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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 Chapter 0 Why Agent-Based Modeling? Chapter 1 What Is Agent-Based Modeling? Chapter 2 Creating Simple Agent-Based Models Chapter 3 Exploring and Extending Agent-Based Models Chapter 4 Creating Agent-Based Models Chapter 5 The Components of Agent-Based Modeling Chapter 6 Analyzing Agent-Based Models Chapter 7 Verification, Validation, and Replication Chapter 8 Advanced Topics and Applications Appendix: The Computational Roots of Agent-Based Modeling
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 o 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 MIT Press books may be purchased at special quantity discounts for business or sales promotional use. For information, please email special_sales@ mitpress. mit. edu This 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 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-ll. Title T57.62.W542015 003.3-dc23 2014023747 10987654321 Contents Preface xi 0 Why Agent-Based Modeling? A Thought Experiment 3 Complex systems and emergence 5 Understanding Complex Systems and Emergence Example 1: Integrative Understanding 7 Example 2: Differential Understanding 8 Agent-Based Modeling as Representational Infrastructure for Restructurations 13 Example: Predator-Prey Interactions 15 Example: Forest Fires 18 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 explorating 42 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 Contents VIl 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 gent Cognition 224 Other Kinds of agents 232 Environments 234 Spatial enviro ts235 Network -Based Environments 241 Special environments 247 ns257 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 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 Verificatio idation, and Replication 3 Correctness of a Model 311 Ⅴ erification312 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 SU ummary Explorations 347 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 c al Methods 383 Some Advanced Computational Methods in NetLogo 391 Extensions to abm 401 Integration of Advanced Data Sources and Output 402 Speed 418 Contents X 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 paralleli 442 Computer Graphics, Particle Systems, and Boids 443 Conclusion 445 References 447 Software and Models 459 Index 463

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pengxiaotu 很好的资源。适合学习。学习NetLogo的很好的书籍,可惜国内这方面的书太少了。
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