Introduction to the Modeling and Analysis of Complex Systems

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19.4. ECOLOGICAL AND EVOLUTIONARY MODELS 463agents.remove(ag)returnelse: # if there are rabbits nearbyif random() < rf:newborn = cp.copy(ag) ###newborn.m += uniform(-0.01, 0.01) ###agents.append(newborn) ###def update_one_unit_time():global agentst = 0.while t < 1.:t += 1. / len(agents)update()import pycxsimulatorpycxsimulator.GUI().start(func=[initialize, observe, update_one_unit_time])You can conduct simulations with this revised code to see how the prey’s and predators’mobilities evolve over time. Is the result consistent with or counter to your initialprediction? I hope you find some interesting discoveries.Exercise 19.11 Revise the observe function of the evolutionary predator-preyABM developed above so that you can see the distributions of m among prey andpredators. Observe how the agents’ mobilities spontaneously evolve in a simulation.Are they converging to certain values or continuously changing?Exercise 19.12 Conduct systematic simulations using both the original and evolutionarypredator-prey ABMs to quantitatively assess whether the introduction ofevolution into the model has made the coexistence of two species more robust andsustainable or not.Exercise 19.13 Make the reproduction rates of prey and predator agents alsoevolvable, in addition to the magnitude of their movements. Conduct simulationsto see how the reproduction rates evolve over time.
464 CHAPTER 19. AGENT-BASED MODELSBelieve it or not, we are now at the end of our over-450-page-long journey. Thanks forexploring the world of complex systems with me. I hope you enjoyed it. Needless to say,this is just the beginning for you to dive into a much larger, unexplored territory of complexsystems science. I believe there are tons of new properties of various complex systemsstill to be discovered, and it would be my utmost pleasure if this textbook was a bit of ahelp for you to make such discoveries in the coming years. Bon voyage!
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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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17.2. SHORTEST PATH LENGTH 37717.2
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17.2. SHORTEST PATH LENGTH 379Eccen
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17.3. CENTRALITIES AND CORENESS 381
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17.4. CLUSTERING 387while the numer
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17.5. DEGREE DISTRIBUTION 3891.00.8
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17.5. DEGREE DISTRIBUTION 391er = n
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17.5. DEGREE DISTRIBUTION 393Pk = [
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17.5. DEGREE DISTRIBUTION 395logkda
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17.6. ASSORTATIVITY 397Assortativit
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17.6. ASSORTATIVITY 399>>> nx.degre
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17.7. COMMUNITY STRUCTURE AND MODUL
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17.7. COMMUNITY STRUCTURE AND MODUL
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406CHAPTER 18. DYNAMICAL NETWORKS I
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Page 431 and 432: 412CHAPTER 18. DYNAMICAL NETWORKS I
Page 447 and 448: 428 CHAPTER 19. AGENT-BASED MODELSE
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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
Page 496 and 497: INDEX 477partial differential equat
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Introduction to the Modeling and Analysis of Complex Systems

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