The study of complex systems has attracted a broad range of researchers from many disciplines spanning both the hard and soft sciences. In the Autumn of 1997, 300 of these researchers came together for the First International Conference on Complex Systems. The proceedings of this conference is the first book in the New England Complex Systems Institute Series on Complexity and includes more than 100 presentations and papers on topics like evolution, emergence, complexity, self-organization, scaling, informatics, time series, emergence of mind, and engineering of complex systems.
Introduction | p. xvii |
"Significant points" in the study of complex systems | p. xxi |
Organization and Program | p. xxv |
Transcripts | p. 1 |
Can there be a science of complex systems? | p. 3 |
General systems theory? | p. 4 |
Some principles of complex system design | p. 5 |
Organizations and markets | p. 9 |
Conclusion | p. 13 |
Evolution | p. 15 |
Selection and production | p. 16 |
Variation | p. 19 |
Psychology and corporations: A complex systems perspective | p. 27 |
Genome complexity (Session introduction: Emergence) | p. 29 |
Emergent properties and behavior of the atmosphere | p. 33 |
Systems properties of metabolic networks | p. 43 |
A hypothesis about hierarchies | p. 45 |
Session introduction: Informatics | p. 53 |
Whole genome bioinformatics | p. 55 |
Session introduction: Computational methods | p. 69 |
Papers | p. 73 |
Theories in (inter) action: A complex dynamic system for theory evaluation in Science Studies | p. 75 |
Modeling fractal patterns with Genetic Algorithm solutions to a variant of the inverse problem for Iterated Function Systems (IFS) | p. 85 |
Introduction | p. 86 |
Encoding the IFS on a GA | p. 88 |
The GA search | p. 91 |
Applications | p. 97 |
Conclusions | p. 99 |
An artificial life model for investigating the evolution of modularity | p. 103 |
Introduction | p. 104 |
The model | p. 105 |
Preliminary results | p. 108 |
Conclusions | p. 109 |
From inductive inference to the fundamental equation of measurement | p. 115 |
Introduction | p. 115 |
The evolution of a model during learning | p. 116 |
Shannon entropy | p. 119 |
Conclusion | p. 121 |
Controlling chaos in systems of coupled maps with long-range interactions | p. 123 |
Introduction | p. 124 |
Model and results | p. 124 |
Discussion | p. 129 |
Assessing software organizations from a complex systems perspective | p. 133 |
Introduction | p. 134 |
The software process and its evaluation | p. 134 |
A metaphor for the software process: Morphogenesis | p. 136 |
Conclusion | p. 139 |
Hazards, self-organization, and risk compensation: A view of life at the edge | p. 143 |
Introduction | p. 144 |
Self-organized criticality | p. 145 |
Risk compensation | p. 147 |
Hazards and the balancing act | p. 148 |
Statistics and indicators | p. 150 |
Multifactor disasters | p. 151 |
Risk compensation and progress | p. 151 |
Summary | p. 151 |
Structure formation by Active Brownian particles with nonlinear friction | p. 153 |
Introduction | p. 153 |
Self-moving particles | p. 154 |
Systems properties of metabolic networks | p. 163 |
Introduction | p. 164 |
Steady state of a metabolic network | p. 168 |
Metabolic control analysis | p. 170 |
Feedback regulation | p. 172 |
Large changes in metabolic rate | p. 174 |
Hierarchical organisation of metabolism | p. 174 |
Complex dynamics of molecular evolutionary processes | p. 179 |
Introduction | p. 180 |
Biomolecules | p. 181 |
Evolutionary dynamics | p. 184 |
Catalytic reaction networks | p. 188 |
Conclusion | p. 193 |
Genetic network inference | p. 199 |
Introduction | p. 200 |
Methods | p. 200 |
Results | p. 201 |
Discussion | p. 204 |
Abbreviations | p. 206 |
Socioeconomic systems as complex self-organizing adaptive holarchies: The dynamic exergy budget | p. 209 |
Introduction | p. 210 |
Efficiency and adaptability (hypercyclic and purely dissipative compartment) | p. 212 |
The dynamic exergy budget | p. 214 |
The scale issue: Environmental loading and need for adaptability | p. 218 |
Conclusion | p. 219 |
Socioeconomic systems as nested dissipative adaptive systems (holarchies) and their dynamic energy budget: Validation of the approach | p. 223 |
Setting up the data base | p. 224 |
BEP as an indicator of development for socioeconomic systems | p. 226 |
Existence of an internal set of constraints on the evolutionary pattern of socio-economic systems | p. 228 |
Establishing links across levels to check the feasibility of future scenarios | p. 229 |
The demographic transition as a shift between two metastable equilibrium points of the dynamic energy budget | p. 232 |
Psychology and corporations: A complex systems perspective | p. 239 |
Introduction | p. 239 |
Organizations as currently organized | p. 240 |
Using organizations to study complex systems | p. 241 |
Leadership as an emergent phenomenon | p. 243 |
Conclusion: The need for a sufficiently rich complex systems perspective | p. 245 |
Symmetry breaking and the origin of life | p. 249 |
Thermodynamics and dissipative systems | p. 249 |
Statistical mechanics | p. 252 |
Cellular automata | p. 253 |
Complexity and functionality: A search for the where, the when, and the how | p. 259 |
Complexity with an attitude--but which one? | p. 259 |
Reductionism | p. 260 |
In search for new laws | p. 263 |
Where and when and how | p. 264 |
From where to when | p. 265 |
From where and when to how | p. 266 |
Conclusion and outlook | p. 267 |
Biological design principles that guide self-organization, emergence, and hierarchical assembly: From complexity to tensegrity | p. 269 |
Introduction | p. 270 |
Complexity in living systems | p. 271 |
Cellular tensegrity | p. 271 |
Mechanochemical control of biochemistry and gene expression | p. 275 |
The architecture of life | p. 275 |
The evolution of form | p. 277 |
Conclusion: Simplicity in complexity | p. 278 |
Information transfer between solitary waves in the saturable Schrodinger equation | p. 281 |
Introduction | p. 282 |
Information transfer | p. 283 |
Computational power | p. 283 |
The NLS equation and its solutions | p. 284 |
Information transfer in collisions of NLS solitary waves | p. 285 |
Radiation | p. 288 |
Physical realization | p. 290 |
Conclusions | p. 290 |
An integrated theory of nervous system functioning embracing nativism and constructivism | p. 295 |
Introduction | p. 295 |
Fundamentals of an integrated theory | p. 296 |
The case of language | p. 301 |
Diagrammatic representation of relationships discussed | p. 302 |
Summary | p. 302 |
Toward the physics of "death" | p. 305 |
Introduction | p. 305 |
Death | p. 306 |
Levels of major complexity | p. 307 |
Involution and levels of selection | p. 315 |
Ragnar Frisch at the edge of chaos | p. 319 |
Will capitalism collapse or equilibrate? | p. 319 |
A shared judgement | p. 321 |
Conclusions | p. 324 |
Programming complex systems | p. 325 |
Introduction | p. 325 |
The lambda calculus | p. 327 |
The lambda-p calculus | p. 329 |
The lambda-q calculus | p. 334 |
Simulation to quantum computers | p. 338 |
Conclusion | p. 340 |
Towards the global: Complexity, topology and chaos in modelling, simulation and computation | p. 343 |
Introduction | p. 343 |
Hierarchical efficiency | p. 345 |
Topology induces complexity | p. 346 |
Finite topology | p. 347 |
Economics and politics | p. 348 |
Complexity and chaos | p. 350 |
Consequences | p. 350 |
An effect of scale in a non-additive genetic model | p. 357 |
Introduction | p. 357 |
Methods | p. 358 |
Results | p. 360 |
Discussion | p. 361 |
Parallel computational complexity in statistical physics | p. 365 |
Introduction | p. 365 |
Parallel complexity theory | p. 366 |
Example: Parallel algorithm and dynamic exponent for DLA | p. 368 |
Summary | p. 370 |
Statistical models of mass extinction | p. 373 |
Introduction | p. 373 |
The fossil data | p. 374 |
Models of extinction | p. 377 |
Conclusions | p. 383 |
A dual processing theory of brain and mind: Where is the limited processing capacity coming from? | p. 385 |
Introduction | p. 386 |
Mapping in neural networks | p. 386 |
Oscillations and synchrony in neural firing | p. 387 |
Controlled and automatic processes in the brain | p. 388 |
Is dynamical neural activity responsible for controlled processes? | p. 390 |
Derived hypothesis | p. 391 |
Conclusions | p. 392 |
Evolutionary strategies of optimization and the complexity of fitness landscapes | p. 397 |
Introduction | p. 397 |
Evolutionary strategies | p. 399 |
The density of states | p. 399 |
Examples | p. 403 |
Secondary RNA structures | p. 405 |
Conclusions | p. 408 |
Conformational switching as assembly instructions in self-assembling mechanical systems | p. 411 |
Introduction | p. 412 |
Related work | p. 412 |
A case study | p. 413 |
A formal model | p. 422 |
Summary | p. 431 |
Aggregation and the emergence of social behavior in rat pups modeled by simple rules of individual behavior | p. 433 |
Introduction | p. 434 |
Basic strategy | p. 435 |
Aggregation in autonomous individuals | p. 441 |
The emergence of synchronized social behavior | p. 443 |
Mechanisms of aggregation | p. 448 |
Conclusions | p. 450 |
The role of information in simulated evolution | p. 453 |
Introduction | p. 453 |
The information hierarchy | p. 455 |
The population level | p. 455 |
The individual level | p. 461 |
Discussion | p. 470 |
Emergence of complex ecologies in ECHO | p. 473 |
Motivation and context | p. 473 |
The statistics | p. 474 |
The ECHO model | p. 475 |
Individual ECHO runs | p. 479 |
Conclusion | p. 483 |
Spatial correlations in the contact process: A step toward better ecological models | p. 487 |
Introduction | p. 487 |
Introduction to the contact process | p. 489 |
Simulation details | p. 489 |
Measures of heterogeneity | p. 489 |
Theoretical predictions | p. 492 |
Discussion | p. 499 |
Many to one mappings as a basis for life | p. 503 |
Criteria for life | p. 503 |
The principle of many to one mapping | p. 504 |
Many to one mappings in the origins of life and evolution of complex networks | p. 504 |
Outlook | p. 511 |
Generic mechanisms for hierarchies | p. 513 |
Introduction | p. 513 |
What is 'discrete scale invariance' | p. 514 |
Properties | p. 515 |
Mechanisms leading to DSI and examples | p. 516 |
Emergence in earthquakes | p. 519 |
Introduction | p. 519 |
Role of water and phase transformations | p. 520 |
Consequences and predictions | p. 521 |
Chemical oscillation in symbolic chemical system and its behavioral pattern | p. 523 |
Introduction | p. 523 |
Model | p. 524 |
Classification of behavioral pattern of ARMS | p. 532 |
Condition of cycles emergence | p. 536 |
Related work | p. 537 |
Extinction dynamics in a large ecological system with random interspecies interactions | p. 541 |
Introduction | p. 542 |
Model | p. 543 |
Results | p. 545 |
Estimation of induction time | p. 550 |
Discussion | p. 553 |
Functional differentiation in developmental systems | p. 557 |
Association and dissociation of system elements | p. 557 |
Compatibility model | p. 558 |
What parameters describe functions?--Life on the flow | p. 562 |
Development of a system is a specialization of its elements | p. 564 |
Tuning complexity on randomly occupied lattices | p. 569 |
Introduction | p. 569 |
Diversity and complexity | p. 570 |
Tuning effect and critical probabilities | p. 571 |
Scaling relations | p. 573 |
Conclusion | p. 576 |
Socioeconomic organization on directional resource landscapes | p. 579 |
Introduction and motivation | p. 580 |
Background and methodology | p. 580 |
Spatially distributed agent model | p. 582 |
Results and discussion | p. 589 |
"Continuous time" in Feigenbaum's model | p. 597 |
Introduction | p. 597 |
Expressions with continuous parameter | p. 598 |
The functions IF[subscript lambda] for [lambda] = 2, 4 | p. 600 |
An application to fractals: Mandelbrot set | p. 601 |
Conclusion and possible applications | p. 601 |
Ordering chaos in a neural network with linear feedback | p. 603 |
Introduction | p. 604 |
System and analysis | p. 604 |
Summary and conclusions | p. 608 |
Self-organisation and information-carrying capacity of collectively autocatalytic sets of polymers: Ligation systems | p. 613 |
Introduction | p. 614 |
Dynamics of autocatalysis | p. 614 |
Ligation/cleavage systems | p. 616 |
Conclusion | p. 621 |
Self-dissimilarity: An empirically observable complexity measure | p. 625 |
Introduction | p. 626 |
Self-dissimilarity | p. 628 |
Probabilistic measures of self-dissimilarity | p. 632 |
Discussion | p. 639 |
Complexity and order in chemical and biological systems | p. 645 |
Introduction | p. 645 |
Order | p. 646 |
Structural complexity of point systems | p. 648 |
The simple molecules | p. 648 |
Wing patterns of the butterfly Bicyclus anynana | p. 651 |
Table of Contents provided by Ingram. All Rights Reserved. |
ISBN: 9780813341224
ISBN-10: 0813341221
Series: New England Complex Systems Institute Series on Complexity : Book 1
Audience:
Tertiary; University or College
Format:
Paperback
Language:
English
Number Of Pages: 696
Published: 31st July 2003
Publisher: Taylor & Francis Inc
Country of Publication: US
Dimensions (cm): 22.9 x 15.2
x 3.89
Weight (kg): 1.01
Edition Number: 1