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 easy-to-use, free, and powerful software platform. Nine chapters then each introduce an important modeling concept and show how to implement it using NetLogo. The book goes on to present strategies for finding the right level of model complexity and developing theory for agent behavior, and for analyzing and learning from models. Agent-Based and Individual-Based Modeling features concise and accessible text, numerous examples, and exercises using small but scientific models. The emphasis throughout is on analysis--such as software testing, theory development, robustness analysis, and understanding full models--and on design issues like optimizing model structure and finding good parameter values.
* The first hands-on introduction to agent-based modeling, from conceptual design to computer implementation to parameterization and analysis* Provides an introduction to NetLogo with nine chapters introducing an important modeling concept and showing how to implement it using NetLogo * Filled with examples and exercises, with updates and supplementary materials at http://www.railsback-grimm-abm-book.com/ * Designed for students and researchers across the biological and social sciences * Written by leading practitioners Leading universities that have adopted this book include: * Amherst College * Brigham Young University * Carnegie Mellon University* Cornell University * Miami University * Northwestern University * Old Dominion University * Portland State University * Rhodes College * Susquehanna University * University College, Dublin * University of Arizona* University of British Columbia* University of Michigan * University of South Florida* University of Texas at Austin * University of Virginia
Rezensionen / Stimmen
"Biologists ... have been relatively slow to take advantage of enhanced computing power and unlock the potential of these techniques. This book removes any excuse. Based on a course run by the authors, who both come from an ecological background, and building on an earlier, more conceptual book, this aims to provide the necessary tools to students and researchers."--Frontiers of Biogeography "This volume would be an excellent text for an introductory course in modeling as science, or for self-study by a mature researcher interested in learning about this important new way of doing science."--H. Van Dyke Parunak, JASSS "This book represents something I have been waiting for some years now: a good and solid introduction to the field of individual- and agent-based models (hereafter IBM/ABM's). This book fulfills my needs, using a mix of theory and practical examples which seems to suit the topic well... [T]he book is not only a practical guide but also serves as a good introduction to the basics of 'healthy' programming. These authors are the right ones to do this as they have a strong background in the philosophical aspects as well as the practical issues of modeling."--Basic and Applied Ecology "Railsback and Grimm have done the heavy lifting required to establish a solid IBM course by providing a carefully crafted inquiry-based curriculum. This accomplishment removes a major impediment to the proliferation of IBM courses. Although the book seems aimed at a graduate-level course, I also do not see why an ambitious teacher with motivated students could not use this textbook as the basis of an upper-level undergraduate course in individual based modeling. Agent-based and individual-based modeling has the potential to foster an appreciation of the value and place of individual-based models in our field in the next generation of emerging ecologists (who already have computational leanings)."--Christopher X. Jon Jensen, Ecology
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Für höhere Schule und Studium
Produkt-Hinweis
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Höhe: 254 mm
Breite: 210 mm
Dicke: 25 mm
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ISBN-13
978-0-691-13674-5 (9780691136745)
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Schweitzer Klassifikation
Steven F. Railsback is adjunct professor of mathematics at Humboldt State University and a consulting environmental scientist. Volker Grimm is senior scientist in the Department of Ecological Modeling at the Helmholtz Centre for Environmental Research-UFZ in Leipzig and professor at the University of Potsdam. They are the authors of Individual-Based Modeling and Ecology (Princeton).
Preface xi Acknowledgments xvii Part I: Agent-Based Modeling and NetLogo Basics 1 Chapter 1: Models, Agent-Based Models, and the Modeling Cycle 3 1.1 Introduction, Motivation, and Objectives 3 1.2 What Is a Model? 4 1.3 The Modeling Cycle 7 1.4 What Is Agent-Based Modeling? How Is It Different? 9 1.5 Summary and Conclusions 11 1.6 Exercises 12 Chapter 2: Getting Started with NetLogo 15 2.1 Introduction and Objectives 15 2.2 A Quick Tour of NetLogo 16 2.3 A Demonstration Program: Mushroom Hunt 18 2.4 Summary and Conclusions 29 2.5 Exercises 32 Chapter 3: Describing and Formulating ABMs: The ODD Protocol 35 3.1 Introduction and Objectives 35 3.2 What Is ODD and Why Use It? 36 3.3 T he ODD Protocol 37 3.4 Our First Example: Virtual Corridors of Butterflies 42 3.5 Summary and Conclusions 44 3.6 Exercises 45 Chapter 4: Implementing a First Agent-Based Model 47 4.1 Introduction and Objectives 47 4.2 ODD and NetLogo 47 4.3 Butterfly Hilltopping: From ODD to NetLogo 48 4.4 Comments and the Full Program 55 4.5 Summary and Conclusions 58 4.6 Exercises 59 Chapter 5: From Animations to Science 61 5.1 Introduction and Objectives 61 5.2 Observation of Corridors 62 5.3 Analyzing the Model 67 5.4 Time-Series Results: Adding Plots and File Output 67 5.5 A Real Landscape 69 5.6 Summary and Conclusions 72 5.7 Exercises 72 Chapter 6: Testing Your Program 75 6.1 Introduction and Objectives 75 6.2 Common Kinds of Errors 76 6.3 Techniques for Debugging and Testing NetLogo Programs 79 6.4 Documentation of Tests 89 6.5 An Example and Exercise: The Marriage Model 90 6.6 Summary and Conclusions 92 6.7 Exercises 94 Part II: Model Design Concepts 95 Chapter 7: Introduction to Part II 97 7.1 Objectives of Part II? 97 7.2 Overview 98 Chapter 8: Emergence 101 8.1 Introduction and Objectives 101 8.2 A Model with Less-Emergent Dynamics 102 8.3 Simulation Experiments and BehaviorSpace 103 8.4 A Model with Complex Emergent Dynamics 108 8.5 Summary and Conclusions 113 8.6 Exercises 114 Chapter 9: Observation 115 9.1 Introduction and Objectives 115 9.2 Observing the Model via NetLogo's View 116 9.3 Other Interface Displays 119 9.4 File Output 120 9.5 Behavior Space as an Output Writer 123 9.6 Export Primitives and Menu Commands 124 9.7 Summary and Conclusions 124 9.8 Exercises 125 Chapter 10: Sensing 127 10.1 Introduction and Objectives 127 10.2 Who Knows What: The Scope of Variables 128 10.3 Using Variables of Other Objects 131 10.4 Putting Sensing to Work: The Business Investor Model 132 10.5 Summary and Conclusions 140 10.6 Exercises 141 Chapter 11: Adaptive Behavior and Objectives 143 11.1 Introduction and Objectives 143 11.2 Identifying and Optimizing Alternatives in NetLogo 144 11.3 Adaptive Behavior in the Business Investor Model 148 11.4 Non-optimizing Adaptive Traits: A Satisficing Example 149 11.5 The Objective Function 152 11.6 Summary and Conclusions 153 11.7 Exercises 154 Chapter 12: Prediction 157 12.1 Introduction and Objectives 157 12.2 Example Effects of Prediction: The Business Investor Model's Time Horizon 158 12.3 Implementing and Analyzing Submodels 159 12.4 Analyzing the Investor Utility Function 163 12.5 Modeling Prediction Explicitly 165 12.6 Summary and Conclusions 166 12.7 Exercises 167 Chapter 13: Interaction 169 13.1 Introduction and Objectives 169 13.2 Programming Interaction in NetLogo 170 13.3 The Telemarketer Model 171 13.4 The March of Progress: Global Interaction 175 13.5 Direct Interaction: Mergers in the Telemarketer Model 176 13.6 The Customers Fight Back: Remembering Who Called 179 13.7 Summary and Conclusions 181 13.8 Exercises 181 Chapter 14: Scheduling 183 14.1 Introduction and Objectives 183 14.2 Modeling Time in NetLogo 184 14.3 Summary and Conclusions 192 14.4 Exercises 193 Chapter 15: Stochasticity 195 15.1 Introduction and Objectives 195 15.2 Stochasticity in ABMs 196 15.3 Pseudorandom Number Generation in NetLogo 198 15.4 An Example Stochastic Process: Empirical Model of Behavior 203 15.5 Summary and Conclusions 205 15.6 Exercises 206 Chapter 16: Collectives 209 16.1 Introduction and Objectives 209 16.2 What Are Collectives? 209 16.3 Modeling Collectives in NetLogo 210 16.4 Example: A Wild Dog Model with Packs 212 16.5 Summary and Conclusions 221 16.6 Exercises 222 Part III: Pattern-Oriented Modeling 225 Chapter 17: Introduction to Part III 227 17.1 Toward Structurally Realistic Models 227 17.2 Single and Multiple, Strong and Weak Patterns 228 17.3 Overview of Part III?230 Chapter 18: Patterns for Model Structure 233 18.1 Introduction 233 18.2 Steps in POM to Design Model Structure 234 18.3 Example: Modeling European Beech Forests 235 18.4 Example: Management Accounting and Collusion 239 18.5 Summary and Conclusions 240 18.6 Exercises 241 Chapter 19: Theory Development 243 19.1 Introduction 243 19.2 Theory Development and Strong Inference in the Virtual Laboratory 244 19.3 Examples of Theory Development for ABMs 246 19.4 Exercise Example: Stay or Leave? 249 19.5 Summary and Conclusions 253 19.6 Exercises 254 Chapter 20: Parameterization and Calibration 255 20.1 Introduction and Objectives 255 20.2 Parameterization of ABMs Is Different 256 20.3 Parameterizing Submodels 257 20.4 Calibration Concepts and Strategies 258 20.5 Example: Calibration of the Woodhoopoe Model 264 20.6 Summary and Conclusions 267 20.7 Exercises 268 Part IV: Model Analysis 271 Chapter 21: Introduction to Part IV 273 21.1 Objectives of Part IV?273 21.2 Overview of Part IV?274 Chapter 22: Analyzing and Understanding ABMs 277 22.1 Introduction 277 22.2 Example Analysis: The Segregation Model 278 22.3 Additional Heuristics for Understanding ABMs 283 22.4 Statistics for Understanding 287 22.5 Summary and Conclusions 288 22.6 Exercises 288 Chapter 23: Sensitivity, Uncertainty, and Robustness Analysis 291 23.1 Introduction and Objectives 291 23.2 Sensitivity Analysis 293 23.3 Uncertainty Analysis 297 23.4 Robustness Analysis 302 23.5 Summary and Conclusions 306 23.6 Exercises 307 Chapter 24: Where to Go from Here 309 24.1 Introduction 309 24.2 Keeping Your Momentum: Reimplementation 310 24.3 Your First Model from Scratch 310 24.4 Modeling Agent Behavior 311 24.5 ABM Gadgets 312 24.6 Coping with NetLogo's Limitations 313 24.7 Beyond NetLogo 315 24.8 An Odd Farewell 316 References 317 Index 323 Index of Programming Notes 329
