Introduction to the Modeling and Analysis of Complex Systems

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12.4. RENORMALIZATION GROUP ANALYSIS TO PREDICT PERCOLATION... 225Exercise 12.9 Estimate the critical percolation threshold for the same forest firemodel but with von Neumann neighborhoods. Confirm the analytical result by conductingsimulations.Exercise 12.10 What would happen if the space of the forest fire propagationwere 1-D or 3-D? Conduct the renormalization group analysis to see what happensin those cases.
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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
Page 194 and 195: 10.2. INTERACTIVE SIMULATION WITH P
Page 200 and 201: 10.4. SIMULATION WITHOUT PYCX 181Co
Page 202 and 203: 10.4. SIMULATION WITHOUT PYCX 183re
Page 204 and 205: Chapter 11Cellular Automata I: Mode
Page 206 and 207: 11.1. DEFINITION OF CELLULAR AUTOMA
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Page 228 and 229: Chapter 12Cellular Automata II: Ana
Page 230 and 231: 12.2. PHASE SPACE VISUALIZATION 211
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Page 234 and 235: 12.3. MEAN-FIELD APPROXIMATION 215E
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Page 246 and 247: Chapter 13Continuous Field Models I
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Page 256 and 257: 13.3. VISUALIZING TWO-DIMENSIONAL S
Page 258 and 259: 13.3. VISUALIZING TWO-DIMENSIONAL S
Page 260 and 261: 13.4. MODELING SPATIAL MOVEMENT 241
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Page 288 and 289: Chapter 14Continuous Field Models I
Page 290 and 291: 14.1. FINDING EQUILIBRIUM STATES 27
Page 292 and 293: 14.2. VARIABLE RESCALING 273Exercis
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14.3. LINEAR STABILITY ANALYSIS OF
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Chapter 15Basics of Networks15.1 Ne
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15.2. TERMINOLOGIES OF GRAPH THEORY
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15.3. CONSTRUCTING NETWORK MODELS W
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15.4. VISUALIZING NETWORKS WITH NET
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15.4. VISUALIZING NETWORKS WITH NET
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15.5. IMPORTING/EXPORTING NETWORK D
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15.6. GENERATING RANDOM GRAPHS 321s
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15.6. GENERATING RANDOM GRAPHS 323R
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Chapter 16Dynamical Networks I: Mod
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16.2. SIMULATING DYNAMICS ON NETWOR
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16.3. SIMULATING DYNAMICS OF NETWOR
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16.4. SIMULATING ADAPTIVE NETWORKS
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372CHAPTER 17. DYNAMICAL NETWORKS I
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Chapter 18Dynamical Networks III: A
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18.2. DIFFUSION ON NETWORKS 40718.2
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18.3. SYNCHRONIZABILITY 4092.749157
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18.3. SYNCHRONIZABILITY 411The firs
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18.3. SYNCHRONIZABILITY 413nextg =
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18.3. SYNCHRONIZABILITY 415(a)1.0(b
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18.5. MEAN-FIELD APPROXIMATION ON R
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18.5. MEAN-FIELD APPROXIMATION ON R
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18.6. MEAN-FIELD APPROXIMATION ON S
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Chapter 19Agent-Based Models19.1 Wh
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19.1. WHAT ARE AGENT-BASED MODELS?
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19.2. BUILDING AN AGENT-BASED MODEL
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19.3. AGENT-ENVIRONMENT INTERACTION
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19.4. ECOLOGICAL AND EVOLUTIONARY M
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Bibliography[1] H. A. Simon, “The
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BIBLIOGRAPHY 467[26] J. D. Sterman,
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BIBLIOGRAPHY 469[52] J. E. Pearson,
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BIBLIOGRAPHY 471[78] M. E. Newman a
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474 INDEXcomplex adaptive system, 8
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476 INDEXlinear system, 36lineariza
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478 INDEXstate-transition function,
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Introduction to the Modeling and Analysis of Complex Systems

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