Agent-Based and Individual-Based Modeling: A Practical Introduction, Second Edition

Steven F. Railsback and Volker Grimm

The essential textbook on agent-based modeling—now fully updated and expanded

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Published:: Mar 26, 2019
Copyright:: 2019
Main_subject:: Biology

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Main_subject:: Biology

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ISBN:
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Copyright:: 2019
Main_subject:: Biology

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Agent-Based and Individual-Based Modeling has become the standard textbook on the subject for classroom use and self-instruction. Drawing on the latest version of NetLogo and fully updated with new examples, exercises, and an enhanced text for easier comprehension, this is the essential resource for anyone seeking to understand how the dynamics of biological, social, and other complex systems arise from the characteristics of the agents that make up these systems.

Steven Railsback and Volker Grimm lead students stepwise through the processes of designing, programming, documenting, and doing scientific research with agent-based models, focusing on the adaptive behaviors that make these models necessary. They cover the fundamentals of modeling and model analysis, introduce key modeling concepts, and demonstrate how to implement them using NetLogo. They also address pattern-oriented modeling, an invaluable strategy for modeling real-world problems and developing theory.

This accessible and authoritative book focuses on modeling as a tool for understanding real complex systems. It explains how to pose a specific question, use observations from actual systems to design models, write and test software, and more.

A hands-on introduction that guides students from conceptual design to computer implementation to analysis
Filled with new examples and exercises and compatible with the latest version of NetLogo
Ideal for students and researchers across the natural and social sciences
Written by two leading practitioners
Supported by extensive instructional materials at www.railsback-grimm-abm-book.com

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.

Preface
Acknowledgments
Part 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 What Does the Modeling Cycle Involve?
  - 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 Culture Dissemination Model
  - 6.6 Summary and Conclusions
  - 6.7 Exercises
Part II Model Design Concepts
- 7 Introduction to Part II
  - 7.1 Objectives of Part II
  - 7.2 Overview of Part II
- 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 Nonoptimizing Adaptive Behavior: 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
Part 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 and Objectives
  - 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 and Objectives
  - 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
Part 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 and Objectives
  - 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 and Objectives
  - 24.2 Keeping Your Momentum: Reimplementation
  - 24.3 Your First Model from Scratch
  - 24.4 Modeling Agent Behavior
  - 24.5 ABM Gadgets
  - 24.6 NetLogo as a Platform for Large Models
  - 24.7 An Odd Farewell
References
Index
- Index of Programming Notes

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