Agent-Based Models, Issue 153 (Google eBook)

Front Cover
SAGE, 2008 - Social Science - 98 pages
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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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Contents

The Idea of AgentBased Modeling
1
11 AgentBased Modeling
2
112 Experiments
3
114 Agents
5
115 The Environment
6
122 Opinion Dynamics
8
123 Consumer Behavior
9
124 Industrial Networks
10
312 MicroLevel Behavior
34
313 Designing a Model
35
314 Verification
38
33 Validation
40
331 Abstract Models
41
332 Middle Range Models
42
333 Facsimile Models
43
34 Techniques for Validation
44

125 Supply Chain Management
11
126 Electricity Markets
12
127 Participative and Companion Modeling
13
13 The Features of AgentBased Modeling
14
133 Representation of the Environment
15
136 Learning
16
141 Microsimulation
17
142 System Dynamics
18
Agents Environments and Timescales
21
211 Ad Hoc Programming
22
212 Production Rule Systems
23
213 Neural Networks
24
22 Environments
26
23 Randomness
27
24 Time
28
Using AgentBased Models in Social Science Research
30
31 An Example of Developing an AgentBased Model
32
311 MacroLevel Regularities
33
342 Comparing the Model and Empirical Data
45
35 Summary
46
411 Repast
48
414 Comparison
49
43 Building the Collectivities Model Step by Step
53
431 Commentary on the Program
55
44 Planning an AgentBased Modeling Project
64
45 Reporting AgentBased Model Research
65
46 Summary
68
52 Learning
70
522 Evolutionary Computation
71
53 Simulating Language
72
Resources
75
Glossary
77
References
81
Index
91
About the Author
98
Copyright

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