- 4.1 Modeling Toolkits, Libraries, Languages, Frameworks, and Environments -- 4.1.1 Repast -- 4.1.2 Mason -- 4.1.3 NetLogo -- 4.1.4 Comparison -- 4.2 Using NetLogo to Build Models -- 4.3 Building the Collectivities Model Step by Step -- 4.3.1 Commentary on the Program -- 4.4 Planning an ABM Project -- 4.5 Reporting Agent-Based Model Research -- 4.6 Summary -- 5. Advances in Agent-Based Modeling -- 5.1 Geographical Information Systems (GIS) -- 5.2 Learning --^
5.2.1 Reinforcement Learning -- 5.2.2 Evolutionary Computation -- 5.3 Simulating Language -- Resources -- Glossary -- References -- Index -- About the Author.