An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo (The MIT Press)

✍ Scribed by Uri Wilensky, William Rand

• Publisher: The MIT Press
• Year: 2015
• Tongue: English
• Leaves: 505
• Edition: Illustrated
• Category: Library

✦ Synopsis

A comprehensive and hands-on introduction to the core concepts, methods, and applications of agent-based modeling, including detailed NetLogo examples.

The advent of widespread fast computing has enabled us to work on more complex problems and to build and analyze more complex models. This book provides an introduction to one of the primary methodologies for research in this new field of knowledge. Agent-based modeling (ABM) offers a new way of doing science: by conducting computer-based experiments. ABM is applicable to complex systems embedded in natural, social, and engineered contexts, across domains that range from engineering to ecology. An Introduction to Agent-Based Modeling offers a comprehensive description of the core concepts, methods, and applications of ABM. Its hands-on approach—with hundreds of examples and exercises using NetLogo—enables readers to begin constructing models immediately, regardless of experience or discipline.

The book first describes the nature and rationale of agent-based modeling, then presents the methodology for designing and building ABMs, and finally discusses how to utilize ABMs to answer complex questions. Features in each chapter include step-by-step guides to developing models in the main text; text boxes with additional information and concepts; end-of-chapter explorations; and references and lists of relevant reading. There is also an accompanying website with all the models and code.

✦ Table of Contents

front-cover
front-page
Copyright Page
Contents
Preface
Who We Wrote This For
NetLogo and the Textbook
Learning Objectives
Features
Organization
Chapters 0 and 1 and the Appendix: What are ABMS
Chapters 2,3,4 and 5: How to build ABMS
Chapters 6,7 and 8: How to analyze and build ABMs
Acknowledgements
0 Why Agent-Based Modeling?
A Thought Experiment
Complex Systems and Emergence
Understanding Complex Systems and Emergence
Example 1: Integrative Understanding
Example 2: Differential Understanding
Agent-Based Modeling as Representational Infrastructure for Restructurations
Example: Predator-Prey Interactions
Example: Forest Fires
1 What Is Agent-Based Modeling?
Ants
Creating the Ant Foraging Model
Results and Observations from the Ant Model
What Good Is an Ant Model?
What Is Agent-Based Modeling?
Agent-Based Models vs. Other Modeling Forms
Randomness vs. Determinism
When Is ABM Most Beneficial?
Trade-offs of ABM
What Is Needed to Understand ABM?
Conclusion
Explorations
Beginner NetLogo Explorations
Ants and Other Model Explorations
Concept Explorations
NetLogo Explorations
2 Creating Simple Agent-Based Models
Life
Heroes and Cowards
Simple Economy
Summary
Explorations
Chapter Model Explorations
NetLogo Explorations
3 Exploring and Extending Agent-Based Models
The Fire Model
Description of the Fire Model
First Extension: Probabilistic Transitions
Second Extension: Adding Wind
Third Extension: Allow Long-Distance Transmission
Summary of the Fire Model
Advanced Modeling Applications
The Diffusion-Limited Aggregation (DLA) Model
Description of Diffusion-Limited Aggregation
First Extension: Probabilistic Sticking
Second Extension: Neighbor Influence
Third Extension: Different Aggregates
Summary of the DLA Model
Advanced Modeling Applications
The Segregation Model
Description of the Segregation Model
First Extension: Adding Multiple Ethnicities
Second Extension: Allowing Diverse Thresholds
Third Extension: Adding Diversity-Seeking Individuals
Summary of the Segregation Model
Advanced Urban Modeling Applications
The El Farol Model
Description of the El Farol Model
First Extension: Color Agents That Are More Successful Predictors
Second Extension: Average, Min, and Max Rewards
Third Extension: Histogram Reward Values
Summary of the El Farol Model
Advanced Modeling Applications
Conclusion
Explorations
4 Creating Agent-Based Models
Designing Your Model
Choosing Your Questions
A Concrete Example
Choosing Your Agents
Choosing Agent Properties
Choosing Agent Behavior
Choosing Parameters of the Model
Summary of the Wolf Sheep Simple Model Design
Examining a Model
Multiple Runs
Predator-Prey Models: Additional Context
Advanced Modeling Applications
Conclusion
Explorations
5 The Components of Agent-Based Modeling
Overview
Agents
Properties
Behaviors (Actions)
Collections of Agents
The Granularity of an Agent
Agent Cognition
Other Kinds of Agents
Environments
Spatial Environments
Network-Based Environments
Special Environments
Interactions
Observer/User Interface
Schedule
Wrapping It All Up
Summary
Explorations
6 Analyzing Agent-Based Models
Types of Measurements
Modeling the Spread of Disease
Statistical Analysis of ABM: Moving beyond Raw Data
The Necessity of Multiple Runs within ABM
Using Graphs to Examine Results in ABM
Analyzing Networks within ABM
Environmental Data and ABM
Summarizing Analysis of ABMs
Explorations
7 Verification, Validation, and Replication
Correctness of a Model
Verification
Communication
Describing Conceptual Models
Verification Testing
Beyond Verification
Sensitivity Analysis and Robustness
Verification Benefits and Issues
Validation
Macrovalidation vs. Microvalidation
Face Validation vs. Empirical Validation
Validation Benefits and Questions
Replication
Replication of Computational Models: Dimensions and Standards
Benefits of Replication
Recommendations for Model Replicators
Recommendations for Model Authors
Summary
Explorations
8 Advanced Topics and Applications
Advanced Topics in ABM
Model Design Guidelines
Rule Extraction
Using ABM for Communication, Persuasion, and Education
Human, Embedded, and Virtual Agents through Mediation
Hybrid Computational Methods
Some Advanced Computational Methods in NetLogo
Extensions to ABM
Integration of Advanced Data Sources and Output
Speed
Applications of ABM
Revisiting the Trade-offs of ABM
The Future of ABM
Explorations
Appendix: The Computational Roots of Agent-Based Modeling
The Vignettes
Cellular Automata and Agent-Based Modeling
Genetic Algorithms, John Holland, and Complex Adaptive Systems
Seymour Papert, Logo, and the Turtle
Object-Oriented Programming and the Actor Model
Data Parallelism
Computer Graphics, Particle Systems, and Boids
Conclusion
References
Software and Models
Index