Agent-Based and Individual-Based Modeling

Agent-Based and Individual-Based Modeling : A Practical Introduction

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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 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 analysisProvides an introduction to NetLogo with nine chapters introducing an important modeling concept and showing how to implement it using NetLogoFilled with examples and exercises, with updates and supplementary materials at for students and researchers across the biological and social sciencesWritten by leading practitionersLeading universities that have adopted this book include: Amherst College Brigham Young University Carnegie Mellon UniversityCornell University Miami University Northwestern University Old Dominion University Portland State University Rhodes College Susquehanna University University College, Dublin University of ArizonaUniversity of British ColumbiaUniversity of Michigan University of South FloridaUniversity of Texas at Austin University of Virginiashow more

Product details

  • Paperback | 352 pages
  • 203.2 x 254 x 22.86mm | 771.1g
  • Princeton University Press
  • New Jersey, United States
  • English
  • 67 line illus. 6 tables.
  • 0691136742
  • 9780691136745
  • 211,013

Review quote

"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 "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 "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 "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 Biogeographyshow more

About Steven F. Railsback

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).show more

Back cover copy

"Knitting together ecology, economics, and social systems, this wonderful book will encourage and enlighten novices and experienced modelers alike. It highlights the importance of patterns at every level of the modeling process, the need for clear explication of assumptions, and the benefits of models composed of discrete entities (agents) which interact, evolve, and mimic reality."--Louis Gross, University of Tennessee, Knoxville "Railsback and Grimm provide a needed book on how to develop, code, and analyze agent-based models. They so expertly explain the art and science of modeling that even the most modeling-shy beginner will master the skills. Readers will also gain a deep understanding of the increasing importance of agent-based models for interpreting the patterns of nature and human society."--Donald L. DeAngelis, U.S. Geological Survey "Railsback and Grimm have written a superb introduction to agent-based models. They combine hands-on programming exercises, introductions to some of the core concepts in complex systems, and instruction in model design and analysis. The result is an excellent book that's ideal for both undergraduates and academics."--Scott E. Page, author of Diversity and Complexity "This exceptional book offers a systematic introduction to the scientific use of agent-based modeling, including the implementation, testing, and validation of models. Until now there was no good textbook available to teach students the theory and practice of agent-based modeling. Railsback and Grimm provide such a text, one that will likely become a classic in the field."--Marco A. Janssen, Arizona State University "This book is an invaluable guide to agent-based modeling. A significant contribution to the field, it will train the next generation of modelers and teach best practices to existing modelers. Railsback and Grimm have in-depth expertise and experience in developing and teaching agent-based modeling, and are well qualified to write such a book."--Richard Stillman, Bournemouth Universityshow more

Table of contents

Preface xiAcknowledgments xviiPart I: Agent-Based Modeling and NetLogo Basics 1Chapter 1: Models, Agent-Based Models, and the Modeling Cycle 31.1 Introduction, Motivation, and Objectives 31.2 What Is a Model? 41.3 The Modeling Cycle 71.4 What Is Agent-Based Modeling? How Is It Different? 91.5 Summary and Conclusions 111.6 Exercises 12Chapter 2: Getting Started with NetLogo 152.1 Introduction and Objectives 152.2 A Quick Tour of NetLogo 162.3 A Demonstration Program: Mushroom Hunt 182.4 Summary and Conclusions 292.5 Exercises 32Chapter 3: Describing and Formulating ABMs: The ODD Protocol 353.1 Introduction and Objectives 353.2 What Is ODD and Why Use It? 363.3 T he ODD Protocol 373.4 Our First Example: Virtual Corridors of Butterflies 423.5 Summary and Conclusions 443.6 Exercises 45Chapter 4: Implementing a First Agent-Based Model 474.1 Introduction and Objectives 474.2 ODD and NetLogo 474.3 Butterfly Hilltopping: From ODD to NetLogo 484.4 Comments and the Full Program 554.5 Summary and Conclusions 584.6 Exercises 59Chapter 5: From Animations to Science 615.1 Introduction and Objectives 615.2 Observation of Corridors 625.3 Analyzing the Model 675.4 Time-Series Results: Adding Plots and File Output 675.5 A Real Landscape 695.6 Summary and Conclusions 725.7 Exercises 72Chapter 6: Testing Your Program 756.1 Introduction and Objectives 756.2 Common Kinds of Errors 766.3 Techniques for Debugging and Testing NetLogo Programs 796.4 Documentation of Tests 896.5 An Example and Exercise: The Marriage Model 906.6 Summary and Conclusions 926.7 Exercises 94Part II: Model Design Concepts 95Chapter 7: Introduction to Part II 977.1 Objectives of Part II? 977.2 Overview 98Chapter 8: Emergence 1018.1 Introduction and Objectives 1018.2 A Model with Less-Emergent Dynamics 1028.3 Simulation Experiments and BehaviorSpace 1038.4 A Model with Complex Emergent Dynamics 1088.5 Summary and Conclusions 1138.6 Exercises 114Chapter 9: Observation 1159.1 Introduction and Objectives 1159.2 Observing the Model via NetLogo's View 1169.3 Other Interface Displays 1199.4 File Output 1209.5 Behavior Space as an Output Writer 1239.6 Export Primitives and Menu Commands 1249.7 Summary and Conclusions 1249.8 Exercises 125Chapter 10: Sensing 12710.1 Introduction and Objectives 12710.2 Who Knows What: The Scope of Variables 12810.3 Using Variables of Other Objects 13110.4 Putting Sensing to Work: The Business Investor Model 13210.5 Summary and Conclusions 14010.6 Exercises 141Chapter 11: Adaptive Behavior and Objectives 14311.1 Introduction and Objectives 14311.2 Identifying and Optimizing Alternatives in NetLogo 14411.3 Adaptive Behavior in the Business Investor Model 14811.4 Non-optimizing Adaptive Traits: A Satisficing Example 14911.5 The Objective Function 15211.6 Summary and Conclusions 15311.7 Exercises 154Chapter 12: Prediction 15712.1 Introduction and Objectives 15712.2 Example Effects of Prediction: The Business Investor Model's Time Horizon 15812.3 Implementing and Analyzing Submodels 15912.4 Analyzing the Investor Utility Function 16312.5 Modeling Prediction Explicitly 16512.6 Summary and Conclusions 16612.7 Exercises 167Chapter 13: Interaction 16913.1 Introduction and Objectives 16913.2 Programming Interaction in NetLogo 17013.3 The Telemarketer Model 17113.4 The March of Progress: Global Interaction 17513.5 Direct Interaction: Mergers in the Telemarketer Model 17613.6 The Customers Fight Back: Remembering Who Called 17913.7 Summary and Conclusions 18113.8 Exercises 181Chapter 14: Scheduling 18314.1 Introduction and Objectives 18314.2 Modeling Time in NetLogo 18414.3 Summary and Conclusions 19214.4 Exercises 193Chapter 15: Stochasticity 19515.1 Introduction and Objectives 19515.2 Stochasticity in ABMs 19615.3 Pseudorandom Number Generation in NetLogo 19815.4 An Example Stochastic Process: Empirical Model of Behavior 20315.5 Summary and Conclusions 20515.6 Exercises 206Chapter 16: Collectives 20916.1 Introduction and Objectives 20916.2 What Are Collectives? 20916.3 Modeling Collectives in NetLogo 21016.4 Example: A Wild Dog Model with Packs 21216.5 Summary and Conclusions 22116.6 Exercises 222Part III: Pattern-Oriented Modeling 225Chapter 17: Introduction to Part III 22717.1 Toward Structurally Realistic Models 22717.2 Single and Multiple, Strong and Weak Patterns 22817.3 Overview of Part III?230Chapter 18: Patterns for Model Structure 23318.1 Introduction 23318.2 Steps in POM to Design Model Structure 23418.3 Example: Modeling European Beech Forests 23518.4 Example: Management Accounting and Collusion 23918.5 Summary and Conclusions 24018.6 Exercises 241Chapter 19: Theory Development 24319.1 Introduction 24319.2 Theory Development and Strong Inference in the Virtual Laboratory 24419.3 Examples of Theory Development for ABMs 24619.4 Exercise Example: Stay or Leave? 24919.5 Summary and Conclusions 25319.6 Exercises 254Chapter 20: Parameterization and Calibration 25520.1 Introduction and Objectives 25520.2 Parameterization of ABMs Is Different 25620.3 Parameterizing Submodels 25720.4 Calibration Concepts and Strategies 25820.5 Example: Calibration of the Woodhoopoe Model 26420.6 Summary and Conclusions 26720.7 Exercises 268Part IV: Model Analysis 271Chapter 21: Introduction to Part IV 27321.1 Objectives of Part IV?27321.2 Overview of Part IV?274Chapter 22: Analyzing and Understanding ABMs 27722.1 Introduction 27722.2 Example Analysis: The Segregation Model 27822.3 Additional Heuristics for Understanding ABMs 28322.4 Statistics for Understanding 28722.5 Summary and Conclusions 28822.6 Exercises 288Chapter 23: Sensitivity, Uncertainty, and Robustness Analysis 29123.1 Introduction and Objectives 29123.2 Sensitivity Analysis 29323.3 Uncertainty Analysis 29723.4 Robustness Analysis 30223.5 Summary and Conclusions 30623.6 Exercises 307Chapter 24: Where to Go from Here 30924.1 Introduction 30924.2 Keeping Your Momentum: Reimplementation 31024.3 Your First Model from Scratch 31024.4 Modeling Agent Behavior 31124.5 ABM Gadgets 31224.6 Coping with NetLogo's Limitations 31324.7 Beyond NetLogo 31524.8 An Odd Farewell 316References 317Index 323Index of Programming Notes 329show more

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