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Agent-based and individual-based modeling : a practical introduction

Author: Steven F Railsback; Volker Grimm
Publisher: Princeton : Princeton University Press, ©2012.
Edition/Format:   Print book : EnglishView all editions and formats
Database:WorldCat
Summary:
"Agent-based modeling is a new technique for understanding how the dynamics of biological, social, and other complex systems arise from the characteristics and behaviors of the agents making up these systems. This innovative textbook gives students and scientists the skills to design, implement, and analyze agent-based models. It starts with the fundamentals of modeling and provides an introduction to NetLogo, an  Read more...
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Genre/Form: Textbooks
Einführung
Ders kitapları
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Steven F Railsback; Volker Grimm
ISBN: 9780691136738 0691136734 9780691136745 0691136742
OCLC Number: 724664023
Description: xviii, 329 pages : illustrations ; 26 cm
Contents: Machine generated contents note: pt. I Agent-Based Modeling and NetLogo Basics --
1.Models, Agent-Based Models, and the Modeling Cycle --
1.1.Introduction, Motivation, and Objectives --
1.2.What Is a Model? --
1.3.The Modeling Cycle --
1.4.What Is Agent-Based Modeling? How Is It Different? --
1.5.Summary and Conclusions --
1.6.Exercises --
2.Getting Started with NetLogo --
2.1.Introduction and Objectives --
2.2.A Quick Tour of NetLogo --
2.3.A Demonstration Program: Mushroom Hunt --
2.4.Summary and Conclusions --
2.5.Exercises --
3.Describing and Formulating ABMs: The ODD Protocol --
3.1.Introduction and Objectives --
3.2.What Is ODD and Why Use It? --
3.3.The ODD Protocol --
3.4.Our First Example: Virtual Corridors of Butterflies --
3.5.Summary and Conclusions --
3.6.Exercises --
4.Implementing a First Agent-Based Model --
4.1.Introduction and Objectives --
4.2.ODD and NetLogo --
4.3.Butterfly Hilltopping: From ODD to NetLogo --
4.4.Comments and the Full Program --
4.5.Summary and Conclusions --
4.6.Exercises --
5.From Animations to Science --
5.1.Introduction and Objectives --
5.2.Observation of Corridors --
5.3.Analyzing the Model --
5.4.Time-Series Results: Adding Plots and File Output --
5.5.A Real Landscape --
5.6.Summary and Conclusions --
5.7.Exercises --
6.Testing Your Program --
6.1.Introduction and Objectives --
6.2.Common Kinds of Errors --
6.3.Techniques for Debugging and Testing NetLogo Programs --
6.4.Documentation of Tests --
6.5.An Example and Exercise: The Marriage Model --
6.6.Summary and Conclusions --
6.7.Exercises --
pt. II Model Design Concepts --
7.Introduction to Part II --
7.1.Objectives of Part II --
7.2.Overview --
8.Emergence --
8.1.Introduction and Objectives --
8.2.A Model with Less-Emergent Dynamics --
8.3.Simulation Experiments and BehaviorSpace --
8.4.A Model with Complex Emergent Dynamics --
8.5.Summary and Conclusions --
8.6.Exercises --
9.Observation --
9.1.Introduction and Objectives --
9.2.Observing the Model via NetLogo's View --
9.3.Other Interface Displays --
9.4.File Output --
9.5.BehaviorSpace as an Output Writer --
9.6.Export Primitives and Menu Commands --
9.7.Summary and Conclusions --
9.8.Exercises --
10.Sensing --
10.1.Introduction and Objectives --
10.2.Who Knows What: The Scope of Variables --
10.3.Using Variables of Other Objects --
10.4.Putting Sensing to Work: The Business Investor Model --
10.5.Summary and Conclusions --
10.6.Exercises --
11.Adaptive Behavior and Objectives --
11.1.Introduction and Objectives --
11.2.Identifying and Optimizing Alternatives in NetLogo --
11.3.Adaptive Behavior in the Business Investor Model --
11.4.Non-optimizing Adaptive Traits: A Satisficing Example --
11.5.The Objective Function --
11.6.Summary and Conclusions --
11.7.Exercises --
12.Prediction --
12.1.Introduction and Objectives --
12.2.Example Effects of Prediction: The Business Investor Model's Time Horizon --
12.3.Implementing and Analyzing Submodels --
12.4.Analyzing the Investor Utility Function --
12.5.Modeling Prediction Explicitly --
12.6.Summary and Conclusions --
12.7.Exercises --
13.Interaction --
13.1.Introduction and Objectives --
13.2.Programming Interaction in NetLogo --
13.3.The Telemarketer Model --
13.4.The March of Progress: Global Interaction --
13.5.Direct Interaction: Mergers in the Telemarketer Model --
13.6.The Customers Fight Back: Remembering Who Called --
13.7.Summary and Conclusions --
13.8.Exercises --
14.Scheduling --
14.1.Introduction and Objectives --
14.2.Modeling Time in NetLogo --
14.3.Summary and Conclusions --
14.4.Exercises --
15.Stochasticity --
15.1.Introduction and Objectives --
15.2.Stochasticity in ABMs --
15.3.Pseudorandom Number Generation in NetLogo --
15.4.An Example Stochastic Process: Empirical Model of Behavior --
15.5.Summary and Conclusions --
15.6.Exercises --
16.Collectives --
16.1.Introduction and Objectives --
16.2.What Are Collectives? --
16.3.Modeling Collectives in NetLogo --
16.4.Example: A Wild Dog Model with Packs --
16.5.Summary and Conclusions --
16.6.Exercises --
pt. III Pattern-Oriented Modeling --
17.Introduction to Part III --
17.1.Toward Structurally Realistic Models --
17.2.Single and Multiple, Strong and Weak Patterns --
17.3.Overview of Part III --
18.Patterns for Model Structure --
18.1.Introduction --
18.2.Steps in POM to Design Model Structure --
18.3.Example: Modeling European Beech Forests --
18.4.Example: Management Accounting and Collusion --
18.5.Summary and Conclusions --
18.6.Exercises --
19.Theory Development --
19.1.Introduction --
19.2.Theory Development and Strong Inference in the Virtual Laboratory --
19.3.Examples of Theory Development for ABMs --
19.4.Exercise Example: Stay or Leave? --
19.5.Summary and Conclusions --
19.6.Exercises --
20.Parameterization and Calibration --
20.1.Introduction and Objectives --
20.2.Parameterization of ABMs Is Different --
20.3.Parameterizing Submodels --
20.4.Calibration Concepts and Strategies --
20.5.Example: Calibration of the Woodhoopoe Model --
20.6.Summary and Conclusions --
20.7.Exercises --
pt. IV Model Analysis --
21.Introduction to Part IV --
21.1.Objectives of Part IV --
21.2.Overview of Part IV --
22.Analyzing and Understanding ABMs --
22.1.Introduction --
22.2.Example Analysis: The Segregation Model --
22.3.Additional Heuristics for Understanding ABMs --
22.4.Statistics for Understanding --
22.5.Summary and Conclusions --
22.6.Exercises --
23.Sensitivity, Uncertainty, and Robustness Analysis --
23.1.Introduction and Objectives --
23.2.Sensitivity Analysis --
23.3.Uncertainty Analysis --
23.4.Robustness Analysis --
23.5.Summary and Conclusions --
23.6.Exercises --
24.Where to Go from Here --
24.1.Introduction --
24.2.Keeping Your Momentum: Reimplementation --
24.3.Your First Model from Scratch --
24.4.Modeling Agent Behavior --
24.5.ABM Gadgets --
24.6.Coping with NetLogo's Limitations --
24.7.Beyond NetLogo --
24.8.An Odd Farewell.
Responsibility: Steven F. Railsback and Volker Grimm.

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Agent-based modeling is a new technique for understanding how the dynamics of biological, social, and other complex systems arise from the characteristics and behaviors of the agents making up these  Read more...
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