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Key Points:
-
Individual agents
- Interpretation and action is based
on mental models
- Agents can have their own or shared
mental models
- Mental models can change; i.e., learning
is possible
- Interconnections among agents
- Actions by one agent changes the
context for others
- System behavior emerges from the
interaction among agents
- The system can exhibit novel behavior
- The system is non-linear; small inputs
can lead to major outcome swings
- System behavior is fundamentally
unpredictable at the detail level
- Broad-brush prediction of system
behavior is sometimes possible
- Order is an inherent property of
the system, it need not be imposed
In a CAS, agents operate according
to their own internal strategies or mental models (the
technical term is "schemata"). In other words, each agent can have its own "rules"
for how to
respond to things in its environment; each agent can have its own interpretations of events.
These rules and interpretations need not be explicit. They do not even need to be logical when
viewed by another agent. These are clearly characteristics of humans in just about any social
system.
Agents can share mental models, or be totally individualistic. Further, agents can change their
mental models. Because agents can both change themselves and share mental models, a CAS
can learn; it's behavior can adapt over time. Again, we clearly know that human organizations
change over time and are capable of progress.
The behavior of
a CAS emerges from the interaction among the agents. A CAS can, and
usually does, exhibit novel behaviors. Because of the interaction, the behavior of the system is
also non-linear; seemingly small changes can result in major swings in system behavior. If you
reflect on this, you can probably recall many examples of these behaviors in human systems.
We are usually surprised when they happen. However, when we learn to view systems through
the lens of CAS, these behaviors become expected, not surprising.
Because of this novelty and non-linearity,
the detailed behavior of a CAS is fundamentally
unpredictable. It is not a question of better understandings of the agents, better models, or
faster computing; you simply cannot reliably predict the detailed behavior of a CAS through
analysis. You have to let the system run to see what happens. The implications of this are that
we can never hope to predict the detailed behavior of a human system. While this seems
obvious to say, note how often managers and leaders (we ourselves!) act as if we know or can
be sure about how others should act in response to our actions.
Still, despite this lack of detailed predictability, it is often possible to make generally true,
practically useful statements about the behavior of a CAS. For example, while we cannot
predict the exact temperature in Atlanta at 4:49 pm on August 4, we can say that it is pretty
likely that a traveler there will not need a heavy coat. This gives us some hope in human
systems, we just need to be careful not to over-estimate our ability to predict what will happen.
Over-estimation is the usual mistake that we all make; if you have ever been surprised by how
something has turned out, you have fallen into the trap of over- estimation.
Stuart Kauffman, Ilya Prigogine, and others have shown that a CAS is inherently self-
organizing. Order is an inherent property of the system; it does not need to be imposed from
outside. Further, in a CAS, control is dispersed throughout the interactions among agents; a
central controller is not needed. Yet, most of traditional management theory is about how to
establish order and control through the actions of a few people at the top of an organizational
hierarchy. This management instinct, one that we have all learned, may be the biggest factor
holding back progress in our organizations.
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science, common, tradition,
context, societies, humanity, history, life, specialisms.
Summary:
Most complexity groups
operate on this premise, and the various specialisms such as artificial
life, cellular automata, genetic algorithms and neural networks are examples of this approach
to our subject.
These aspects are already covered in our introductions.
Such an approach however tends to fragment our subject matter, and loses the considerable
commonality between all these systemic approaches and similar historical attempts to
understand life, humanity and spirit.
In our essays we will take a much broader interdisciplinary view of complexity, and try to bring
out its wider implications for our behaviour as scientific humans and the way we organise our
societies and our lives.
Escaping from the dualist world of absolute truth, the yes/no logic of the ancient Greeks, has
much in common with the Zen approach to spiritual enlightenment.
In that tradition we enhance the true or false approach common to Western philosophy and
science with the possibilities that a question can be both true and false and that it can be
neither true nor false.
Complexity thinking is a scientific form of such enlightenment and uses context to escape the
paradoxes inherent in these apparently illogical possibilities.
It is one role of complex systems studies to supply that contextual framework, so that we can
better understand how our simplifications can lead to prejudices and bias, to dogmas that fail
to correctly classify the world and force us into actions that are detrimental to our fitness as a
species and to the earth as a whole.
In such systems complex feedback processes force the structure into one of a number of
possible semi-stable states or attractors.
The potential for conflict here is clear, two people or cultures with different views will tend to
avoid compromise, they often cannot even comprehend the worldview of the other party.
Emotional and Body (sensual and kinaesthetic) intelligences complement our Intellectual
skills, and form an essentially parallel or multi-dimensional communication system, interfacing
on many levels to the world around us and actively defining our values.
Studying humanity as a complex system requires us to take into account the interplay
between these intellectual, emotional and bodily sub-systems and to understand how these all
coevolve and balance in the complex environmental context in which we exist.
Whilst it is easy to abstract out single values and concentrate our efforts on improving these
(e.g. animal rights), we must also recognise that such activities can unbalance the whole,
leading to a worst quality of life overall.
Yet throughout history the certainty of one time has been overturned by the discoveries of the
next.
Transcending what we believe today may be the essential step in taking humanity onwards into
a new millenium based upon a new understanding of complex systems.
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complex systems, Browser,
programming, networks, survives, applications, relevant links,
Cellular Automata, emergent properties.
Summary:
These go through processes
of change that are not describable by a single rule nor are
reducible to only one level of explanation, these levels often include features whose emergence
cannot be predicted from their current specifications.
Complex Systems Theory also includes the study of the interactions of the many parts of the
system.
Previously, when studying a subject, researchers tended to use a reductionist approach which
attempted to summarize the dynamics, processes, and change that occurred in terms of
lowest common denominators and the simplest, yet most widely provable and applicable
elegant explanations.
But since the advent of powerful computers which can handle huge amounts of data,
researchers can now study the complexity of factors involved in a subject and see what
insights that complexity yields without simplification or reduction.
Scientists are finding that complexity itself is often characterized by a number of important
characteristics: (II.1) Self-Organization (II.2) Non-Linearity (II.3) Order/Chaos Dynamic (II.4)
Emergent Properties.
From the learning process of analyzing, simulating, and modelling these characteristics of
Complex Systems, a number of unique and thought-provoking computer programming
approaches have emerged: (III.1) Artificial Life (III.2) Genetic Algorithms (III.3) Neural Networks
(III.4) Cellular Automata (III.5) Boolean Networks.
To obtain more information about Complex Systems in general Press: Help & Overview or
Thematic Introductions Tutorials & FAQs Applications Relevant Links Press your Browser
BACK BUTTON to return to this point when or if you wish to.
Scientists are finding that change occurs naturally and automatically in systems in order to
increase efficiency and effectiveness, as long as the systems are complex enough as defined
above.
This change is accomplished by the elements that make up the system when they respond
automatically to feedback from the environment the system inhabits.
Environmental feedback can be seen as providing information about the system's efficiency and
effectiveness.
Elements that survive negative environmental feedback will automatically re-settle themselves,
or re-organize themselves and their interactions in order to better accomplish the system's
goals.
Success at this then assures their continued existence by also protecting or reinforcing the
structures of which the elements are a part.
Such responsiveness occurs even when the elements and system are non-organic,
unintelligent, and unconscious as long as the system is complex as described above.
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Natural Selection: Natural Selection is best known from the theory of evolution that describes
what species succeed and what species become extinct in the battle to survive in the natural
environment.
In that theory, individuals are born with a range of characteristics.
Those characteristics that support survival become more common in the species as a whole
because the individuals that have those characteristics tend to live longer and mate more
successfully, thus spreading the gene for that characteristic more readily.
In contrast, those individuals that have characteristics that do not support survival do not as
readily pass on the gene for that characteristic, because they do not tend to survive as well and
thus are less likely to breed as often.
Such a process can also happen with non-organic complex systems, because naturally in the
course of development in all complex systems (organic or non-organic) those processes that
work will be reinforced or occur again, or be protected or strengthened somehow (because the
structures they are a part of will tend to not be damaged as much or will be protected in some
way).
In contrast, those processes that damage or endanger the system will operationally not occur
as often or be as easily transferred because the structures that they are a part of are more
likely to be damaged or destroyed.
Researchers have developed computer programming techniques that solve problems based on
the complex processes of biological evolution and natural selection.
For a brief introduction to these techniques, press their name or the corresponding number.
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Another pattern for Self-Organization is found for example in the complex system of the central
nervous system of animals.
In this example, the brain cell networks that are the ones that most successfully help the
animal survive are the one's that are the most used, and thus are the ones that grow the most
in size and complexity.
In contrast, those brain cell networks that do not help the animal survive are less used, and
thus grow less, and may even stop growing, atrophy, and disappear.
Researchers have developed a computer programming technique based on this approach to
solving problems (like survival) called (III.3) Neural Networks.
For a brief introduction to this technique press on it's name or outline topic number.
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A further example of a pattern of Self-Organization is where an overall task is accomplished by
breaking it down into mini-tasks which are then spread among separate little parts for
execution, which then also coordinate together where necessary to support the overall
functioning.
In nature we find this manifested most obviously in cells which make up organs in the body.
Cells do not exist separate from the organ.
Cells in fact make up the organ's very structure, and then perform different roles in the overall
work of the organ which accomplishes it's overall purpose.
Researchers have developed a computer programming technique based on this approach to
solving problems called (III.4) Cellular Automata.
For a brief introduction to this technique press on it's name or outline topic number.
Press your Browser BACK BUTTON to return to this point when/if you wish to.
When change occurs in Complex Systems it occurs in a non-linear fashion.
Linear change is where there is a sequence of events that affect each other in order as they
appear one after the other.
In contrast, in non-linear change, one sees elements being changed by previous elements, but
then in turn these changed elements affect the elements that are before it in the sequence.
Thus in non-linear analysis, researchers look at how everything in the sequence has the
possibility of affecting everything else in the sequence before and after it.
Thus often the result ends up being unproportional to the original input.
This type of dynamic in a complex system is much closer to how things actually happen in
nature.
Almost never in nature does a purely linear sequence of events and change occur.
For more general information about Non-Linearity, press HERE Applications: Relevant Links
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It is usually fairly easy to predict what will develop in the next stage of system development
when one has extensive knowledge of the previous stage.
And this knowledge is usually of a range of possibilities that can develop next.
But as one begins to deal with stages of development farther and farther down the sequence of
developmental stages, it becomes more and more difficult to predict what will develop based
only on knowledge of that first stage, even when that knowledge is extensive.
Thus even though there is logical development from stage to stage, there is an increasing
inability to predict what will actually be the next development.
This uncertainty of predictability is called "chaos".
Thus, one can then see how a tiny change in a condition can eventually lead to a huge number
of different possible results.
But yet all these changes are still logical results of that tiny change, it just becomes
increasingly difficult to predict exactly which result will actually occur.
But since some probability of occurrence for many of them can be known, then statistical
analysis is still very important for helping describe the overall situation.
The classic illustration for this is the idea of how the flapping of butterfly wings in one part of
the
world can contribute to the evolution of a hurricane in another part of the world.
For more general information about Order and Chaos, press HERE then HERE Applications:
Relevant links.
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The unpredictability that is thus inherent in the natural evolution of complex systems then can
yield results that are totally unpredictable based on knowledge of the original conditions.
Such unpredictable results are called emergent properties.
Emergent properties thus show how complex systems are inherently creative ones.
Emergent properties are still a logical result, just not a predictable one.
This can also include higher level phenomenon that cannot be reduced to it's simpler
constitutes or it's origins.
For more information in general about Emergent Properties, press HERE Applications:
Relevant Links.: Press your Browser BACK BUTTON to return to this point when/if you wish to.
Artificial Life is a modelling program that simulates on a computer screen, with computer
generated entities (called agents), the evolutionary processes of life which dictate which will
survive and which will not.
For more information in general about Artificial Life, press HERE Applications: Relevant Links
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Genetic Algorithms is a computing approach that is based on processes that attack problem
solving in the manner that natural selection in biological evolution attacks the problem of
survival of the fittest.
Just as in natural selection, the program is set up to generate all sorts of programming
solutions to a particular problem, and the ones that succeed and solves the problem survives,
and the ones that don't are discarded.
For more information in general about Genetic Algorithms, press HERE Applications: Relevant
Links Press your Browser BACK BUTTON to return this point when/if you wish to.
An interconnected group of artificial nerve cells affect each other in such a way as to arrive at a
result based upon their inputs.
This is adjusted in time until it best matches the required answer.
This approach is primarily used to study processes of learning and self-organization.
This programming approach is patterned after the developmental processes of the nervous
systems of the animal kingdom.
For more information in general about Neural Networks, press HERE Applications: Relevant
links Press your Browser BACK BUTTON to return this point when/if you wish to.
Their functioning is limited to a sub-task of the overall task of the structure that contains them.
They cannot move around independent of that structure.
These limitations are often also the programmed parameters of the designated problem or goal.
These agents then solve their own individual programming goals, and coordinate together,
which then contributes to the accomplishment of the overall structure's task or goal.
This is patterned after the way cells operate as parts of an organ in the body, and support the
functioning of that organ.
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dynamics, context, overall,
interactions, viewpoint, science, attractors, environment,
connections.
Summary:
Philosophy is essentially
about the questioning of assumptions, those axioms that form the
starting point for any mathematical or scientific perspective.
Here we will introduce our take on the ideas that comprise this viewpoint and contrast them
with traditional views, in a way that emphasises the value of this new thinking.
The complexity viewpoint is not however restricted to scientific areas and can usefully be
employed in considering many personal and social situations where complex interactions and
difficult decisions need to be evaluated.
This combines, in our view, three strands of thought, systems thinking (incorporating
cybernetics) which relates to non- specific systems, organic thinking (including evolution)
relating to non-static systems, and connectionist thinking (attractor based) relating to non-
reductionism.
Normally we associate the idea with cybernetics, a type of system that incorporates feedback,
causal loops that force nonlinear behaviours, and which develops homeostasis or constancy in
system space.
These sort of systems are self- contained and self- regulatory, we cannot look at the parts in
isolation but must consider the overall (holistic) purpose of the system.
This relates to emergence, the generation of new higher level system properties that contain
functions that do not exist in any of the parts.
Organic systems have a metabolism, they are both self- producing (they manufacture their own
parts, unlike artificial systems) and self- maintaining (self-repair is possible).
They are responsive to their environment, but unlike general cybernetic systems are also
adaptive, discovering new behaviours over time - they are innovative.
This relates to associative learning and over a longer evolutionary period to genetic algorithms
where coevolution amongst large populations by natural selection plays a part.
The idea of connectionism is derived from artificial neural networks in cognitive science, where
inter- unit wiring is both explicit and brain like, and employs a distributed data structure.
These systems all self- organize and that is one of the defining features of connectionist
systems, the connections allow information to communicate across the system and the
system closure (feedback loops) then causes attractors to form.
Complex systems are generally composed of independent or autonomous agents (not the
identical parts often assumed in science).
Thus taking the properties of each part and adding them will not give a valid solution to overall
fitness - the whole is different than the sum of the parts.
This means that the existence and properties of the parts themselves are affected by the
emergent properties (or higher level systemic features) of the whole, which form constraints or
boundary conditions on the freedom of the constituents.
For example, we, as humans, determine (by our actions) the fate of our cells just as much as
their function determines us, and this two way structural interplay is common in complex
systems.
These systems operate far from equilibrium since they are dissipative (i.e. they take energy
from their environment to maintain the far-from- equilibrium position).
Their part freedoms will allow varying associations or movement, permitting clumping and
changes over time, thus initially homogenous systems will develop self- organizing structures
dynamically (therefore order increases over time rather than decreasing as expected in
conventional thought).
These 'edge of chaos' states are critical points in connectivity terms and the system is
maintained at the phase boundary by its self- organising dynamics - very different than the
either/or phases of conventional systems.
Trajectories differ, some show this divergence in state space from nominally similar inputs,
others show convergence to an attractor.
Parts can change their associations or connectivity freely - either randomly or by evolved
learning procedures.
Thus the system can be regarded as redesigning itself over time, as far as proves necessary to
maintain or change function within its operating context.
Breaking away from the constraints of old-style scientific axioms (which nethertheless remain
valid within their limited domains) allows us to explore an organic world that until now has been
difficult to understand in overall terms.
In such high-dimensional (multivalued) systems reductionist thinking proves inadequate,
isolated single dimensional results do not predict real system behaviours.
The coevolutionary or epistatic nature of interrelated systems requires us to take a contextual
approach, studying the dynamics of interactions rather than the static makeup of parts studied
in more conventional science.
Contextual approaches recognise that systems do not exist in isolation, but are defined only in
conjunction with other systems (including that of the observer).
This coevolutionary nature of multiple systems brings us to an ecosystem viewpoint and allows
us to understand the irregular changes over time that characterise such systems.
This viewpoint is not emphasised in the assumptions of our conventional sciences, which are
based on static snapshots of what are non-static systems.
In complex systems solutions are always compromises, there is no single answer.
What we must do instead is to compare alternative answers or options in state space, using a
plurality of techniques, with a view to identifying the most fit, the global optimum in the context
of interest.
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complexity, complexity
theory, life, applications, papers, science, tour, navigation, dynamics.
Summary:
Critically interacting
components self- organize to form potentially evolving structures exhibiting
a hierarchy of emergent system properties.
I'm lost, what do I do?
Hi all, this is a big site so we've put together a few hints.
First what are we here for?
The Complexity Theory idea covers many separate scientific research fields, nominally
designated by such titles as ALife, Cellular Automata, Fractals, Genetic Algorithms, Neural
Networks and Nonlinear Systems.
The ideas used in these fields are complementary to each other and are all ways of
approaching the study of those difficult areas involving many connected variables - complex
systems.
This site will relate and contrast all these approaches, and highlight their applications to both
new areas of research and to long standing problems within conventional science and the
humanities.
We provide a non- technical introduction for those who have not met any of these ideas before,
we supply a more in- depth knowledge for those new to these fields and also offer a wider
perspective for any visiting specialists.
Science normally considers simplified aspects of reality.
A sub-set of properties (e.g. the movement of planets) is abstracted and mathematical formulae
are developed and tested, to determine how well the property of interest can be modelled and
predicted.
Where this cannot easily be done the average of a collection of separate results is used
instead to generate statistically valid models (e.g. birth rates per year by country).
This has been a spectacularly successful enterprise since Newton's day, yet it has limitations.
Many of the subjects that we wish to study are not amenable to simplification, they arise as a
result of the complex interactions between many different individual parts.
Into this category come much of life and intelligent (human) behaviours.
The field of Complexity Theory attempts to apply scientific methods to these complex
systems, concentrating not on the entities (parts) but on the interactions, the dynamics of the
system.
Artificial Life attempts to apply these techniques to emulate biological processes, simulating
the process of life and extracting the properties that are common to all possible life, and not
just that based on our own Carbon metabolism.
These fields also complement and extend earlier work in cybernetics, general systems theory,
system dynamics, synergetics and dynamical systems theory.
Close relations with artificial intelligence, process thought, fuzzy logic, evolutionary theory,
general semantics, axiology and constructivism are also evident.
In the humanities we try to both understand the creative impulse and to direct it in interesting
ways.
Here too complex system studies have a lot to offer.
We can contrast the achievements so far seen in the arts with those theoretically possible, and
gain insights into possible new directions and inherent limitations.
We can evolve artforms in new ways, using the facilities of new technology and in this way give
creative expression to those of us unskilled in conventional methods.
Historically we can gain understanding of the dynamics of society and the ebb and flow of
power within a framework of changing stability and chaos.
We are able to balance the opposing forces of destruction and creation and come to a new
understanding of meaning and ethics within political and economic systems.
The main scientific entry point for our site is our Introduction to Complex Systems intended for
those completely new to these subjects.
It leads on to more detailed material.
A general introduction to our wider philosophical concept, and how Complexity Theory fits into
this paradigm, is on our Concept page, and a overview of the CALResCo Group, our ideas and
our work is available on the Information page.
Introductions to various aspects of these fields can be accessed from the Themes page, with
some pictorial results starting on the Images page.
Our Art Exhibitions give an exciting alternative viewpoint from an artistic perspective and you
can easily Search our entire site to locate material containing particular words of interest to
you.
For those requiring research information, we offer our Online Papers page which gives access
to some of the best research material on the Web within these fields and our Related Papers
page which references applications and similar work in other fields.
Our FAQ on Self- Organizing Systems may be of interest and to catch up on the latest
updates to our site we have our What's New page.
We flag both New and Updated (changed URL) entries on the relevant pages for your
convenience, but sites do get moved by authors and we also flag links that we think are Dead
where we don't know their new location.
All of the pages on the site have a number of navigation buttons at the top and bottom of the
page.
Selecting any of these will take you directly to the indicated page.
Most pages (including this one) have "Hot Links" (that look like that) that take you directly
to
the specified page or information (that one goes back up to 'Our Scope'), just click on these to
select them.
To make things easier our recommended tour is automated and described fully on our This
page is the first port of call on the tour, so if you have already started just press to continue the
tour...
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1. My Academic Work
