TABLE OF CONTENTS

Title Page

Copyright
Dedication

Foreword

Preface

About the Editors

List of Contributors

Section I: Emergent Behavior in Complex Systems

CHAPTER 1: METAPHYSICAL AND SCIENTIFIC ACCOUNTS OF EMERGENCE: VARIETIES OF FUNDAMENTALITY AND THEORETICAL COMPLETENESS
1. SUMMARY
2. INTRODUCTION
3. TO EXPLAIN IS NOT TO ELIMINATE
4. EMERGENT PROPERTIES AND MORE FUNDAMENTAL PROPERTIES
5. WHERE DOES THE PHILOSOPHICAL PROBLEM OF EMERGENCE COME FROM?
6. INCOMPLETENESS
7. MODELS, EMERGENCE, AND FUNDAMENTALITY
8. ACKNOWLEDGMENTS
9. REFERENCES
CHAPTER 2: EMERGENCE: WHAT DOES IT MEAN AND HOW IS IT RELEVANT TO COMPUTER ENGINEERING?
1. SUMMARY
2. INTRODUCTION
3. MAPPING MEANINGS OF EMERGENCE
4. EMERGENCE IN COMPUTER ENGINEERING
5. CONCLUSION: FUTURE DIRECTIONS
6. Bibliography
CHAPTER 3: SYSTEM THEORETIC FOUNDATIONS FOR EMERGENT BEHAVIOR MODELING: THE CASE OF EMERGENCE OF HUMAN LANGUAGE IN A RESOURCE-CONSTRAINED COMPLEX INTELLIGENT DYNAMICAL SYSTEM
1. SUMMARY
2. INTRODUCTION
3. BACKGROUND
4. EMERGENCE OF LANGUAGE CAPABILITIES IN HUMAN EVOLUTION
5. FUNDAMENTAL SYSTEMS APPROACH FOR RCIDS FOR EMERGENCE OF LANGUAGE EXAMPLE
6. PERSPECTIVES: EVOLUTION AND SYSTEM MODELING
7. RCIDS AND ACTIVITY-BASED PERSPECTIVE ON SHARED ATTENTION
8. CONCLUSIONS AND FUTURE WORK
9. DISCLAIMER
10. REFERENCES
CHAPTER 4: GENERATIVE PARALLAX SIMULATION: CREATIVE COGNITION MODELS OF EMERGENCE FOR SIMULATION-DRIVEN MODEL DISCOVERY
1. SUMMARY
2. INTRODUCTION
3. BACKGROUND
4. MODEL DISCOVERY AND EMERGENCE AS A CREATIVE COGNITION PROCESS
5. GENERATIVE PARALLAX SIMULATION: BASIC CONCEPTS
6. A REFERENCE ARCHITECTURE FOR MODEL ENSEMBLES AS COGNITIVE AGENTS
7. MODEL EMERGENCE VIA REFLECTIVE EQUILIBRIUM
8. CONCLUSIONS
9. REFERENCES

Section II: Emergent Behavior Modeling in Complex Systems Engineering

CHAPTER 5: COMPLEX SYSTEMS ENGINEERING AND THE CHALLENGE OF EMERGENCE
1. SUMMARY
2. INTRODUCTION
3. SYSTEMS ENGINEERING AND EMERGENCE
4. UNDERSTANDING AND MANAGING EMERGENCE
5. SUMMARY AND DISCUSSION
6. DISCLAIMER
7. REFERENCES
CHAPTER 6: EMERGENCE IN COMPLEX ENTERPRISES
1. SUMMARY
2. INTRODUCTION
3. COMPLEX SYSTEMS
4. MULTI-LEVEL MODELS
5. EMERGENCE IN SOCIETY
6. EMERGENCE IN CITIES
7. EMERGENCE IN INSTITUTIONS
8. EMERGENCE IN COMPANIES
9. ROLE OF MODELING AND SIMULATION
10. CONCLUSIONS
11. REFERENCES
CHAPTER 7: EMERGENCE IN INFORMATION ECONOMIES: AN AGENT-BASED MODELING PERSPECTIVE
1. INTRODUCTION
2. EMERGENCE IN THE SOCIAL SCIENCES
3. INFORMATION ECONOMIES AND COMPUTATIONAL SOCIAL SCIENCE
4. WHY AGENT-BASED MODELING?
5. MODEL OVERVIEW
6. DETAILS
7. EXPERIMENT AND FINDINGS
8. CONCLUSIONS
9. REFERENCES
CHAPTER 8: MODELING EMERGENCE IN SYSTEMS OF SYSTEMS USING THERMODYNAMIC CONCEPTS
1. SUMMARY
2. INTRODUCTION
3. THERMODYNAMICS AND ITS EXPLANATORY ADVANTAGES
4. EMERGENCE AND CHEMICAL REACTIONS
5. DEVELOPING A CONCEPTUAL MODEL OF EMERGENCE IN SOS
6. POTENTIAL IMPLICATION
7. CONCLUSIONS
8. RECOMMENDATIONS AND FUTURE WORK
9. REFERENCES
CHAPTER 9: INDUCED EMERGENCE IN COMPUTATIONAL SOCIAL SYSTEMS ENGINEERING: MULTIMODELS AND DYNAMIC COUPLINGS AS METHODOLOGICAL BASIS
1. SUMMARY
2. INTRODUCTION
3. COMPUTATIONAL SOCIAL SYSTEMS ENGINEERING AND INDUCED EMERGENCE
4. MULTIMODELS
5. MODEL COUPLING
6. VARIABLE STRUCTURE MODELS
7. INDUCED EMERGENCE IN COMPUTATIONAL SOCIAL SYSTEMS ENGINEERING: ROLES OF MULTIMODELS
8. BEYOND AGENT-BASED FOR MULTIMODELING AND SIMULATING EMERGENCE
9. CONCLUSIONS AND FUTURE STUDIES
10. DISCLAIMER
11. REFERENCES
CHAPTER 10: APPLIED COMPLEXITY SCIENCE: ENABLING EMERGENCE THROUGH HEURISTICS AND SIMULATIONS
1. INTRODUCTION – A COARSE-GRAINED LOOK
2. DEFINITIONS AND A TAXONOMY FOR APPLIED COMPLEXITY SCIENCE
3. HEURISTICS FOR APPLYING COMPLEXITY SCIENCE TO ENGINEER FOR EMERGENCE
4. UNMANNED AUTONOMOUS VEHICLE (UV) SWARMS
5. OPERATIONAL UV SWARMS
6. DISCUSSION AND CONCLUSIONS
7. DISCLAIMER
8. REFERENCES

Section III: Engineering Emergent Behavior in Computational Environments

CHAPTER 11: TOWARD THE AUTOMATED DETECTION OF EMERGENT BEHAVIOR
1. INTRODUCTION
2. OVERVIEW OF EXISTING WORK
3. IDENTIFYING EMERGENCE USING INTERACTION GRAPHS
4. EXPERIMENTAL ANALYSIS
5. CONCLUSION
6. References
CHAPTER 12: ISOLATING THE CAUSES OF EMERGENT FAILURES IN COMPUTER SOFTWARE
1. SUMMARY
2. INTRODUCTION
3. ISOLATING THE CAUSE OF DETERMINISTIC FAILURE
4. EMERGENT FAILURES
5. STOCHASTICS IN SOFTWARE
6. FAULTS ACTIVATED BY EFFECTS OF THE INTERNAL SYSTEM ENVIRONMENT
7. SUMMARY
8. REFERENCES
CHAPTER 13: FROM MODULARITY TO COMPLEXITY: A CROSS-DISCIPLINARY FRAMEWORK FOR CHARACTERIZING SYSTEMS
1. SUMMARY
2. INTRODUCTION
3. CHARACTERIZING SYSTEMS
4. ASPECTS AND ABSTRACTIONS OF MODULARITY
5. ASPECTS OF COMPLEXITY
6. CONCLUSIONS
7. REFERENCES
Chapter 14: THE EMERGENCE OF SOCIAL SCHEMAS AND LOSSY CONCEPTUAL INFORMATION NETWORKS: HOW INFORMATION TRANSMISSION CAN LEAD TO THE APPARENT “EMERGENCE” OF CULTURE
1. SUMMARY
2. INTRODUCTION
3. CONSENSUS IN LARGE HUMAN SOCIAL GROUPS
4. OVERVIEW OF MODEL
5. TESTING THE MODEL
6. RESULTS
7. EMERGENCE OF SOCIAL CONSENSUS IN THE IIS
8. CONCLUSION: EMERGENCE, WHAT IS CLAIMED, AND WHAT SHOULD NEVER BE
9. REFERENCES
CHAPTER 15: MODELING AND SIMULATION OF EMERGENT BEHAVIOR IN TRANSPORTATION INFRASTRUCTURE RESTORATION
1. SUMMARY
2. INTRODUCTION
3. SYSTEM DYNAMICS APPROACH
4. METHODOLOGY
5. ILLUSTRATIVE EXAMPLE
6. CONCLUSION AND FUTURE WORK
7. REFERENCES

Section IV: Research Agenda

CHAPTER 16: RESEARCH AGENDA FOR NEXT-GENERATION COMPLEX SYSTEMS ENGINEERING
1. SUMMARY
2. ON ENGINEERING EMERGENCE: LEARNING FROM OTHERS
3. MAKING SENSE OF EMERGENT BEHAVIOR: A MIXED METHOD APPROACH
4. GRAND CHALLENGES OF EMERGENCE RESEARCH: A RESEARCH AGENDA
5. DISCLAIMER
6. REFERENCES
Index

End User License Agreement

List of Illustrations

CHAPTER 1: METAPHYSICAL AND SCIENTIFIC ACCOUNTS OF EMERGENCE: VARIETIES OF FUNDAMENTALITY AND THEORETICAL COMPLETENESS

Figure 1.1 Combinations of novelty and naturalness with respect to kinds of fundamental property.

CHAPTER 2: EMERGENCE: WHAT DOES IT MEAN AND HOW IS IT RELEVANT TO COMPUTER ENGINEERING?

Figure 2.1 Types of emergence.

CHAPTER 3: SYSTEM THEORETIC FOUNDATIONS FOR EMERGENT BEHAVIOR MODELING: THE CASE OF EMERGENCE OF HUMAN LANGUAGE IN A RESOURCE-CONSTRAINED COMPLEX INTELLIGENT DYNAMICAL SYSTEM

Figure 3.1 System with timed transitions.
Figure 3.2 Sensor-gateway system model for a single-level architecture.
Figure 3.3 Tri-layered architecture for positive emergence.
Figure 3.4 Interaction of two components attempting to form a composite system.
Figure 3.5 DEVS modeling of one-way agent-agent communication.

CHAPTER 4: GENERATIVE PARALLAX SIMULATION: CREATIVE COGNITION MODELS OF EMERGENCE FOR SIMULATION-DRIVEN MODEL DISCOVERY

Figure 4.1 Models as mediators.
Figure 4.2 Systems model of creativity.
Figure 4.3 Generate and explore reference model.
Figure 4.4 Model ensembles.
Figure 4.5 Reference architecture.
Figure 4.6 Connectionist constraint network.

CHAPTER 5: COMPLEX SYSTEMS ENGINEERING AND THE CHALLENGE OF EMERGENCE

Figure 5.1 The “Vee” model of systems engineering.
Figure 5.2 System categories as defined by Kurtz and Snowden (2003).
Figure 5.3 Systems and emergence.

CHAPTER 6: EMERGENCE IN COMPLEX ENTERPRISES

Figure 6.1 Architecture of complex enterprises.
Figure 6.2 The world as a system.
Figure 6.3 A city as a system.
Figure 6.4 Comparison of organizational behaviors.
Figure 6.5 Architecture of academic enterprises.
Figure 6.6 Hybrid architecture of academia.
Figure 6.7 Architecture of healthcare delivery enterprise.
Figure 6.8 Transformation framework.
Figure 6.9 Product/technology strategy.
Figure 6.10 Product/market model.
Figure 6.11 Simulation of NYC Health Ecosystem in Immersion Lab.
Figure 6.12 Strategy framework for enterprise decision makers.

CHAPTER 7: EMERGENCE IN INFORMATION ECONOMIES: AN AGENT-BASED MODELING PERSPECTIVE

Figure 7.1 Technology phenotype examples.
Figure 7.2 Agents assess Hamming distances and, if unhappy, eliminate the “farthest” neighbor and adopting a new technology.
Figure 7.3 Network structures and Hamming distances at initialization.
Figure 7.4 Network structures and Hamming distances at t = 500.
Figure 7.5 Logged degree distribution.
Figure 7.6 Frequency of technologies adopted at t = 500, for nine sample runs representing different starting network structures (left to right) and different technology complexities (5, 7, and 10 bit string lengths top to bottom).

CHAPTER 8: MODELING EMERGENCE IN SYSTEMS OF SYSTEMS USING THERMODYNAMIC CONCEPTS

Figure 8.1 Endothermic chemical reaction.
Figure 8.2 Conceptual model of emergence in system of systems.
Figure 8.3 Causal tree diagram: factors for emergent effects in engineered system.

CHAPTER 9: INDUCED EMERGENCE IN COMPUTATIONAL SOCIAL SYSTEMS ENGINEERING: MULTIMODELS AND DYNAMIC COUPLINGS AS METHODOLOGICAL BASIS

Figure 9.1 A multiaspect multimodel of water.
Figure 9.2 A multistage multimodel.
Figure 9.3 A coupled model Z consisting of component models Z1-Z5.
Figure 9.4 A template for model coupling.
Figure 9.5 Declarative specification of the coupling of the coupled model depicted in Figure 9.3 (“a → b” and “c ← d” can be read as “a is connected to b” and “c is connected from d”).
Figure 9.6 Declarative specification of the internal coupling of the coupled model depicted in Figure 9.3.
Figure 9.7 An extensible finite-state machine.

CHAPTER 10: APPLIED COMPLEXITY SCIENCE: ENABLING EMERGENCE THROUGH HEURISTICS AND SIMULATIONS

Figure 10.1 Complex systems engineering. The feedback between traditional engineering practice and complexity science. The emergent phenomenon exhibited at the largest scale of the system's existence (the right side of the figure) has a top-down influence on the system's components (created via traditional engineering of subcomponents (sC a–h) that are combined into larger system components (Component 1 … n)). Traditional systems engineering practices do not pay mind to this cross-scale feedback loop, the study of which is a primary focus of complexity science.
Figure 10.2 Sample swarm dynamics. Variance in heading of the U × V swarm as density is decreased by holding swarm size constant and varying environment from a 31 × 31 patch size to a 101 × 101 patch size in increments of 10. Swarms form very quickly at a density of 250 U × Vs in a 51 × 51 size patch. (a) Swarm heading dynamics, 250 U × Vs, 31 × 31 space. (b) Swarm heading dynamics, 250 U × Vs, 41 × 41 space. (c) Swarm heading dynamics, 250 U × Vs, 51 × 51 space. (d) Swarm heading dynamics, 250 U × Vs, 61 × 61 space. (e) Swarm heading dynamics, 250 U × Vs, 71 × 71 space. (f) Swarm heading dynamics, 250 U × Vs, 81 × 81 space. (g) Swarm heading dynamics, 250 U × Vs, 91 × 91 space. (h) Swarm heading dynamics, 250 U × Vs, 101 × 101 space.
Figure 10.3 Highly coherent swarms. The impact of unorganized obstacles on swarm dynamics, as can be seen, variance tends to increase with the number of obstacles. Highly coherent swarms are not found if 4 or more obstacles are introduced, as is indicated by the horizontal gray line. (a) Variance of heading, 250 U × Vs in a 51 × 51 world, correlated obstacle of size 2. (b) Variance of heading, 250 U × Vs in a 51 × 51 world, correlated obstacle of size 4. (c) Variance of heading, 250 U × Vs in a 51 × 51 world, correlated obstacle of size 6. (d) Variance of heading, 250 U × Vs in a 51 × 51 world, correlated obstacle of size 8.
Figure 10.4 Highly organized obstacles. The impact of organized obstacles on swarm dynamics. We can see that as the organized obstacles become large enough, new emergent swarm dynamics form within the system's new boundaries. (a) Variance of heading, 250 U × Vs in a 51 × 51 world, correlated obstacle of size 2. (b) Variance of heading, 250 U × Vs in a 51 × 51 world, correlated obstacle of size 4. (c) Variance of heading, 250 U × Vs in a 51 × 51 world, correlated obstacle of size 6. (d) Variance of heading, 250 U × Vs in a 51 × 51 world, correlated obstacle of size 8.

CHAPTER 11: TOWARD THE AUTOMATED DETECTION OF EMERGENT BEHAVIOR

Figure 11.1 Architecture overview.
Figure 11.2 Pre-processing agents based on their interactions.
Figure 11.4 Comparison between $c011-math-0123$ and $c011-math-0124$ . (a) Flock of birds: $c011-math-0125$ with minimum HD: 109.77. (b) Flock of birds: reference graph: $c011-math-0126$
Figure 11.3 Game of life with pulsar pattern.
Figure 11.5 Comparison between $c011-math-0133$ and $c011-math-0134$ . (a) Flock of birds: $c011-math-0135$ with minimum HD: 86.84. (b) Flock of birds: reference graph: $c011-math-0136$ .
Figure 11.6 Comparison between $c011-math-0153$ and $c011-math-0152$ . (a) Predator-Prey: $c011-math-0153$ with minimum HD: 5.657. (b) Predator-Prey: reference graph: $c011-math-0154$ .
Figure 11.7 Comparison between Flock of Birds $c011-math-0163$ , $c011-math-0164$ and $c011-math-0165$ . (a) Flock of Birds: $c011-math-0166$ with minimum HDA: 3.58. (b) Flock of Birds: reference graph: $c011-math-0167$ . (c) Flock of Birds: $c011-math-0168$ with minimum HDA: 72.35.
Figure 11.8 IG $c011-math-0172$ with minimum HDA: 43.06.
Figure 11.9 Comparison between $c011-math-0184$ , $c011-math-0187$ and $c011-math-0201$ . (a) $c011-math-0205$ with $c011-math-0192$ closest to 0: $c011-math-0193$ 0.53. (b) $c011-math-0194$ with $c011-math-0195$ closest to 0: 0.003. (c) Flock of Birds: reference graph: $c011-math-0196$ .
Figure 11.10 IG $c011-math-0212$ for two bird types with $c011-math-0213$ closest to $c011-math-0214$ : 0.07.
Figure 11.11 Game of life: $c011-math-0275$ with $c011-math-0276$ closest to 0: 0.000.
Figure 11.12 Best results from HD, HDA, and $c011-math-0298$ when compared to the reference graph $c011-math-0299$ . (a) Reference graph: $c011-math-0300$ . (b) HD: $c011-math-0301$ . (c) HDA: $c011-math-0302$ . (d) $c011-math-0303$ : $c011-math-0304$ .
Figure 11.13 Best results from $c011-math-0306$ for Game of Life models with different agent amounts when compared to the reference graph $c011-math-0307$ . (a) Reference graph: $c011-math-0308$ . (b) $c011-math-0309$ : IG $c011-math-0310$ . (c) $c011-math-0311$ : $c011-math-0312$ .

CHAPTER 12: ISOLATING THE CAUSES OF EMERGENT FAILURES IN COMPUTER SOFTWARE

Figure 12.1 The set union and set intersection approaches to statistical debugging.
Figure 12.2 Statistical debugging example using Tarantula.
Figure 12.3 Eclipse plugin for automated debugging based on Tarantula's ranking. Note: the program being debugged is different from the one shown in Figure 12.2.
Figure 12.4 An example of CBI-ranked predicates from a numeric program.
Figure 12.5 An overview of the Whyline tool being used in a graphical programming environment.
Figure 12.6 A visualization of the Ising model and the algorithm used to implement it.
Figure 12.7 Fuzzy membership with designations of young, middle-aged, and old.
Figure 12.8 Equation specifying the passing extent, u, of an output list X.
Figure 12.9 The result of applying the fuzzy passing function to test cases for the faulty implementation of the Ising model. The center portion of the curve reflects fuzzy passing values that applying the fuzzy function to the test cases did not yield.
Figure 12.10 A multi-threaded program with four lines yielding six unique execution sequences and four unique end states.
Figure 12.11 Software layer and flow diagram for scheduling determinism in programs with emergent faults (Musuvathi et al., 2008).

CHAPTER 13: FROM MODULARITY TO COMPLEXITY: A CROSS-DISCIPLINARY FRAMEWORK FOR CHARACTERIZING SYSTEMS

Figure 13.1 Composition.
Figure 13.2 Classification.
Figure 13.3 Multiple characterizations.
Figure 13.4 Clean hierarchy.
Figure 13.5 Heterarchy.
Figure 13.6 Three aspects of modularity.
Figure 13.7 Function-driven encapsulation.
Figure 13.8 Interface compatibility.
Figure 13.9 Architectural variety from interface compatibility.
Figure 13.10 Component swapping and component sharing.
Figure 13.11 Redundancy through duplicated architectures and distinct architectures.
Figure 13.12 Robustness through multi-structural function realization.
Figure 13.13 Context-dependent multi-functionality.
Figure 13.14 Functions mapped to architectures defined at different levels.
Figure 13.15 Endogenous and exogenous functions.
Figure 13.16 State transition rules.
Figure 13.17 Different behavioral trajectories depending on initial state.
Figure 13.18 Environmentally influenced behavioral rules.

Chapter 14: THE EMERGENCE OF SOCIAL SCHEMAS AND LOSSY CONCEPTUAL INFORMATION NETWORKS: HOW INFORMATION TRANSMISSION CAN LEAD TO THE APPARENT “EMERGENCE” OF CULTURE

Figure 14.1 Mean motivation level for two groups over time.
Figure 14.2 Mean social identification level for two groups over time.
Figure 14.3 Consensus for two groups. 1 = total consensus on beliefs among members of a group; 0 = total disagreement among members of a group.
Figure 14.4 Average fusion levels for two groups.
Figure 14.5 Group sizes for two groups in the simulation. Note that this only tracks raw group identification.
Figure 14.6 Individual participation encodes information into personal belief schemas, which in turn are used to align with social belief schemas.
Figure 14.7 Consensus visualized as an “emergent” social process.

CHAPTER 15: MODELING AND SIMULATION OF EMERGENT BEHAVIOR IN TRANSPORTATION INFRASTRUCTURE RESTORATION

Figure 15.1 Cause-and-effect relationship.
Figure 15.2 Road capacity lost due to connectivity issue.
Figure 15.3 Road capacity used per road maintenance.
Figure 15.4 Road capacity used per emergency vehicle.
Figure 15.5 Effect of transportation network disruption on travel costs per vehicle.
Figure 15.6 Maximum capacity of the road.
Figure 15.7 Factors affecting the available road capacity.
Figure 15.8 Speed versus cost graph when 140 and 280 vehicles are rerouted on the two alternate paths. The light gray line shows the results when 140 crucks are rerouted and the dark gray line shows the results when 280 crucks are rerouted. The first and second numbers in brackets on the graph are the number of vehicles on alternate route 1 and 2, respectively.

CHAPTER 16: RESEARCH AGENDA FOR NEXT-GENERATION COMPLEX SYSTEMS ENGINEERING

Figure 16.1 Experimental LVC approach for generating emergence.
Figure 16.2 Simulation experience approach (SEA) to design an LVC experiment to investigate ontological emergence.

List of Tables

CHAPTER 3: SYSTEM THEORETIC FOUNDATIONS FOR EMERGENT BEHAVIOR MODELING: THE CASE OF EMERGENCE OF HUMAN LANGUAGE IN A RESOURCE-CONSTRAINED COMPLEX INTELLIGENT DYNAMICAL SYSTEM

Table 3.1 DEVS Levels of Systems Specification
Table 3.2 Positive Emergence Tri-Layered Architecture as Applicable in the Case of Human Language
Table 3.3 Relating RCIDS Characteristics with DEVS Levels of Systems Specifications for Emergence of Language
Table 3.4 Introducing Dynamism at Various Levels of System Specifications (Mittal, 2013)

CHAPTER 5: COMPLEX SYSTEMS ENGINEERING AND THE CHALLENGE OF EMERGENCE

Table 5.1 Observations in Traditional and Complex Systems Engineering
Table 5.2 Supporting Methods for Complex Systems Engineering

CHAPTER 8: MODELING EMERGENCE IN SYSTEMS OF SYSTEMS USING THERMODYNAMIC CONCEPTS

Table 8.1 Thermodynamic Analogies
Table 8.2 Variable Conversions: Chemical System to System of Systems

CHAPTER 9: INDUCED EMERGENCE IN COMPUTATIONAL SOCIAL SYSTEMS ENGINEERING: MULTIMODELS AND DYNAMIC COUPLINGS AS METHODOLOGICAL BASIS

Table 9.1 Multidisciplinary Nature of Computational Social Science (CSS) and its Impact on Computational Social Systems Engineering (CSSE)
Table 9.2 Types of Multimodels Based on Modeling Methodologies, Submodel Structure, and on Submodel Activation Knowledge
Table 9.3 Major Categories of Multimodels and Criteria to Distinguish Them
Table 9.4 Ontological Dictionary of Terms Related with Multimodels: Based on Modeling Methodology of Submodels (Atomic Models)
Table 9.9 Ontological Dictionary of Terms Related with Multimodels: Based on Submodel Activation Knowledge (Location of the Knowledge to Activate Submodels)
Table 9.7 Ontological Dictionary of Terms Related with Multimodels: Nased on Structure of Submodels (Variability of Structure of the Submodels)
Table 9.10 Multimodels Based on Modeling Methodology of Submodels (Atomic Models Where They can be Used for Simulation-Based CSS Studies)
Table 9.13 Multimodels Based on Structure of Submodels (Variability of Structure of the Submodels) Where They can be Used for Simulation-Based CSS Studies
Table 9.14 Multimodels Based on Submodel Activation Knowledge (Nature and Location of the Knowledge to Activate Submodels) and the Methods to Induce Emergence for Simulation-Based CSS Studies

CHAPTER 10: APPLIED COMPLEXITY SCIENCE: ENABLING EMERGENCE THROUGH HEURISTICS AND SIMULATIONS

Table 10.1 A Taxonomy of Applied Complexity Science
Table 10.2 Swarm Coherence as a Function of Density for Environment Sizes 30 × 30 to 100 × 100

CHAPTER 11: TOWARD THE AUTOMATED DETECTION OF EMERGENT BEHAVIOR

Table 11.1 Differences in Model Features
Table 11.2 $c011-math-0114$ : 20 Birds, $c011-math-0115$
Table 11.3 $c011-math-0127$ : 50 Birds, $c011-math-0128$
Table 11.4 $c011-math-0144$ : 4 Predators, 8 Prey, $c011-math-0145$ Grid, $c011-math-0146$
Table 11.5 $c011-math-0137$ : 8 Predators, 8 Prey, $c011-math-0138$ grid, $c011-math-0139$
Table 11.6 $c011-math-0157$ : 20 Birds, $c011-math-0158$
Table 11.7 $c011-math-0174$ : 4 Predators, 8 Prey, $c011-math-0175$ Grid, $c011-math-0176$
Table 11.8 $c011-math-0190$ : 50 Birds, $c011-math-0191$
Table 11.9 $c011-math-206$ : 50 Birds, $c011-math-0207$
Table 11.10 $c011-math-0231$ : $c011-math-0232$ Cells, $c011-math-0233$ ; Random Initialization
Table 11.11 $c011-math-0215$ : $c011-math-0216$ Cells, $c011-math-0217$ ; Glider Pattern
Table 11.12 $c011-math-0255$ : $c011-math-0248$ Cells, $c011-math-0257$ ; Pulsar Pattern
Table 11.13 $c011-math-0265$ : $c011-math-0266$ Cells, $c011-math-0267$ ; Pulsar Pattern
Table 11.14 $c011-math-0281$ : 4 Predators, 8 Prey, $c011-math-0282$ Grid, $c011-math-0283$

CHAPTER 13: FROM MODULARITY TO COMPLEXITY: A CROSS-DISCIPLINARY FRAMEWORK FOR CHARACTERIZING SYSTEMS

Table 13.1 Different Complex Systems Characterizations Related to Different Aspects and Abstractions of Modularity (Non-Complex Systems Characterizations)
Table A.1 Aspects of Modularity Found in Different Definitions of Modularity

Chapter 14: THE EMERGENCE OF SOCIAL SCHEMAS AND LOSSY CONCEPTUAL INFORMATION NETWORKS: HOW INFORMATION TRANSMISSION CAN LEAD TO THE APPARENT “EMERGENCE” OF CULTURE

Table 14.1 Mapping Definitions of Emergence in the Social Science and Modeling and Simulation Literatures
Table 14.2 Values for Parameter Sweep Experiment
Table 14.3 Means for Social Identity by Group Size
Table 14.4 Overview of Approaches to Emergence in Social Systems

CHAPTER 15: MODELING AND SIMULATION OF EMERGENT BEHAVIOR IN TRANSPORTATION INFRASTRUCTURE RESTORATION

Table 15.1 Factors Affecting Available Road Capacity
Table 15.2 Average Length and Buffer Length for Vehicles Traveling at 55 mph
Table 15.3 Average Length of Vehicle Based on the Speed at Which the Traffic is Flowing
Table 15.4 Number of Vehicles on Alternate Paths and Length of Alternate Paths
Table 15.5 Available Road Capacity on Alternate Routes for Vehicles Traveling at Different Speeds
Table 15.6 Available Road Capacity

CHAPTER 16: RESEARCH AGENDA FOR NEXT-GENERATION COMPLEX SYSTEMS ENGINEERING

Table 16.1 Sample of Fields that Study Emergence
Table 16.2 Summary of Chapter's View of Emergence
Table 16.3 SEA Framework as a Transdisciplinary Approach

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FOREWORD

The etymological roots for the word Emergence are in the Latin words “emergere” or “emergo,” which mean to arise or to bring into the light: something that was covered or hidden becomes visible. Only in the recent decades has the term has been used in science and philosophy to reference the observation of some new properties that are exposed by natural and engineered systems without having been explicitly created. Such an emergent property of a system is usually discovered at the macro-level of the behavior of the system and cannot be immediately traced back to the specifications of the components, whose interplay produce this emergence. In natural systems, this may reflect a lack of depth of understanding of the phenomena of interest; for engineered systems, this tends to reflect a lack of understanding of the implications of design decisions.

The confusion with the term emergence is nearly Babylonian. The term is used in many different ways in science and philosophy, and its definition is a substantive research question itself. Researchers are not sure if their observations are domain specific, or if they must contribute in multi-, trans-, and interdisciplinary research endeavors to new insights into the bigger, general challenges of system thinking. Philosophy discusses the differences between ontological and epistemological emergence. Scientists are applying new methods, many from the field of modeling and simulation, to generate emergent behaviors and gain new insight from the study of the dynamic representation of such systems that can produce emergence. But who within these communities holds the Holy Grail?

In his book, The Tao of Physics (1975), Fritjof Carpa, the founding director of the Center for Ecoliteracy in Berkeley, California, writes in the epilogue: “Mystics understand the roots of the Tao but not its branches; scientists understand its branches but not its roots. Science does not need mysticism and mysticism does not need science; but man needs both.” Such a view drove us to design and develop this book. Complexity has led us to understand the limits of reductionism. Such findings may not only be true for individual disciplines, but generally even more so for multi-, trans-, and interdisciplinary research. The very first step to enable such cooperation is to get to know each other. The resulting mix of invited experts in this volume therefore exposes a high degree of diversity, embracing many different views, definitions, and interpretations to show the many facets that collectively contribute to the bigger picture, hoping that we be able to reach a similar conclusion as Carpa, who states in the same source referenced above: “The mystic and the physicist arrive at the same conclusion; one starting from the inner realm, the other from the outer world. The harmony between their views confirms the ancient Indian wisdom that Brahman, the ultimate reality without, is identical to Atman, the reality within.” We need experts highly educated and experienced in their facets who are willing to talk and listen to each other.

Our underlying guidance to all authors was to think about how their contribution can make a difference for those who are designing, developing, managing, operating, and maintaining systems, including system of systems, in helping them to better detect, understand, and hopefully manage emergence to reap the benefits, for example, of innovations, and avoid the dangers, for example, of unfortunate consequences. What can system scientists and engineers contribute? Can we construct simulation systems that reproduce natural systems closely enough to gain insights about emergent behavior? How should our management and governance of complex systems look? Can we validate emergence? Is emergent always repeatable, or is it path dependent? Can we apply higher principles, such as entropy, to gain more insight? What are the computational and epistemological constraints we must be aware of? A much broader approach that involves experts from many domains is needed.

Simulation always has been a melting pot of expertise coming from many disciplines interested in many different application domains. The state-of-the-art presented in this book about methods and technologies that aim to understand emergent behavior in complex systems engineering in various scientific, social, economic, and multidisciplinary systems engineering disciplines is defining the new frontiers for humankind. The insights elaborated here have broader, ongoing consequences than expected, as we are witnessing a closely related evolution in science: the increasing use of computational means to support research. Computational science emerges – pun intended – in many disciplines: computational physics, biology, social science, systems engineering, finance, to name just a few. In order to use computational means, these disciplines first have to build models about the phenomena of interest and then build algorithms to make them executable on computers: in other words, they are constructing models and simulations. The same limits and constraints on validity of simulation approaches to complexity research are therefore applicable to computational science dealing with complex systems as well.

Complexity introduces a set of challenges to engineers, scientists, and managers in many real-world applications that affect our daily life. A better understanding of emergence in such systems, including possible limits and constraints of what we can do with current methods and tools, will enable making best use of such systems to serve society better. This book is not a solution book, but a foundations book, addressing the fields that have to contribute to address the research questions that have to be answered to better detect, understand, and manage emergence and complexity.

William Rouse

Stevens Institute of Technology

Alexander Crombie Humphreys Professor

Director of the Center for Complex Systems and Enterprises

Andreas Tolk

The MITRE Corporation

Fellow of the Society for Modeling and Simulation

September 15, 2017

PREFACE

We are surrounded by emergence.

Human civilization transformed through significant periods starting from the hunter-gatherer era, through the agricultural period, to the industrial age, and now the information and digital age. Each period emerges from the previous over time not only through technological advances and economic progress but also through conflicts, war, and transformative political and social changes. What qualifies a period in history as an “era?” How does an era start, and why does it end? Among the many reasons we have listed above, it is important to emphasize the impact of technology on society and the role technological revolutions (Industrial revolution, Internet, etc.) play in shaping the direction of Humanity. Having said that, we are not completely sure how era-changing technologies come into being and are mostly unable to predict which technologies will change civilization, and which will go unnoticed. We can only observe that when a new technology appears, it is sometimes met with skepticism, mockery, ridicule, and denial. Such reactions are often due to the lack of understanding of the technology and its implications. However, some technologies – once created – add tremendous knowledge and insight while spawning new industries, disciplines, and ecosystems that generate new professions and a new workforce, thus bringing about a new societal structure that can cope with the new technology. Some technologies are so disruptive and life changing that they mark the beginning of a new era. Would not it be desirable to better understand technologies that have the potential for such large-scale emergence or maybe even able to predict and manage the consequent emergence? Might Isaac Asimov's vision of Hari Seldon's Psychohistory become a reality? Are we on the cusp of the emergence of a new era?

Beyond societal emergence, engineered systems capable of displaying emergent behavior are entering our daily routines at a high rate. For instance, there is currently an increasing number of unmanned system technology being applied in a wide variety of domains. Robots are conducting surgeries; we see self-driving cars maturing; packages are delivered by drones, and unmanned systems show up on the battlefield. These unmanned systems observe their environment, compare the perceived situation with their goals, and then follow rules set to achieve their objectives. Even relatively simple rules can lead to very complex swarm behavior, exposing emergent behavior beyond the intention of the designers. If this behavior is helpful in reaching their planned objective, all is good, but where is the threshold for such behavior to become dangerous or even harmful? How can we better recognize unintended consequences, which may easily be magnified due to the many and often nonlinear connections between the components? How can we ensure that such unmanned solutions evolve into a favorable direction and not like James Cameron's Skynet into an existential threat for society?

It is such questions and ideas that have motivated us to work on this book. We want to understand the world as a complex system and to gain some semblance of control as we inject more and more engineered systems in this existing complex system. We want to answer questions such as: Is emergence systemic, or can we reduce or even eliminate it as we gain enough knowledge about the system, its components, and relations? Do we need better tools and methods to study emergence? We strive to bring together the discipline of complex systems engineering that needs to incorporate the element of complexity, inherent in the very structure of a system, and the elements of emergent behavior that complex system engineering could never design in the first place but still needs to account for.

To this end, we are particularly interested in exploring the subject of emergence through the lens of Modeling and Simulation (M&S). Modeling is the art of simplification and abstraction, taking only “so much” from reality to answer the question put forth at the right abstraction level. Simulation is the increasingly computerized execution of a model over time to understand its dynamic behavior. Such computational means are potent tools that allow scientists and engineers to hypothesize, design, analyze, and even theorize about a particular phenomenon. Can we recreate emergence in such artificial systems in a way that helps us understand emergence in the real system of interest better? What are the limits of such M&S support? Furthermore, M&S supports scientist in social sciences with powerful tools, such as agent-based simulation systems that are increasingly used in support of computational social science. How can we gain insight regarding the natural system by evaluation of such simulations? Can we explore all types of emergence currently discussed by philosophers as well as engineers, or are there limitations and constraints computational scientists need to be aware of?

The goal of this book is to provide an overview of the current discussions on complexity and emergence, and how systems engineering methods in general and simulation methods in particular can help in gaining new insight and support users of complex systems in providing better governance. The book is organized into 16 invited chapters in four sections, providing an overview of philosophical, model engineering, computational methods using simulation, and research specific viewpoints.

The topics addressed in the chapters reflect the different viewpoints on emergence and discuss why we should not rule it out, whether complex systems can be engineered, whether all complex systems can be reduced to complicated systems if we increase our knowledge, how simulation can help to better understand and manage emergence, and what role can system thinking play in understanding emergence? The authors provide a wide variety of approaches to studying emergence ranging from formal system specification that account for emergence, deriving factors from observations of emergence in physics and chemistry, the emergence of language between two hominid agents in a resource-constrained system, and looking at emergence in complex enterprises. The editors conclude the book with observations on a possible research agenda to address some of the grand challenges the complex systems engineering community must consider.

This book is a diverse collection of contributions from a broad background of recognized experts in their field highlighting aspects of complexity and emergence important from their viewpoint. By bringing them together in one compendium, we hope to spawn a discussion on new methods and tools needed to address the challenges of complexity that obviously go beyond the limits of traditional approaches.

Saurabh Mittal, Herndon, VA

Saikou Diallo, Suffolk, VA

Andreas Tolk, Hampton, VA

September 2017

ABOUT THE EDITORS

Saurabh Mittal is the lead systems engineer/scientist in the Modeling, Simulation, Experimentation, and Analytics (MSEA) Technical Center of the MITRE Corporation. He is also affiliated with Dunip Technologies, LLC, and Society of Computer Simulation (SCS) International. He currently serves as associate editor-in-chief for Transactions of SCS and editor-in-chief of Enterprise Architecture Body of Knowledge (EABOK) Consortium. He received his M.S. and Ph.D. degrees in electrical and computer engineering from the University of Arizona. Previously, he was a scientist and architect at National Renewable Energy Laboratory, Department of Energy at Golden, Colorado, where he contributed to complex energy systems and co-simulation environments. He also worked at L3 Link Simulation & Training at 711 HPW, US Air Force Research Lab at Wright-Patterson Air Force Base, Ohio, where he contributed to integrating artificial agents and various cognitive architectures in Live, Virtual and Constructive (LVC) environments using formal systems theoretical model-based engineering approaches. He was a research assistant professor at the Department of Electrical and Computer Engineering at the University of Arizona. Dr. Mittal served as general chair of Springsim'17 and SummerSim'15, vice general chair for SpringSim'16 and SummerSim'14, and program chair for SpringSim'15. He is the founding chair for M&S and Complexity in Intelligent, Adaptive and Autonomous (MSCIAAS) Symposium offered in Springsim, Summersim, and Winter Simulation Conferences. He is a recipient of US Department of Defense (DoD) highest civilian contraction recognition: Golden Eagle award (2006) and SCS's Outstanding Service (2016) and Professional Contribution (2017) award. He has coauthored over 80 articles in various international conferences and journals, including books titled “Netcentric System of Systems Engineering with DEVS Unified Process” and “Guide to Simulation-based disciplines: Advancing our computational future” that serves the areas of executable architectures; service-oriented distributed simulation; formal Systems M&S; system of systems engineering; multiplatform modeling; intelligence-based, complex, adaptive, and autonomous systems; and large-scale M&S integration and interoperability.

Saikou Diallo is a research associate professor at the Virginia Modeling, Analysis and Simulation Center, and an adjunct professor at Old Dominion University. Dr. Diallo has studied the concepts of interoperability of simulations and composability of models for over 15 years. He is VMASC's lead researcher in Simulated Empathy where he focuses on applying modeling and simulation to study how people connect with one another and experience their environment and creations. He currently has a grant to conduct research into modeling religion, culture, and civilizations. He is also involved in developing cloud-based simulation engines and user interfaces in order to promote the use of simulation outside of traditional engineering fields. Dr. Diallo graduated with a M.S. degree in engineering in 2006 and a Ph.D. in modeling and simulation in 2010 both from Old Dominion University. He is the vice president in charge of conferences and a member of the Board of Directors for the Society for Modeling and Simulation International (SCS). Dr. Diallo has over one hundred publications in peer-reviewed conferences, journals, and books.

Andreas Tolk is technology integrator in the Modeling, Simulation, Experimentation, and Analytics (MSEA) Technical Center of the MITRE Corporation. He is also adjunct full professor of engineering management and systems engineering and modeling, simulation, and visualization engineering at Old Dominion University in Norfolk, Virginia. He holds an M.S. and a Ph.D. degree in computer science from the University of the Federal Armed Forces in Munich, Germany. He published more than 200 contributions to journals, book chapters, and conference proceedings and edited several books on Modeling & Simulation and Systems Engineering. He received the Excellence in Research Award from the Frank Batten College of Engineering and Technology in 2008, the Technical Merit Award from the Simulation Interoperability Standards Organization (SISO) in 2010, and the Outstanding Professional Contributions Award from the Society for Modeling and Simulation (SCS) in 2012, and the Distinguished Achievement Award from SCS in 2014. He is a fellow of SCS and a senior member of ACM and IEEE.

LIST OF CONTRIBUTORS

Lachlan Birdsey
School of Computer Science
The University of Adelaide
Adelaide, SA 5005
Australia

Chih-Chun Chen
Department of Engineering
University of Cambridge
Cambridge CB2 1PZ
UK

Steven Corns
Department of Engineering Management and Systems Engineering
Missouri University of Science and Technology
Rolla, MO 65401
USA

Nathan Crilly
Department of Engineering
University of Cambridge
Cambridge CB2 1PZ
UK

Saikou Diallo
Virginia Modeling, Analysis & Simulation Center
Old Dominion University
Suffolk, VA
USA

Umut Durak
German Aerospace Center
Cologne
Germany

David C. Earnest
Department of Political Science
University of South Dakota
Vermillion, SD 57069
USA

Erika Frydenlund
Virginia Modeling, Analysis and Simulation Center
Old Dominion University
Suffolk, VA 23435
USA

Ross Gore
Virginia Modeling, Analysis and Simulation Center
Old Dominion University
Norfolk, VA 23529
USA

John J. Johnson IV
Systems Thinking & Solutions
Ashburn, VA 20148
USA

Matthew T.K. Koehler
The MITRE Corporation
Bedford, MA
USA

Justin E. Lane
Institute of Cognitive and Evolutionary Anthropology
Department of Anthropology
University of Oxford
64 Banbury Road, Oxford OX2 6PN
UK

LEVYNA, Ústav religionistiky
Masaryk University
Veveří 28, Brno 602 00
Czech Republic

Suzanna Long
Department of Engineering Management and Systems Engineering
Missouri University of Science and Technology
Rolla, MO 65401
USA

Saurabh Mittal
The MITRE Corporation
McLean, VA
USA

Michael D. Norman
The MITRE Corporation
Bedford, MA
USA

Akhilesh Ojha
Department of Engineering Management and Systems Engineering
Missouri University of Science and Technology
Rolla, MO 65401
USA

Tuncer Ören
School of Electrical Engineering and Computer Science
University of Ottawa
Ottawa
Canada

Jose J. Padilla
Virginia Modeling Analysis and Simulation Center
Old Dominion University
Suffolk, VA
USA

Robert Pitsko
The MITRE Corporation
McLean, VA
USA

Ruwen Qin
Department of Engineering Management and Systems Engineering
Missouri University of Science and Technology
Rolla, MO 65401
USA

William B. Rouse
Center for Complex Systems and Enterprises
Stevens Institute of Technology
1 Castle Point Terrace, Hoboken, NJ 07030
USA

Tom Shoberg
U.S. Geological Survey
CEGIS
Rolla, MO 65409
USA

F. LeRon Shults
Institute for Religion, Philosophy and History
University of Agder
Kristiansand 4604
Norway

Andres Sousa-Poza
Engineering Management & System Engineering
Old Dominion University
Norfolk, VA 23529
USA

John Symons
Department of Philosophy
The University of Kansas
Lawrence, KS 66045
USA

Claudia Szabo
School of Computer Science
The University of Adelaide
Adelaide, SA 5005
Australia

Andreas Tolk
The MITRE Corporation
Hampton, VA
USA

Wesley J. Wildman
School of Theology
Boston University
Boston, MA 02215
USA

Center for Mind and Culture
Boston, MA 02215
USA

Levent Yilmaz
Department of Computer Science and Software Engineering, Samuel Ginn College of Engineering
Auburn University
Auburn, AL 36849
USA

Bernard P. Zeigler
RTSync Corporation
University of Arizona
Tucson, AZ
USA

EMERGENT BEHAVIOR IN COMPLEX SYSTEMS ENGINEERING

A Modeling and Simulation Approach

DEDICATION

FOREWORD

PREFACE

ABOUT THE EDITORS

LIST OF CONTRIBUTORS

SECTION I

EMERGENT BEHAVIOR IN COMPLEX SYSTEMS