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 of theoretical ecology at the University of Potsdam.

Praise for the first edition "Biologists . . . have been relatively slow to take advantage of enhanced computing power and unlock the potential of these techniques. This book removes any excuse."—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 [awaiting] for some years now: a good and solid introduction to the field of individual- and agent-based models. . . . The 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 modelling."—Basic and Applied Ecology"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."—Christopher X. Jon Jensen, Ecology

• PrefaceAcknowledgmentsPart I Agent-Based Modeling and NetLogo Basics1 Models, Agent-Based Models, and the Modeling Cycle1.1 Introduction, Motivation, and Objectives1.2 What Is a Model?1.3 What Does the Modeling Cycle Involve?1.4 What Is Agent-Based Modeling? How Is It Different?1.5 Summary and Conclusions1.6 Exercises2 Getting Started with NetLogo2.1 Introduction and Objectives2.2 A Quick Tour of NetLogo2.3 A Demonstration Program: Mushroom Hunt2.4 Summary and Conclusions2.5 Exercises3 Describing and Formulating ABMs: The ODD Protocol3.1 Introduction and Objectives3.2 What Is ODD and Why Use It?3.3 The ODD Protocol3.4 Our First Example: Virtual Corridors of Butterflies3.5 Summary and Conclusions3.6 Exercises4 Implementing a First Agent-Based Model4.1 Introduction and Objectives4.2 ODD and NetLogo4.3 Butterfly Hilltopping: From ODD to NetLogo4.4 Comments and the Full Program4.5 Summary and Conclusions4.6 Exercises5 From Animations to Science5.1 Introduction and Objectives5.2 Observation of Corridors5.3 Analyzing the Model5.4 Time-Series Results: Adding Plots and File Output5.5 A Real Landscape5.6 Summary and Conclusions5.7 Exercises6 Testing Your Program6.1 Introduction and Objectives6.2 Common Kinds of Errors6.3 Techniques for Debugging and Testing NetLogo Programs6.4 Documentation of Tests6.5 An Example and Exercise: The Culture Dissemination Model6.6 Summary and Conclusions6.7 ExercisesPart II Model Design Concepts7 Introduction to Part II7.1 Objectives of Part II7.2 Overview of Part II8 Emergence8.1 Introduction and Objectives8.2 A Model with Less Emergent Dynamics8.3 Simulation Experiments and BehaviorSpace8.4 A Model with Complex Emergent Dynamics8.5 Summary and Conclusions8.6 Exercises9 Observation9.1 Introduction and Objectives9.2 Observing the Model via NetLogo’s View9.3 Other Interface Displays9.4 File Output9.5 BehaviorSpace as an Output Writer9.6 Export Primitives and Menu Commands9.7 Summary and Conclusions9.8 Exercises10 Sensing10.1 Introduction and Objectives10.2 Who Knows What: The Scope of Variables10.3 Using Variables of Other Objects10.4 Putting Sensing to Work: The Business Investor Model10.5 Summary and Conclusions10.6 Exercises11 Adaptive Behavior and Objectives11.1 Introduction and Objectives11.2 Identifying and Optimizing Alternatives in NetLogo11.3 Adaptive Behavior in the Business Investor Model11.4 Nonoptimizing Adaptive Behavior: A Satisficing Example11.5 The Objective Function11.6 Summary and Conclusions11.7 Exercises12 Prediction12.1 Introduction and Objectives12.2 Example Effects of Prediction: The Business Investor Model’s Time Horizon12.3 Implementing and Analyzing Submodels12.4 Analyzing the Investor Utility Function12.5 Modeling Prediction Explicitly12.6 Summary and Conclusions12.7 Exercises13 Interaction13.1 Introduction and Objectives13.2 Programming Interaction in NetLogo13.3 The Telemarketer Model13.4 The March of Progress: Global Interaction13.5 Direct Interaction: Mergers in the Telemarketer Model13.6 The Customers Fight Back: Remembering Who Called13.7 Summary and Conclusions13.8 Exercises14 Scheduling14.1 Introduction and Objectives14.2 Modeling Time in NetLogo14.3 Summary and Conclusions14.4 Exercises15 Stochasticity15.1 Introduction and Objectives15.2 Stochasticity in ABMs15.3 Pseudorandom Number Generation in NetLogo15.4 An Example Stochastic Process: Empirical Model of Behavior15.5 Summary and Conclusions15.6 Exercises16 Collectives16.1 Introduction and Objectives16.2 What Are Collectives?16.3 Modeling Collectives in NetLogo16.4 Example: A Wild Dog Model with Packs16.5 Summary and Conclusions16.6 ExercisesPart III Pattern-Oriented Modeling17 Introduction to Part III17.1 Toward Structurally Realistic Models17.2 Single and Multiple, Strong and Weak Patterns17.3 Overview of Part III18 Patterns for Model Structure18.1 Introduction and Objectives18.2 Steps in POM to Design Model Structure18.3 Example: Modeling European Beech Forests18.4 Example: Management Accounting and Collusion18.5 Summary and Conclusions18.6 Exercises19 Theory Development19.1 Introduction and Objectives19.2 Theory Development and Strong Inference in the Virtual Laboratory19.3 Examples of Theory Development for ABMs19.4 Exercise Example: Stay or Leave?19.5 Summary and Conclusions19.6 Exercises20 Parameterization and Calibration20.1 Introduction and Objectives20.2 Parameterization of ABMs Is Different20.3 Parameterizing Submodels20.4 Calibration Concepts and Strategies20.5 Example: Calibration of the Woodhoopoe Model20.6 Summary and Conclusions20.7 ExercisesPart IV Model Analysis21 Introduction to Part IV21.1 Objectives of Part IV21.2 Overview of Part IV22 Analyzing and Understanding ABMs22.1 Introduction and Objectives22.2 Example Analysis: The Segregation Model22.3 Additional Heuristics for Understanding ABMs22.4 Statistics for Understanding22.5 Summary and Conclusions22.6 Exercises23 Sensitivity, Uncertainty, and Robustness Analysis23.1 Introduction and Objectives23.2 Sensitivity Analysis23.3 Uncertainty Analysis23.4 Robustness Analysis23.5 Summary and Conclusions23.6 Exercises24 Where to Go from Here24.1 Introduction and Objectives24.2 Keeping Your Momentum: Reimplementation24.3 Your First Model from Scratch24.4 Modeling Agent Behavior24.5 ABM Gadgets24.6 NetLogo as a Platform for Large Models24.7 An Odd FarewellReferencesIndexIndex of Programming Notes

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 of theoretical ecology at the University of Potsdam.

Praise for the first edition "Biologists . . . have been relatively slow to take advantage of enhanced computing power and unlock the potential of these techniques. This book removes any excuse."—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 [awaiting] for some years now: a good and solid introduction to the field of individual- and agent-based models. . . . The 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 modelling."—Basic and Applied Ecology"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."—Christopher X. Jon Jensen, Ecology

• PrefaceAcknowledgmentsPart I Agent-Based Modeling and NetLogo Basics1 Models, Agent-Based Models, and the Modeling Cycle1.1 Introduction, Motivation, and Objectives1.2 What Is a Model?1.3 What Does the Modeling Cycle Involve?1.4 What Is Agent-Based Modeling? How Is It Different?1.5 Summary and Conclusions1.6 Exercises2 Getting Started with NetLogo2.1 Introduction and Objectives2.2 A Quick Tour of NetLogo2.3 A Demonstration Program: Mushroom Hunt2.4 Summary and Conclusions2.5 Exercises3 Describing and Formulating ABMs: The ODD Protocol3.1 Introduction and Objectives3.2 What Is ODD and Why Use It?3.3 The ODD Protocol3.4 Our First Example: Virtual Corridors of Butterflies3.5 Summary and Conclusions3.6 Exercises4 Implementing a First Agent-Based Model4.1 Introduction and Objectives4.2 ODD and NetLogo4.3 Butterfly Hilltopping: From ODD to NetLogo4.4 Comments and the Full Program4.5 Summary and Conclusions4.6 Exercises5 From Animations to Science5.1 Introduction and Objectives5.2 Observation of Corridors5.3 Analyzing the Model5.4 Time-Series Results: Adding Plots and File Output5.5 A Real Landscape5.6 Summary and Conclusions5.7 Exercises6 Testing Your Program6.1 Introduction and Objectives6.2 Common Kinds of Errors6.3 Techniques for Debugging and Testing NetLogo Programs6.4 Documentation of Tests6.5 An Example and Exercise: The Culture Dissemination Model6.6 Summary and Conclusions6.7 ExercisesPart II Model Design Concepts7 Introduction to Part II7.1 Objectives of Part II7.2 Overview of Part II8 Emergence8.1 Introduction and Objectives8.2 A Model with Less Emergent Dynamics8.3 Simulation Experiments and BehaviorSpace8.4 A Model with Complex Emergent Dynamics8.5 Summary and Conclusions8.6 Exercises9 Observation9.1 Introduction and Objectives9.2 Observing the Model via NetLogo’s View9.3 Other Interface Displays9.4 File Output9.5 BehaviorSpace as an Output Writer9.6 Export Primitives and Menu Commands9.7 Summary and Conclusions9.8 Exercises10 Sensing10.1 Introduction and Objectives10.2 Who Knows What: The Scope of Variables10.3 Using Variables of Other Objects10.4 Putting Sensing to Work: The Business Investor Model10.5 Summary and Conclusions10.6 Exercises11 Adaptive Behavior and Objectives11.1 Introduction and Objectives11.2 Identifying and Optimizing Alternatives in NetLogo11.3 Adaptive Behavior in the Business Investor Model11.4 Nonoptimizing Adaptive Behavior: A Satisficing Example11.5 The Objective Function11.6 Summary and Conclusions11.7 Exercises12 Prediction12.1 Introduction and Objectives12.2 Example Effects of Prediction: The Business Investor Model’s Time Horizon12.3 Implementing and Analyzing Submodels12.4 Analyzing the Investor Utility Function12.5 Modeling Prediction Explicitly12.6 Summary and Conclusions12.7 Exercises13 Interaction13.1 Introduction and Objectives13.2 Programming Interaction in NetLogo13.3 The Telemarketer Model13.4 The March of Progress: Global Interaction13.5 Direct Interaction: Mergers in the Telemarketer Model13.6 The Customers Fight Back: Remembering Who Called13.7 Summary and Conclusions13.8 Exercises14 Scheduling14.1 Introduction and Objectives14.2 Modeling Time in NetLogo14.3 Summary and Conclusions14.4 Exercises15 Stochasticity15.1 Introduction and Objectives15.2 Stochasticity in ABMs15.3 Pseudorandom Number Generation in NetLogo15.4 An Example Stochastic Process: Empirical Model of Behavior15.5 Summary and Conclusions15.6 Exercises16 Collectives16.1 Introduction and Objectives16.2 What Are Collectives?16.3 Modeling Collectives in NetLogo16.4 Example: A Wild Dog Model with Packs16.5 Summary and Conclusions16.6 ExercisesPart III Pattern-Oriented Modeling17 Introduction to Part III17.1 Toward Structurally Realistic Models17.2 Single and Multiple, Strong and Weak Patterns17.3 Overview of Part III18 Patterns for Model Structure18.1 Introduction and Objectives18.2 Steps in POM to Design Model Structure18.3 Example: Modeling European Beech Forests18.4 Example: Management Accounting and Collusion18.5 Summary and Conclusions18.6 Exercises19 Theory Development19.1 Introduction and Objectives19.2 Theory Development and Strong Inference in the Virtual Laboratory19.3 Examples of Theory Development for ABMs19.4 Exercise Example: Stay or Leave?19.5 Summary and Conclusions19.6 Exercises20 Parameterization and Calibration20.1 Introduction and Objectives20.2 Parameterization of ABMs Is Different20.3 Parameterizing Submodels20.4 Calibration Concepts and Strategies20.5 Example: Calibration of the Woodhoopoe Model20.6 Summary and Conclusions20.7 ExercisesPart IV Model Analysis21 Introduction to Part IV21.1 Objectives of Part IV21.2 Overview of Part IV22 Analyzing and Understanding ABMs22.1 Introduction and Objectives22.2 Example Analysis: The Segregation Model22.3 Additional Heuristics for Understanding ABMs22.4 Statistics for Understanding22.5 Summary and Conclusions22.6 Exercises23 Sensitivity, Uncertainty, and Robustness Analysis23.1 Introduction and Objectives23.2 Sensitivity Analysis23.3 Uncertainty Analysis23.4 Robustness Analysis23.5 Summary and Conclusions23.6 Exercises24 Where to Go from Here24.1 Introduction and Objectives24.2 Keeping Your Momentum: Reimplementation24.3 Your First Model from Scratch24.4 Modeling Agent Behavior24.5 ABM Gadgets24.6 NetLogo as a Platform for Large Models24.7 An Odd FarewellReferencesIndexIndex of Programming Notes