Key Points:

Breaking Our Mental Chains
http://www.calresco.org/lucas/breaking.htm

Concepts:

science, common, tradition, context, societies, humanity, history, life, specialisms.

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.

CALResCo's Introduction to Complex Systems
http://www.calresco.org/intro.htm

Concepts:

complex systems, Browser, programming, networks, survives, applications, relevant links, Cellular Automata, emergent properties.

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.

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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 Press your Browser BACK BUTTON to return to this point when/if you wish to.

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 Press your Browser BACK BUTTON to return to this point when/if you wish to.

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.

The Philosophy of Complexity
http://www.calresco.org/lucas/philos.htm

Concepts:

dynamics, context, overall, interactions, viewpoint, science, attractors, environment, connections.

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.

Complexity Theory: Overview of the CALResCo website
http://www.calresco.org/help.htm#comp

Concepts:

complexity, complexity theory, life, applications, papers, science, tour, navigation, dynamics.

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...