Intelligent Complex Adaptive Systems

381 Pages·2008·3.81 MB·English

by  Ang Yang


Intelligent Complex
Adaptive Systems
Ang Yang
CSIRO Land and Water, Australa
Yn Shan
Medcare Australa
IGI PublIShInG
IGIP
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Intelligent complex adaptive systems / Ang Yang and Yin Shan, editors.
p. cm.
Companion volume to: Applications of complex adaptive systems.
Summary: “This book explores the foundation, history, and theory of intelligent adaptive systems, providing
a fundamental resource on topics such as the emergence of intelligent adaptive systems in social sciences,
biologically inspired artificial social systems, sensory information processing, as well as the conceptual and
methodological issues and approaches to intelligent adaptive systems”--Provided by publisher.
Includes bibliographical references and index.
ISBN-13: 978-1-59904-717-1 (hardcover)
ISBN-13: 978-1-59904-719-5 (ebook)
1. Functionalism (Social sciences) 2. System analysis. 3. Biocomplexity--Simulation methods. 4. Social
systems--Simulation methods. 5. Economics--Methodology. 6. Organizational sociology--Simulation meth-
ods. 7. Modularity (Engineering) 8. Modularity (Psychology) 9. Self-organizaing systems. 10. Adaptive
control systems. I. Yang, Ang. II. Shan, Yin.
HM484.I57 2008
300.1’1--dc22
2007032059
British Cataloguing in Publication Data
A Cataloguing in Publication record for this book is available from the British Library.
All work contributed to this book is new, previously-unpublished material. The views expressed in this book
are those of the authors, but not necessarily of the publisher.

Intelligent Complex
Adaptive Systems
Table of Contents
Foreword.................................................................................................vi
Preface..................................................................................................viii
Section.I
General.Theories
Chapter.I
From.Reductive.to.Robust:.Seeking.the.Core.of.Complex.
Adaptive.Systems.Theory.......................................................................1
Steven E. Wallis, Independent Consultant, USA
Chapter.II
Method.of.Systems.Potential.as.“Top-Bottom”.Technique.of.
the.Complex.Adaptive.Systems.Modelling.........................................26
Grigorii S. Pushnoi, St. Petersburg, Russia
Gordon L. Bonser, California, USA
v
Section.II
Important.Concepts
Chapter.III
Modularity.and.Complex.Adaptive.Systems......................................75
David Conforth, University of NSW, Australia
David G. Green, Monash University, Australia
Chapter.IV
Concept and Definition of Complexity..............................................105
Russell K. Standish, UNSW, Australia
Section.III
Computing.Perspectives
Chapter.V
Emergence.of.Creativity:.A.Simulation.Approach...........................126
Hrafn Thorri Thórisson, Reykjavík University, Iceland
Chapter.VI
Solving.the.Sensory.Information.Bottleneck.to.Central.
Processing.in.Adaptive.Systems.........................................................159
Thomy Nilsson, University of Prince Edward Island, Canada
Chapter.VII
Complexity,.Information,.and.Robustness:.The.Role.of.
Information.“Barriers”.in.Boolean.Networks..................................187
Kurt A. Richardson, ISCE Research, USA
Chapter.VIII
Emergent.Specialization.in.Biologically.Inspired.Collective.
Behavior.Systems................................................................................215
G. S. Nitschke, Vrije Universiteit, The Netherlands
M. C. Schut, Vrije Universiteit, The Netherlands
A. E. Eiben, Vrije Universiteit, The Netherlands
v
Section.IV
Social.Science.Perspectives
Chapter.IX
Emergence.in.Agent-Based.Computational.Social.Science:.
Conceptual,.Formal,.and.Diagrammatic.Analysis...........................255
Jean Louis Dessalles, ENST, Paris, France
Jacques Ferber, LIRMM, CNRS, & University of Montpellier,
France
Denis Phan, GEMAS, CNRS, and University of Paris IV
Sorbonne, & CREM, CNRS, and University of Rennes I,
France
Chapter.X
Ontological Reflections on Peace and War.......................................300
Hayward R. Alker, University of Southern California &
Watson Institute, Brown University, USA
Chapter.XI
The.Allocation.of.Complexity.in.Economic.Systems.......................331
Jason Potts, University of Queensland & Queensland
University of Technology, Australia
Kate Morrison, Volterra Pacific Pty. Ltd., Australia
Joseph Clark, University of Queensland & Suncorp Pty. Ltd.,
Australia
About.the.Contributors......................................................................351
Index.....................................................................................................358
v
Foreword
I don’t believe in the existence of a complex systems theory as such and, so
far, I’m still referring to complex systems science (CSS) in order to describe
my research endeavours. In my view, the latter is constituted, up until now,
by a bundle of loosely connected methods and theories aiming to observe—
from contrasted standpoints—these fascinating objects of research called
complex adaptive systems. Nearly 40 years after Von Bertalanffy’s General
System Theory (1968) and Jacques Monod’s Chance and Necessity (1971),
it is fair to look back and to try to assess how much remains to be said about
these complex adaptive systems. After all, Prigogine’s Order out of Chaos
(1984) already demonstrated that future wasn’t entirely predictable in a his-
tory-contingent world. Nearly at the same period, Maturana and Varela’s
Tree of Knowledge (1987) questioned the closure of biological systems and
proposed a challenging theory of autopoieitic systems, oddly left aside by
CSS’s mainstream research. Later on, Holland’s Hidden Order (1996) set out
the terminology associated with and the characteristics of complex adaptive
systems, still in use nowadays. More recently, Watts’s Six Degrees (2004)
epitomized current assumptions of network theorists asserting that a system’s
structure and organization—most of the time—dictate its functional proper-
ties. What remains from these influential contributions are a heterogeneous
corpus of partly conflicting theories and a disparate set of tools and methods.
Furthermore, too often complex systems science lends itself to criticism when
it trades its artificial complex adaptive systems for natural (i.e., actual) ones.
v
Computer-based simulations, regardless of their expected accuracy, aren’t
the reality, there are just metaphoric representations; “the world as it might
be, not the world as it is” according to Holland himself.
So, yes, much remains to be said about complex adaptive systems (CAS).
Altogether, we need to better our understanding of natural CAS and to im-
prove analytical capacities of artificial CAS. Both aspects need to be dealt
with cautiously in order to avoid ill-fated circularities that have sometimes
characterized research out of in-vitro simulations or artificial society experi-
ments. It is indeed an understated challenge to design a computer metaphor
that describes a given reality independently of the hypothetical processes
to be tested. Flawed designs often result in logical tautologies whereby the
model always verifies the assumptions. Another challenge consists in the
reconciliation between system-wide and individual-centred representations
of CAS. This task is anything but trivial as technical limitations and epis-
temological differences have contributed to the divide. Technically, latest
hybrid simulation platforms provide the means to couple agent-based mod-
elling with dynamical systems modelling or network-oriented simulations.
But epistemological differences on internationality, for example, need to be
dealt with in a same way biology has progressively dealt with the tension
between Lamarckism and Darwinism on evolution.
In this context, the present ouvrage comes at its time. The carefully se-
lected chapters cover the latest theoretical developments on natural CAS
and innovative ways to improve the analytical capacities of artificial CAS.
Traditional concepts of complex systems science are re-visited: what is emer-
gence? Can we explain the emergence of creativity in natural CAS? How
does emergent specialization improve artificial CAS’s design? Likewise,
essential characteristics of social CAS are scrutinized: How do information
flows influence the complexity of social systems? Can we propose a robust
ontological foundation for social simulations? Finally, this book invites us
into an interdisciplinary journey through biological evolution, neo-classical
economics, system thinking and social sciences, using CSS as its Arian’s
thread. Intelligent complex adaptive systems (ICAS) will emerge from this
interdisciplinary cross-fertilization combined with technological advances.
They will provide powerful analytical capacities, supported by a reunified
and holistic vision on complex adaptive systems. They will help us to build
what I will have to call, finally, a complex systems theory.
Pascal Perez
Associate Professor, Research School of Pacific & Asian Studies, Australian
National University
v
Preface
Our world is a large, integrated system of systems. These systems, whether
they are ecological, social, or financial, are complex and constantly adapt to
their environment. Many of them are essential for our very existence. Be-
ing so complex, and because of the intensive interactions among the system
components, they cannot be fully understood by isolating their components or
applying simple cause and effect reasoning. These systems, however, can be
examined by looking for patterns within their behaviour. Intelligent complex
adaptive systems (ICAS) research uses systemic inquiry to build multi-dis-
ciplinary representations of reality to study such complex systems.
Because the use of ICAS is prevalent across a number of disciplines, papers
describing ICAS theory and applications are scattered through different
journals and conference proceedings. It is, therefore, important to have a
book that broadly covers the state-of-art in this highly evolving area. There
has been a strong interest among researchers regarding the publication of
this book. Forty-nine submissions were received. All papers went through
rigid peer review by at least three reviewers and only 23 were accepted for
publication, an acceptance rate of just under 50%. Because of size constraints,
these papers are published two volumes. This book focuses on the theoreti-
cal side of ICAS while its sister book Applications of Intelligent Complex
Adaptive Systems emphasises the techniques and applications. These two
volumes cover a broad spectrum of ICAS research from discussion of general
theory and foundations to more practical studies of ICAS in various artificial
x
and natural systems. It is important to highlight that a significant portion of
contributions come from the social sciences. This will, we believe, provide
readers of these books with extremely valuable diverse views of ICAS, and
also clearly demonstrates the wide applicability of ICAS theories.
Intelligent.Complex.Adaptive.Systems
The study of ICAS draws richly from foundations in several disciplines,
perhaps explaining in part why ICAS research is so active and productive.
These diverse fields that contributed to the formation of ICAS included the
genetic algorithm (Holland, 1975) and cellular automata (Gardner, 1970,
von Neumann, 1966) in computer sciences, evolution and predator-prey
models (Lotka, 1925) in biology, and game theory (von Neumann & Mor-
genstern, 1944) in economics.
Researchers of ICAS are interested in various questions, but these can be
summarised as to how to describe complex systems, and how to describe the
interactions within these systems that give rise to patterns. Thus, although
researchers from different backgrounds may have very different approaches
to the study of ICAS, it is the unique properties of ICAS systems, such as
nonlinearity, emergence, adaptivity and modularity that form the centre of
inquiries. Many of these properties will be thoroughly explored in these two
volumes. It is the complexity of ICAS systems which means that although a
variety of techniques which have been employed to study ICAS, computer
simulations have become important and widely used. These simulations
involve several important computing techniques that may interest readers
of these books.
• Evolutionary computation (EC) is a highly active field of research
inspired by natural evolution. Essentially, EC models the dynamics
of a population of distinctive entities such as chromosomes in genetic
algorithms or programs in genetic programming. Thus, while EC has
been used as a simplified model to study ICAS, it is also an ICAS itself
having wide applicability for solving scientific and engineering prob-
lems.
• Cellular automata (CA), and related techniques such as Boolean net-
works, are common techniques in ICAS. The behaviour of entities that
respond to the environment is defined as rules or other forms. Each