113,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Stevens Institute Series on Complex Systems and Enterprises
- Sprache: Englisch
A comprehensive text that reviews the methods and technologies that explore emergent behavior in complex systems engineering in multidisciplinary fields In Emergent Behavior in Complex Systems Engineering, the authors present the theoretical considerations and the tools required to enable the study of emergent behaviors in manmade systems. Information Technology is key to today's modern world. Scientific theories introduced in the last five decades can now be realized with the latest computational infrastructure. Modeling and simulation, along with Big Data technologies are at the forefront of such exploration and investigation. The text offers a number of simulation-based methods, technologies, and approaches that are designed to encourage the reader to incorporate simulation technologies to further their understanding of emergent behavior in complex systems. The authors present a resource for those designing, developing, managing, operating, and maintaining systems, including system of systems. The guide is designed to help better detect, analyse, understand, and manage the emergent behaviour inherent in complex systems engineering in order to reap the benefits of innovations and avoid the dangers of unforeseen consequences. This vital resource: * Presents coverage of a wide range of simulation technologies * Explores the subject of emergence through the lens of Modeling and Simulation (M&S) * Offers contributions from authors at the forefront of various related disciplines such as philosophy, science, engineering, sociology, and economics * Contains information on the next generation of complex systems engineering Written for researchers, lecturers, and students, Emergent Behavior in Complex Systems Engineering provides an overview of the current discussions on complexity and emergence, and shows how systems engineering methods in general and simulation methods in particular can help in gaining new insights in complex systems engineering.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 764
Veröffentlichungsjahr: 2018
Ähnliche
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
SUMMARY
INTRODUCTION
TO EXPLAIN IS NOT TO ELIMINATE
EMERGENT PROPERTIES AND MORE FUNDAMENTAL PROPERTIES
WHERE DOES THE PHILOSOPHICAL PROBLEM OF EMERGENCE COME FROM?
INCOMPLETENESS
MODELS, EMERGENCE, AND FUNDAMENTALITY
ACKNOWLEDGMENTS
REFERENCES
CHAPTER 2: EMERGENCE: WHAT DOES IT MEAN AND HOW IS IT RELEVANT TO COMPUTER ENGINEERING?
SUMMARY
INTRODUCTION
MAPPING MEANINGS OF EMERGENCE
EMERGENCE IN COMPUTER ENGINEERING
CONCLUSION: FUTURE DIRECTIONS
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
SUMMARY
INTRODUCTION
BACKGROUND
EMERGENCE OF LANGUAGE CAPABILITIES IN HUMAN EVOLUTION
FUNDAMENTAL SYSTEMS APPROACH FOR RCIDS FOR EMERGENCE OF LANGUAGE EXAMPLE
PERSPECTIVES: EVOLUTION AND SYSTEM MODELING
RCIDS AND ACTIVITY-BASED PERSPECTIVE ON SHARED ATTENTION
CONCLUSIONS AND FUTURE WORK
DISCLAIMER
REFERENCES
CHAPTER 4: GENERATIVE PARALLAX SIMULATION: CREATIVE COGNITION MODELS OF EMERGENCE FOR SIMULATION-DRIVEN MODEL DISCOVERY
SUMMARY
INTRODUCTION
BACKGROUND
MODEL DISCOVERY AND EMERGENCE AS A CREATIVE COGNITION PROCESS
GENERATIVE PARALLAX SIMULATION: BASIC CONCEPTS
A REFERENCE ARCHITECTURE FOR MODEL ENSEMBLES AS COGNITIVE AGENTS
MODEL EMERGENCE VIA REFLECTIVE EQUILIBRIUM
CONCLUSIONS
REFERENCES
Section II: Emergent Behavior Modeling in Complex Systems Engineering
CHAPTER 5: COMPLEX SYSTEMS ENGINEERING AND THE CHALLENGE OF EMERGENCE
SUMMARY
INTRODUCTION
SYSTEMS ENGINEERING AND EMERGENCE
UNDERSTANDING AND MANAGING EMERGENCE
SUMMARY AND DISCUSSION
DISCLAIMER
REFERENCES
CHAPTER 6: EMERGENCE IN COMPLEX ENTERPRISES
SUMMARY
INTRODUCTION
COMPLEX SYSTEMS
MULTI-LEVEL MODELS
EMERGENCE IN SOCIETY
EMERGENCE IN CITIES
EMERGENCE IN INSTITUTIONS
EMERGENCE IN COMPANIES
ROLE OF MODELING AND SIMULATION
CONCLUSIONS
REFERENCES
CHAPTER 7: EMERGENCE IN INFORMATION ECONOMIES: AN AGENT-BASED MODELING PERSPECTIVE
INTRODUCTION
EMERGENCE IN THE SOCIAL SCIENCES
INFORMATION ECONOMIES AND COMPUTATIONAL SOCIAL SCIENCE
WHY AGENT-BASED MODELING?
MODEL OVERVIEW
DETAILS
EXPERIMENT AND FINDINGS
CONCLUSIONS
REFERENCES
CHAPTER 8: MODELING EMERGENCE IN SYSTEMS OF SYSTEMS USING THERMODYNAMIC CONCEPTS
SUMMARY
INTRODUCTION
THERMODYNAMICS AND ITS EXPLANATORY ADVANTAGES
EMERGENCE AND CHEMICAL REACTIONS
DEVELOPING A CONCEPTUAL MODEL OF EMERGENCE IN SOS
POTENTIAL IMPLICATION
CONCLUSIONS
RECOMMENDATIONS AND FUTURE WORK
REFERENCES
CHAPTER 9: INDUCED EMERGENCE IN COMPUTATIONAL SOCIAL SYSTEMS ENGINEERING: MULTIMODELS AND DYNAMIC COUPLINGS AS METHODOLOGICAL BASIS
SUMMARY
INTRODUCTION
COMPUTATIONAL SOCIAL SYSTEMS ENGINEERING AND INDUCED EMERGENCE
MULTIMODELS
MODEL COUPLING
VARIABLE STRUCTURE MODELS
INDUCED EMERGENCE IN COMPUTATIONAL SOCIAL SYSTEMS ENGINEERING: ROLES OF MULTIMODELS
BEYOND AGENT-BASED FOR MULTIMODELING AND SIMULATING EMERGENCE
CONCLUSIONS AND FUTURE STUDIES
DISCLAIMER
REFERENCES
CHAPTER 10: APPLIED COMPLEXITY SCIENCE: ENABLING EMERGENCE THROUGH HEURISTICS AND SIMULATIONS
INTRODUCTION – A COARSE-GRAINED LOOK
DEFINITIONS AND A TAXONOMY FOR APPLIED COMPLEXITY SCIENCE
HEURISTICS FOR APPLYING COMPLEXITY SCIENCE TO ENGINEER FOR EMERGENCE
UNMANNED AUTONOMOUS VEHICLE (UV) SWARMS
OPERATIONAL UV SWARMS
DISCUSSION AND CONCLUSIONS
DISCLAIMER
REFERENCES
Section III: Engineering Emergent Behavior in Computational Environments
CHAPTER 11: TOWARD THE AUTOMATED DETECTION OF EMERGENT BEHAVIOR
INTRODUCTION
OVERVIEW OF EXISTING WORK
IDENTIFYING EMERGENCE USING INTERACTION GRAPHS
EXPERIMENTAL ANALYSIS
CONCLUSION
References
CHAPTER 12: ISOLATING THE CAUSES OF EMERGENT FAILURES IN COMPUTER SOFTWARE
SUMMARY
INTRODUCTION
ISOLATING THE CAUSE OF DETERMINISTIC FAILURE
EMERGENT FAILURES
STOCHASTICS IN SOFTWARE
FAULTS ACTIVATED BY EFFECTS OF THE INTERNAL SYSTEM ENVIRONMENT
SUMMARY
REFERENCES
CHAPTER 13: FROM MODULARITY TO COMPLEXITY: A CROSS-DISCIPLINARY FRAMEWORK FOR CHARACTERIZING SYSTEMS
SUMMARY
INTRODUCTION
CHARACTERIZING SYSTEMS
ASPECTS AND ABSTRACTIONS OF MODULARITY
ASPECTS OF COMPLEXITY
CONCLUSIONS
REFERENCES
Chapter 14: THE EMERGENCE OF SOCIAL SCHEMAS AND LOSSY CONCEPTUAL INFORMATION NETWORKS: HOW INFORMATION TRANSMISSION CAN LEAD TO THE APPARENT “EMERGENCE” OF CULTURE
SUMMARY
INTRODUCTION
CONSENSUS IN LARGE HUMAN SOCIAL GROUPS
OVERVIEW OF MODEL
TESTING THE MODEL
RESULTS
EMERGENCE OF SOCIAL CONSENSUS IN THE IIS
CONCLUSION: EMERGENCE, WHAT IS CLAIMED, AND WHAT SHOULD NEVER BE
REFERENCES
CHAPTER 15: MODELING AND SIMULATION OF EMERGENT BEHAVIOR IN TRANSPORTATION INFRASTRUCTURE RESTORATION
SUMMARY
INTRODUCTION
SYSTEM DYNAMICS APPROACH
METHODOLOGY
ILLUSTRATIVE EXAMPLE
CONCLUSION AND FUTURE WORK
REFERENCES
Section IV: Research Agenda
CHAPTER 16: RESEARCH AGENDA FOR NEXT-GENERATION COMPLEX SYSTEMS ENGINEERING
SUMMARY
ON ENGINEERING EMERGENCE: LEARNING FROM OTHERS
MAKING SENSE OF EMERGENT BEHAVIOR: A MIXED METHOD APPROACH
GRAND CHALLENGES OF EMERGENCE RESEARCH: A RESEARCH AGENDA
DISCLAIMER
REFERENCES
Index
End User License Agreement
Pages
x
xi
xii
xiii
xiv
15
xvi
xvii
16
17
18
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
391
392
393
394
395
Guide
Cover
Table of Contents
Preface
Begin Reading
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 and . (a) Flock of birds: with minimum HD: 109.77. (b) Flock of birds: reference graph:
Figure 11.3 Game of life with pulsar pattern.
Figure 11.5 Comparison between and . (a) Flock of birds: with minimum HD: 86.84. (b) Flock of birds: reference graph: .
Figure 11.6 Comparison between and . (a) Predator-Prey: with minimum HD: 5.657. (b) Predator-Prey: reference graph: .
Figure 11.7 Comparison between Flock of Birds , and . (a) Flock of Birds: with minimum HDA: 3.58. (b) Flock of Birds: reference graph: . (c) Flock of Birds: with minimum HDA: 72.35.
Figure 11.8 IG with minimum HDA: 43.06.
Figure 11.9 Comparison between , and . (a) with closest to 0: 0.53. (b) with closest to 0: 0.003. (c) Flock of Birds: reference graph: .
Figure 11.10 IG for two bird types with closest to : 0.07.
Figure 11.11 Game of life: with closest to 0: 0.000.
Figure 11.12 Best results from HD, HDA, and when compared to the reference graph . (a) Reference graph: . (b) HD: . (c) HDA: . (d) : .
Figure 11.13 Best results from for Game of Life models with different agent amounts when compared to the reference graph . (a) Reference graph: . (b) : IG. (c) : .
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 : 20 Birds,
Table 11.3 : 50 Birds,
Table 11.4 : 4 Predators, 8 Prey, Grid,
Table 11.5 : 8 Predators, 8 Prey, grid,
Table 11.6 : 20 Birds,
Table 11.7 : 4 Predators, 8 Prey, Grid,
Table 11.8 : 50 Birds,
Table 11.9 : 50 Birds,
Table 11.10 : Cells, ; Random Initialization
Table 11.11 : Cells, ; Glider Pattern
Table 11.12 : Cells, ; Pulsar Pattern
Table 11.13 : Cells, ; Pulsar Pattern
Table 11.14 : 4 Predators, 8 Prey, Grid,
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
EMERGENT BEHAVIOR IN COMPLEX SYSTEMS ENGINEERING
A Modeling and Simulation Approach
Edited by
SAURABH MITTAL
SAIKOU DIALLO
ANDREAS TOLK
This edition first published 2018
© 2018 John Wiley & Sons, Inc
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of Saurabh Mittal, Saikou Diallo, and Andreas Tolk to be identified as the editors of the editorial material in this work has been asserted in accordance with law.
Registered Office
John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA
Editorial Office
111 River Street, Hoboken, NJ 07030, USA
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
Wiley also publishes its books in a variety of electronic formats and by print-on-demand. Some content that appears in standard print versions of this book may not be available in other formats.
Limit of Liability/Disclaimer of Warranty
In view of ongoing research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of experimental reagents, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each chemical, piece of equipment, reagent, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
Library of Congress Cataloging-in-Publication Data
Names: Mittal, Saurabh, editor. | Diallo, Saikou Y., editor. | Tolk, Andreas,
editor.
Title: Emergent behavior in complex systems engineering : a modeling and
simulation approach / edited By Saurabh Mittal, Saikou Diallo, Andreas
Tolk.
Description: 1st edition. | Hoboken, NJ : John Wiley & Sons, 2018. | Series:
Stevens Institute series on complex systems and enterprises | Includes
bibliographical references and index. |
Identifiers: LCCN 2017052148 (print) | LCCN 2018000725 (ebook) | ISBN
9781119378853 (pdf) | ISBN 9781119378938 (epub) | ISBN 9781119378860
(cloth : alk. paper)
Subjects: LCSH: Systems engineering. | System design.
Classification: LCC TA168 (ebook) | LCC TA168 .E53 2018 (print) | DDC
620.001/1--dc23
LC record available at https://lccn.loc.gov/2017052148
Cover Design: Wiley
Cover Images: (Background image) © archibald1221/Gettyimages; (Sphere) © v_alex/Gettyimages
DEDICATION
To my parents, all my teachers, and mentors, who lit the path for me,
To my wife and kids, my loving companions in this fulfilling journey,
To the Almighty, who widens my horizons and fill my heart with joy abound
Saurabh Mittal
To all who have contributed a pebble to my foundation, teachers, and family and especially my wife Faby and my son EJA, whose teachings are as entertaining and they are enlightening
Saikou Diallo
To all my mentors who helped me to become who I am, and all my colleagues who continue what I tried to start!
Andreas Tolk
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
and
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
and
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
SECTION I
EMERGENT BEHAVIOR IN COMPLEX SYSTEMS
Chapter 1METAPHYSICAL AND SCIENTIFIC ACCOUNTS OF EMERGENCE: VARIETIES OF FUNDAMENTALITY AND THEORETICAL COMPLETENESS
John Symons
Department of Philosophy, The University of Kansas, Lawrence, KS, 66045, USA
SUMMARY
The concept of emergence figures prominently in contemporary science. It has roots in philosophical reflection on the nature of fundamentality and novelty that took place in the early decades of the twentieth century. Although it is no longer necessary to offer philosophical defenses of the science of emergent properties, attention to basic metaphysical questions remains important for engineering and scientific purposes. Most importantly, this chapter argues for precision with respect to what scientists and engineers take to count as fundamental for the sake of their uses of the concept of emergence.
INTRODUCTION
Two defining characteristics, novelty and naturalness, mark the concept of emergence. When emergent properties are first instantiated, they are said to be novel in some difficult to specify, but presumably nontrivial, sense. Although every moment of natural history is new in the sense of being at least at a different time from what came before, the kind of novelty that is associated with emergent properties is understood to constitute a metaphysically significant difference. What might that significance amount to? Very roughly, we can say that if an emergent property appears, there is a new kind of entity or property on the scene. Not just more of the same. To claim that a property, say for example a property like transparency, liquidity, or consciousness, is emergent is to make a judgment about the way it relates to more fundamental features of the world. The emergent property or entity differs in kind from that which preexisted it or is more fundamental than it. The first task of this chapter is to explore what it might mean for emergent properties to relate or fail to be related to more fundamental properties.
The discussion of emergent properties in scientific and philosophical research has emphasized discontinuities and differences between the emergent property and the prior or more fundamental properties from which it arises. However, emergent properties are not just discontinuous with what came before. They are also thought to be part of the natural order in some intelligible sense. According to most contemporary proponents, emergent properties are not unnaturally or supernaturally new (their appearance is not miraculous) but instead can be understood scientifically insofar as they are intelligibly connected with parts of the natural world and in particular with other properties that are prior or more fundamental.
The scientific problem of emergence involves understanding the relations between the emergent property and the more fundamental or prior properties. The practical payoff of this understanding is improved levels of prediction and control over those emergent properties and entities that concern us most.
TO EXPLAIN IS NOT TO ELIMINATE
How could there be an intelligible connection between metaphysically distinct kinds? In one sense, this is a question only a philosopher would bother asking. There are plenty of simple examples. Take Putnam's (1975, 295–298) famous example of the explanation for why a square peg fails to pass through a round hole. The rigidity of the pegs and the rigidity of the walls of the holes are dependent on their physical structure. However, the property of being able to pass through a hole of a particular size and shape is a different kind of property than the properties governing the physical constituents of the peg. Geometrical facts about the sizes of the cross section of the peg and the hole are sufficient to explain the facts about the pegs being able to pass through. An attempt to account for this higher level property in terms of the physics governing the particles in the peg would result in an unexplanatory, albeit a true and very long, description of the particular case. The geometrical explanation, by contrast is simple, provides clear guidance for interaction in the system and generalizes beyond this particular peg and hole to all pegs, all holes, and beyond.
The geometrical explanation explains many things at various scales, including why we have round manhole covers rather than square ones. Manhole covers have the property of not falling dangerously on people working in the sewers below because of the circular (rather than, say, rectangular) shape of the covers. This is one example of how we can intelligibly connect distinct kinds of properties. The microphysical properties of this particular peg, its particular material instantiation, can be connected with the macro-level property of passing through this particular hole via a geometrical explanation. That geometrical explanation has the virtue of being applicable beyond this particular case. The property of being a hole, being able to pass through, having a particular stable shape in space, having the particular microphysics that this peg has, and so on, are connected in the explanation in a way that satisfies our demand for explanation in this context perfectly.
Putnam intended this to be an example of a non-reductive explanation, as, he thought, the material constitution of the peg is almost completely irrelevant to the explanation of its fitting or failing to fit. His use of this example was meant to indicate the role of explanations that are not simply descriptions of physical microstates of systems. There is more going on in nature, he argued, than merely the microphysical.
Philosophers in the 1960s and 1970s were very concerned with the distinction between what they saw as reductive and non-reductive explanation. They fixated on the distinction between reductionist and anti-reductionist explanations because of their concern for the ontological implications of explanations. For some, the threat of reductionism is that we are encouraged to believe that one kind of object simply does not exist insofar as it can be described in terms of some more basic kind of object. This is an ontological concern. Notice that it involves a judgment that is independent of the process of explanation: We might decide that the existence of certain kinds of explanation license ontologies with fewer things in them. Thus, given the fact that we can explain traffic jams on the highway in terms of the interactions of individual vehicles, we might be tempted to draw the ontological conclusion that there is no traffic jam. Notice that if one decided to take this strategy with respect to one's ontology, it is a step beyond what the explanation of the traffic jam by itself tells us. In fact, I would argue, one needs to justify the step from a successful reductive scientific explanation to the claim that because of this successful explanation we can therefore eliminate the thing that has been explained from our ontology. Furthermore, in paradigmatically reductionist explanations, we see examples of intelligible relations being discovered between distinct kinds of properties. For example, subatomic particles are not the kinds of things that have properties like rigidity or wetness. A structural explanation of the subatomic constituents of a diamond goes some way toward explaining why the diamond in the engagement ring is rigid. There is an intelligible relation between the macro-properties of the diamond and the micro-properties of its constituents that adverts to the structure of the diamond crystal. Properties like hardness or rigidity are manifest only on some scales and result from interactions of large numbers of molecules. There is simply no non-relational explanation of why diamond molecules give rise to hardness. These relations, like the geometrical properties of Putnam's pegs, are not built into their relata.
The concern among philosophers is inspired by the concern that giving an explanation is equivalent to explaining away. Philosophers sometimes argue, following Carnap and Quine, that “explication is elimination” in natural science as well as in mathematics. This is due to a mistaken conflation of kinds of explanations and the diverse theoretical goals motivating explanatory projects. Quine's arguments concerning eliminativism were drawn from purely mathematical contexts. He was moved, primarily by his understanding of the history of analysis in nineteenth century mathematics. The infinitesimal is a puzzling artifact of early calculus that (according to popular opinion) we no longer need to include in lessons to high school students thanks to the work of Weierstrass, Dedekind, and Cantor. As the story goes, Weierstrass gave us the means to eliminate the infinitesimal, Dedekind and Cantor helped to finish the job. Quine strongly approved of this story and built his account of explication as elimination upon it.1 He proposed a view that began by individuating metaphysically puzzling notions in mathematics, like the infinitesimal or the ordered pair, via the mathematical roles that they play. Insofar, as they are “prima facie useful to theory and, at the same time, troublesome,” Quine recommended that we simply find other ways to perform their theoretical role. Once we find these other ways, we can stop worrying about those concepts. Like the infinitesimal, they are eliminated (1960a, 266).
The explanatory project that motivates complexity science or other studies of emergent properties is not the same as that which motivated Quine's approach to philosophical analysis. For Quine, the method of philosophical analysis is to “fix on the particular functions of the unclear expression that make is worth troubling about, and then devise a substitute, clear, and couched in terms of our liking that fills those functions” (1960b, 258–259). By contrast, the goal of research in the natural sciences is the discovery of novel objects and relations in the world. The explanatory goal is understanding rather than the rearticulation, in more parsimonious terms, of functions that make the phenomenon worth troubling about.
In scientific and engineering research more generally, this kind of elimination is simply not a goal. Insofar as things like traffic jams or epidemics are troublesome, that trouble is not eliminated by defining ways that other, less troublesome things, cause delays and illness. A traffic jam or an epidemic is not
