Introduction to the Modeling and Analysis of Complex Systems

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Chapter 19Agent-Based Models19.1 What Are Agent-Based Models?At last, we have reached the very final chapter, on agent-based models (ABMs). ABMsare arguably the most generalized framework for modeling and simulation of complexsystems, which actually include both cellular automata and dynamical networks as specialcases. ABMs are widely used in a variety of disciplines to simulate dynamical behaviorsof systems made of a large number of entities, such as traders’ behaviors in a market (ineconomics), migration of people (in social sciences), interaction among employees andtheir performance improvement (in organizational science), flocking/schooling behaviorof birds/fish (in behavioral ecology), cell growth and morphogenesis (in developmentalbiology), and collective behavior of granular materials (in physics). Figure 19.1 shows aschematic illustration of an ABM.It is a little challenging to define precisely what an agent-based model is, because itsmodeling assumptions are wide open, and thus there aren’t many fundamental constraintsthat characterize ABMs. But here is what I hope to be a minimalistic definition of them:Agent-based models are computational simulation models that involve many discreteagents.There are a few keywords in this definition that are important to understand ABMs.The first keyword is “computational.” ABMs are usually implemented as simulationmodels in a computer, where each agent’s behavioral rules are described in an algorithmicfashion rather than a purely mathematical way. This allows modelers to implementcomplex internal properties of agents and their nontrivial behavioral rules. Such representationsof complex individual traits are highly valued especially in social, organizational427
428 CHAPTER 19. AGENT-BASED MODELSEnvironmentAgent’s attributes• position• age• gender• knowledge• memory• experience• emotionetc…AgentsFigure 19.1: Schematic illustration of what an agent-based model (ABM) looks like.
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Introduction to the Modeling and An
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iiiAbout the TextbookIntroduction t
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New York. He is also a Fellow of th
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PrefaceThis is an introductory text
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a comprehensive view of the related
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Chen, Hal Lewis, Vlad Miskovic, Chu
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xviCONTENTS5 Discrete-Time Models I
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xviiiCONTENTS15.3 Constructing Netw
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Chapter 1Introduction1.1 Complex Sy
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1.1. COMPLEX SYSTEMS IN A NUTSHELL
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1.2. TOPICAL CLUSTERS 7The possibil
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1.2. TOPICAL CLUSTERS 9these topica
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12 CHAPTER 2. FUNDAMENTALS OF MODEL
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Part IISystems with a Small Number
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30 CHAPTER 3. BASICS OF DYNAMICAL S
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36 CHAPTER 4. DISCRETE-TIME MODELS
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Chapter 6Continuous-Time Models I:
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6.2. CLASSIFICATIONS OF MODEL EQUAT
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6.3. CONNECTING CONTINUOUS-TIME MOD
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6.4. SIMULATING CONTINUOUS-TIME MOD
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6.4. SIMULATING CONTINUOUS-TIME MOD
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6.5. BUILDING YOUR OWN MODEL EQUATI
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112 CHAPTER 7. CONTINUOUS-TIME MODE
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Chapter 8Bifurcations8.1 What Are B
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8.2. BIFURCATIONS IN 1-D CONTINUOUS
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8.3. HOPF BIFURCATIONS IN 2-D CONTI
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8.3. HOPF BIFURCATIONS IN 2-D CONTI
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8.4. BIFURCATIONS IN DISCRETE-TIME
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Chapter 9Chaos9.1 Chaos in Discrete
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9.1. CHAOS IN DISCRETE-TIME MODELS
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Next phase space9.3. LYAPUNOV EXPON
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9.3. LYAPUNOV EXPONENT 159easily co
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9.3. LYAPUNOV EXPONENT 16110Lyapuno
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9.4. CHAOS IN CONTINUOUS-TIME MODEL
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Chapter 10Interactive Simulation of
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10.2. INTERACTIVE SIMULATION WITH P
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10.4. SIMULATION WITHOUT PYCX 181Co
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10.4. SIMULATION WITHOUT PYCX 183re
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Chapter 11Cellular Automata I: Mode
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11.1. DEFINITION OF CELLULAR AUTOMA
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11.1. DEFINITION OF CELLULAR AUTOMA
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11.2. EXAMPLES OF SIMPLE BINARY CEL
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11.3. SIMULATING CELLULAR AUTOMATA
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11.5. EXAMPLES OF BIOLOGICAL CELLUL
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Chapter 12Cellular Automata II: Ana
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12.2. PHASE SPACE VISUALIZATION 211
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12.2. PHASE SPACE VISUALIZATION 213
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12.3. MEAN-FIELD APPROXIMATION 215E
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12.3. MEAN-FIELD APPROXIMATION 217T
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12.4. RENORMALIZATION GROUP ANALYSI
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228 CHAPTER 13. CONTINUOUS FIELD MO
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OutIn232 CHAPTER 13. CONTINUOUS FIE
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296 CHAPTER 15. BASICS OF NETWORKSg
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298 CHAPTER 15. BASICS OF NETWORKSi
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300 CHAPTER 15. BASICS OF NETWORKS5
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302 CHAPTER 15. BASICS OF NETWORKSE
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304 CHAPTER 15. BASICS OF NETWORKSC
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306 CHAPTER 15. BASICS OF NETWORKSC
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308 CHAPTER 15. BASICS OF NETWORKSt
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312 CHAPTER 15. BASICS OF NETWORKSs
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314 CHAPTER 15. BASICS OF NETWORKSn
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316 CHAPTER 15. BASICS OF NETWORKSL
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318 CHAPTER 15. BASICS OF NETWORKSJ
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320 CHAPTER 15. BASICS OF NETWORKSF
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322 CHAPTER 15. BASICS OF NETWORKSr
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326 CHAPTER 16. DYNAMICAL NETWORKS
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Chapter 17Dynamical Networks II: An
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17.1. NETWORK SIZE, DENSITY, AND PE
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17.1. NETWORK SIZE, DENSITY, AND PE
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Page 485 and 486: 466 BIBLIOGRAPHY[12] “The Human C
Page 487 and 488: 468 BIBLIOGRAPHY[39] H. Sayama, “
Page 489 and 490: 470 BIBLIOGRAPHY[65] P. Holme and J
Page 492 and 493: Indexaction potential, 202actor, 29
Page 494 and 495: INDEX 475equilibrium point, 61, 111
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INDEX 477partial differential equat
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Introduction to the Modeling and Analysis of Complex Systems

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