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BK

Second edition

Princeton ; Oxford : Princeton University Press, [2019]

xviii, 340 stran : ilustrace ; 26 cm

ISBN 978-0-691-19083-9 (brožováno)

Obsahuje bibliografii na stranách 325-331 a rejstřík

001479208

Contents // Preface x i // Acknowledgments xvii // Part I Agent-Based Modeling and NetLogo Basics 7 // 1 Models, Agent-Based Models, and the Modeling Cycle 3 // 1.1 Introduction, Motivation, and Objectives 3 // 1.2 What Is a Model? 4 // 1.3 What Does the Modeling Cycle Involve? 7 // 1.4 What Is Agent-Based Modeling? How Is It Different? 10 // 1.5 Summary and Conclusions 72 // 1.6 Exercises 13 // 2 Getting Started with NetLogo 15 // 2.1 Introduction and Objectives 75 // 2.2 A QuickTour of NetLogo 76 // 2.3 A Demonstration Program: Mushroom Hunt 18 // 2.4 Summary and Conclusions 29 // 2.5 Exercises 32 // 3 Describing and Formulating ABMs: The ODD Protocol 35 // 3.1 Introduction and Objectives 35 // 3.2 What Is ODD and Why Use It? 36 // 3.3 The ODD Protocol 37 // 3.4 Our First Example: Virtual Corridors of Butterflies 43 // 3.5 Summary and Conclusions 46 // 3.6 Exercises 47 // 4 Implementing a First Agent-Based Model 49 // 4.1 Introduction and Objectives 49 // 4.2 ODD and NetLogo 49 // 4.3 Butterfly Hilltopping: From ODD to NetLogo 50 // 4.4 Comments and the Full Program 57 // 4.5 Summary and Conclusions 60 // 4.6 Exercises 6 ? // 5 From Animations to Science 63 // 5.1 Introduction and Objectives 63 // 5.2 Observation of Corridors 64 // 5.3 Analyzing the Model 69 // 5.4 Time-Series Results: Adding Plots and File Output 69 // 5.5 A Real Landscape 77 // 5.6 Summary and Conclusions 74 // 5.7 Exercises 75 // 6 Testing Your Program 77 // 6.1 Introduction and Objectives 77 // 6.2 Common

Kinds of Errors 78 // 6.3 Techniques for Debugging and Testing NetLogo Programs // 6.4 Documentation of Tests 9/ // 6.5 An Example and Exercise:The Culture Dissemination Model // 6.6 Summary and Conclusions 94 // 6.7 Exercises 95 // Part II Model Design Concepts 97 // 7 Introduction to Part II 99 // 7.1 Objectives of Part II 99 // 7.2 Overview of Part II 700 // 8 Emergence 103 // 8.1 Introduction and Objectives 703 // 8.2 A Model with Less Emergent Dynamics 104 // 8.3 Simulation Experiments and BehaviorSpace 705 // 8.4 A Model with Complex Emergent Dynamics 7 7 7 // 8.5 Summary and Conclusions 7 76 // 8.6 Exercises 7 76 // 9 Observation 7 79 // 9.1 Introduction and Objectives 7 79 // 9.2 Observing the Model via NetLogo’s View 120 // 9.3 Other Interface Displays 123 // 9.4 File Output 124 // 9.5 BehaviorSpace as an Output Writer 128 // 9.6 Export Primitives and Menu Commands 128 // 9.7 Summary and Conclusions 129 // 9.8 Exercises 729 // 10 Sensing 131 // 10.1 Introduction and Objectives 737 // 10.2 Who Knows What: The Scope of Variables 132 // 10.3 Using Variables of Other Objects 735 // 10.4 Putting Sensing to Work: The Business Investor Model 11 // 10.5 Summary and Conclusions 145 // 10.6 Exercises 146 // 11 Adaptive Behavior and Objectives 149 // 11.1 Introduction and Objectives 149 // 11.2 Identifying and Optimizing Alternatives in NetLogo 150 U S Adaptive Behavior in the Business Investor Model 154 // 11.4 Nonoptimizing Adaptive Behavior: A Satisficing Example 155 // 11.5

The Objective Function 158 // 11.6 Summary and Conclusions 159 // 11.7 Exercises 160 // 12 Prediction 161 // 12.1 Introduction and Objectives 161 // 12.2 Example Effects of Prediction: The Business Investor Model’s Time Horizon // 12.3 Implementing and Analyzing Submodels 464 // 12.4 Analyzing the Investor Utility Function 167 // 12.5 Modeling Prediction Explicitly 769 // 12.6 Summary and Conclusions 170 // 12.7 Exercises 171 // 13 Interaction 173 // 13.1 Introduction and Objectives 173 // 13.2 Programming Interaction in NetLogo 174 // 13.3 The Telemarketer Model 775 // 13.4 The March of Progress: Global Interaction 180 // 13.5 Direct Interaction: Mergers in the Telemarketer Model 180 // 13.6 The Customers Fight Back: Remembering Who Called 182 // 13.7 Summary and Conclusions 185 // 13.8 Exercises 186 // 14 Scheduling 189 // 14.1 Introduction and Objectives 189 // 14.2 Modeling Time in NetLogo 190 // 14.3 Summary and Conclusions 198 // 14.4 Exercises 799 // 15 Stochasticity 201 // 15.1 Introduction and Objectives 201 // 15.2 Stochasticity in ABMs 202 // 15.3 Pseudorandom Number Generation in NetLogo 204 // 15.4 An Example Stochastic Process: Empirical Model of Behavior 210 // 15.5 Summary and Conclusions 27 7 // 15.6 Exercises 213 // 16 Collectives 275 // 16.1 Introduction and Objectives 275 // 16.2 What Are Collectives? 276 // 16.3 Modeling Collectives in NetLogo 276 // 16.4 Example: A Wild Dog Model with Packs 218 // 16.5 Summary and Conclusions 228 // 16.6 Exercises 229

Partili Pattern-Oriented Modeling 231 // 17 Introduction to Part III 233 // 17.1 Toward Structurally Realistic Models 233 // 17.2 Single and Multiple, Strong and Weak Patterns 234 // 17.3 Overviewof Part III 236 // 18 Patterns for Model Structure 239 // 18.1 Introduction and Objectives 239 // 18.2 Steps in POM to Design Model Structure 240 // 18.3 Example: Modeling European Beech Forests 241 // 18.4 Example: Management Accounting and Collusion 245 // 18.5 Summary and Conclusions 246 // 18.6 Exercises 247 // 19 Theory Development 249 // 19.1 Introduction and Objectives 249 // 19.2 Theory Development and Strong Inference in the Virtual Laboratory 250 // 19.3 Examples of Theory Development for ABMs 252 // 19.4 Exercise Example: Stay or Leave? 255 // 19.5 Summary and Conclusions 259 // 19.6 Exercises 260 // 20 Parameterization and Calibration 263 // 20.1 Introduction and Objectives 263 // 20.2 Parameterization of ABMs Is Different 264 // 20.3 Parameterizing Submodels 265 // 20.4 Calibration Concepts and Strategies 266 // 20.5 Example: Calibration of the Woodhoopoe Model 272 // 20.6 Summary and Conclusions 275 // 20.7 Exercises 276 // Part IV Model Analysis 279 // 21 Introduction to Part IV 281 // 21.1 Objectives of Part IV 281 // 21.2 Overview of Part IV 282 // 22 Analyzing and Understanding ABMs 285 // 22.1 Introduction and Objectives 285 // 22.2 Example Analysis: The Segregation Model 286 // 22.3 Additional Heuristics for Understanding ABMs 29/ // 22.4 Statistics for Understanding

295 // 22.5 Summary and Conclusions 296 // 22.6 Exercises 297 // 23 Sensitivity, Uncertainty, and Robustness Analysis 299 // 23.1 Introduction and Objectives 299 // 23.2 Sensitivity Analysis 301 // 23.3 Uncertainty Analysis 307 // 23.4 Robustness Analysis 3/2 // 23.5 Summary and Conclusions 313 // 23.6 Exercises 314 // 24 Where to Go from Here 317 // 24.1 Introduction and Objectives 317 // 24.2 Keeping Your Momentum: Reimplementation 318 // 24.3 Your First Model from Scratch 318 // 24.4 Modeling Agent Behavior 319 // 24.5 ABM Gadgets 320 // 24.6 NetLogo as a Platform for Large Models 321 // 24.7 An Odd Farewell 323 // References 325 Index 333 // Index of Programming Notes 339