An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo

505 Pages·2015·19.61 MB·English

by  Uri Wilensky

This content downloaded from 165.190.89.176 on Mon, 18 Jan 2016 23:51:59 UTC
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An Introduction to Agent-Based Modeling
This content downloaded from 165.190.89.176 on Mon, 18 Jan 2016 23:51:59 UTC
All use subject to JSTOR Terms and Conditions
This content downloaded from 165.190.89.176 on Mon, 18 Jan 2016 23:51:59 UTC
All use subject to JSTOR Terms and Conditions
An Introduction to Agent-Based Modeling
Modeling Natural, Social, and Engineered Complex Systems with NetLogo
Uri Wilensky and William Rand
The MIT Press
Cambridge, Massachusetts
London, England
This content downloaded from 165.190.89.176 on Mon, 18 Jan 2016 23:51:59 UTC
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© 2015 Massachusetts Institute of Technology

All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means
(including photocopying, recording, or information storage and retrieval) without permission in writing from the
publisher.

M IT Press books may be purchased at special quantity discounts for business or sales promotional use. For
information, please email [email protected].

T his book was set in Times LT Std 10/13pt by Toppan Best-set Premedia Limited, Hong Kong. Printed and
bound in the United States of America.

Library of Congress Cataloging-in-Publication Data
Wilensky, Uri, 1955 –
An introduction to agent-based modeling : modeling natural, social, and engineered complex systems with
NetLogo / Uri Wilensky and William Rand.
pages cm
Includes bibliographical references and index.
ISBN 978-0-262-73189-8 (pbk. : alk. paper) 1. System analysis — Data processing. 2. Computer simulation.
3. Multiagent systems. 4. NetLogo (Computer program language) I. Rand, William, 1976 – II. Title.
T57.62.W54 2015
003’.3 — dc23
2014023747

10 9 8 7 6 5 4 3 2 1
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Contents
Preface xi
0 Why Agent-Based Modeling? 1
A Thought Experiment 3
Complex Systems and Emergence 5
Understanding Complex Systems and Emergence 7
Example 1: Integrative Understanding 7
Example 2: Differential Understanding 8
A gent-Based Modeling as Representational Infrastructure for Restructurations 13
Example: Predator-Prey Interactions 15
Example: Forest Fires 18
1 What Is Agent-Based Modeling? 21
Ants 21
Creating the Ant Foraging Model 22
Results and Observations from the Ant Model 27
What Good Is an Ant Model? 28
What Is Agent-Based Modeling? 32
Agent-Based Models vs. Other Modeling Forms 32
Randomness vs. Determinism 34
When Is ABM Most Beneficial? 35
Trade-offs of ABM 36
What Is Needed to Understand ABM? 38
Conclusion 39
Explorations 40
Beginner NetLogo Explorations 40
Ants and Other Model Explorations 41
Concept Explorations 41
NetLogo Explorations 42
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vi Contents
2 Creating Simple Agent-Based Models 45
Life 45
Heroes and Cowards 68
Simple Economy 87
Summary 96
Explorations 97
Chapter Model Explorations 97
NetLogo Explorations 99
3 Exploring and Extending Agent-Based Models 101
The Fire Model 103
Description of the Fire Model 104
First Extension: Probabilistic Transitions 110
Second Extension: Adding Wind 112
Third Extension: Allow Long-Distance Transmission 115
Summary of the Fire Model 116
Advanced Modeling Applications 117
The Diffusion-Limited Aggregation (DLA) Model 118
Description of Diffusion-Limited Aggregation 119
First Extension: Probabilistic Sticking 121
Second Extension: Neighbor Influence 122
Third Extension: Different Aggregates 125
Summary of the DLA Model 127
Advanced Modeling Applications 127
The Segregation Model 128
Description of the Segregation Model 131
First Extension: Adding Multiple Ethnicities 134
Second Extension: Allowing Diverse Thresholds 136
Third Extension: Adding Diversity-Seeking Individuals 137
Summary of the Segregation Model 140
Advanced Urban Modeling Applications 140
The El Farol Model 141
Description of the El Farol Model 141
First Extension: Color Agents That Are More Successful Predictors 143
Second Extension: Average, Min, and Max Rewards 145
Third Extension: Histogram Reward Values 146
Summary of the El Farol Model 149
Advanced Modeling Applications 150
Conclusion 152
Explorations 152
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Contents vii
4 Creating Agent-Based Models 157
Designing Your Model 158
Choosing Your Questions 161
A Concrete Example 163
Choosing Your Agents 164
Choosing Agent Properties 165
Choosing Agent Behavior 166
Choosing Parameters of the Model 168
Summary of the Wolf Sheep Simple Model Design 169
Examining a Model 189
Multiple Runs 191
Predator-Prey Models: Additional Context 193
Advanced Modeling Applications 195
Conclusion 196
Explorations 197
5 The Components of Agent-Based Modeling 203
Overview 203
Agents 205
Properties 205
Behaviors (Actions) 209
Collections of Agents 211
The Granularity of an Agent 222
Agent Cognition 224
Other Kinds of Agents 232
Environments 234
Spatial Environments 235
Network-Based Environments 241
Special Environments 247
Interactions 257
Observer/User Interface 262
Schedule 268
Wrapping It All Up 271
Summary 275
Explorations 276
6 Analyzing Agent-Based Models 283
Types of Measurements 283
Modeling the Spread of Disease 283
Statistical Analysis of ABM: Moving beyond Raw Data 287
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viii Contents
The Necessity of Multiple Runs within ABM 288
Using Graphs to Examine Results in ABM 291
Analyzing Networks within ABM 296
Environmental Data and ABM 301
Summarizing Analysis of ABMs 305
Explorations 307
7 Verification, Validation, and Replication 311
Correctness of a Model 311
Verification 312
Communication 313
Describing Conceptual Models 314
Verification Testing 315
Beyond Verification 317
Sensitivity Analysis and Robustness 321
Verification Benefits and Issues 324
Validation 325
Macrovalidation vs. Microvalidation 329
Face Validation vs. Empirical Validation 331
Validation Benefits and Questions 335
Replication 336
Replication of Computational Models: Dimensions and Standards 337
Benefits of Replication 340
Recommendations for Model Replicators 341
Recommendations for Model Authors 344
Summary 346
Explorations 347
8 Advanced Topics and Applications 351
Advanced Topics in ABM 351
Model Design Guidelines 353
Rule Extraction 356
Using ABM for Communication, Persuasion, and Education 369
Human, Embedded, and Virtual Agents through Mediation 372
Hybrid Computational Methods 383
Some Advanced Computational Methods in NetLogo 391
Extensions to ABM 401
Integration of Advanced Data Sources and Output 402
Speed 418
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Contents ix
Applications of ABM 419
Revisiting the Trade-offs of ABM 423
The Future of ABM 424
Explorations 425
Appendix: The Computational Roots of Agent-Based Modeling 431
The Vignettes 433
Cellular Automata and Agent-Based Modeling 433
Genetic Algorithms, John Holland, and Complex Adaptive Systems 435
Seymour Papert, Logo, and the Turtle 439
Object-Oriented Programming and the Actor Model 440
Data Parallelism 442
Computer Graphics, Particle Systems, and Boids 443
Conclusion 445
References 447
Software and Models 459
Index 463
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