The systems age

In the 1950s, with the introduction of computers, hydrogen bombs and space exploration, large-scale problems began to penetrate Western society. The traffic-system breakdowns, environmental disasters and the nuclear threat were immediately high on the agenda. Society was faced with messes, interacting problems varying from technical and organizational to social and political.

It was suddenly realized that many solutions were inadequate when applied to problems which no longer existed in their original form. Change itself, with its accelerating rate, was a major concern. Two hundred years of success for classical science and technology had created a form of development the long- term effects of which apparently were programmed to be devastating for humanity. Gerald Weinberg states in one of his books that ‘science and engineering have been unable to keep pace with the second order effects produced by their first order victories’.

The following examples address some of the problems:

  • deterioration of the human gene-pool, increasing allergies, diabetes, and antibiotic resistance
  • deterioration of human epidemic environment, g. AIDS
  • environmental destruction and climatological changes
  • deforestation and desertification
  • garbage accumulation, nuclear radiation, water, soil, and air pollution
  • acidification, decreasing subsoil water and shrinking ozone layer
  • decreasing biodiversity and extinction of species
  • population explosion, general migration, criminalization, terrorism
  • junk food and genetically altered cereals and fruit
  • urbanization, unemployment, and proletarianization
  • energy wastage and resource depletion
  • motorization and noise pollution
  • data pollution, lack of information and knowledge
  • commercialization and cultural impoverishment
  • mental corruption and drug abuse
  • environmental ugliness with growing amounts of concrete and asphalt
  • bureaucratization, passivization and dulling of the human intellect
  • destruction of arable land with buildings, highways, mining districts, junkyards and minefields
  • wars constantly in progress on several places in the world

Classical science, with its over-specialization and compartmen- talization, had already proved its inability to handle problems of such tremendously increased complexity as in the above list. The interaction of system-variables are so interlinked to each other that cause and effect is a kind of circular logic. One separate variable thus can be both cause and effect. An attempt to reduce complexities to their constituents and build an understanding of the wholeness through knowledge of its parts is no longer valid. Not understanding that the wholes are more than the sum of their parts, scientists had assembled knowledge into islands, extending into an archipelago of disconnected data.

Not long ago, physics was regarded an archetype for all genuine science. A reductionary chain was envisaged where psychology was deducted to neurophysiology, neurophysiology to biochemistry, biochemistry to chemistry and this in turn to quantum mechanics. Today, modern biology has shown that this kind of reductionism is out of the question. Physics, chemistry and biology have united with each other into molecular biology — a new overarching description system separated from the area of both physics and chemistry.

Many scientists have now realized that the way they had embraced the world was not far-reaching enough to understand and explain what they observed and encountered. As Gary Zuchov (1979) says in his book The Dancing Wu-Li Masters: ‘Their noses had been too deeply buried in the bark of a special tree, to be able to discuss forests in a meaningful way.’ Against this background, the adaptation of science became systems thinking. This attitude was an answer to the inability of the mechanistic outlook to explain social and biological phenomena. It can be deduced from the 1920s when synergy-effects in living organisms began to be observed. General Systems Theory was born as an attempt to convergence in a world where the unity of science had been lost and different disciplines had drifted apart.

It was gradually accepted that systems are wholes which cannot be understood through analysis inasmuch as their primary properties derive from the interactions of their parts. Thus awareness grew that everything in the universe — including themselves — which seems to exist independently, was in fact part of an all-embracing organic pattern. No single part of this pattern was ever really separated from another. It was possible to catch a glimpse of a universality of systemic order and behaviour which characterized both living and non-living systems. That humans now had got access to some of the main design principles of the universe implied that they too were included in the drawings for some very significant ultimate purpose.

Earlier, the alternative to systemic intervention was to suffer the consequences, to endure whatever happened; scientists had too often waited for systems failures to see what these could reveal about the mechanism. Today function, not anatomy, is the main point. The important task is to solve problems in real life. To describe and understand were not values in themselves; their purpose was to enhance the capability for large-scale system prediction and control.

The technicians strove to have things work well, the social scientist to have things behave well. Science was to become more ethical, less philosophical. To do things, was considered to be more important than to think about them. In these circumstances emerged the new interdisciplinary and holistic approach. Here, holism was an attempt to bring together fragmentary research findings in a comprehensive view on man, nature, and society. In practice it was a search of an outlook to see better, a network to understand better and a platform to act better. Without hesitation this had it roots in the wartime efforts and the special mentality of operations research. This ’emergency-discipline’ handled military strategic decisions, resource allocations, optimal scheduling and risk analysis, etc. in a truly pragmatic way. Its aim was to do, to the best of human knowledge in a given context and with given time and resources, all in order to win the war. Its main guidelines were the following:

  • It is not necessary to understand everything, rather to have it under control. Ask what happens instead of why.
  • Do not collect more information than is necessary for the job. Concentrate on the main consequences of the task, the small details may rest in peace.
  • Solve the problems of today and be aware that prerequisites and solutions soon become obsolete.

Operational research gave rise to the first successful methodology where the problem complex knot was disassembled into disciplinary parts and could be treated as one entity by different researchers.

In 1954, the International Society for General Systems Theory, ISGST, was founded. This society later become the International Society for Systems Science, ISSS. Two of the most prominent founders were Ludwig von Bertalanffy and Kenneth Boulding. Although Bertalanffy had already formulated his ideas in the 1930s, he was not recognized until one of his now-classic papers on systems theory appeared in the American journal Science in 1950. Then, the idea that systems had general characteristics independent of the scientific areas to which they belonged was both new and revolutionary. Boulding in turn published his well-known system hierarchy in 1956.

The founding team of interdisciplinary scientists, had a shared interest in a universal science. They wanted to link together the many splintered disciplines with a law of laws applicable to them all. The following aims were stated:

  • to integrate similarities and relations within science;
  • to promote communication across disciplinai boundaries;
  • to establish a theoretical basis for general scientific

Integration should be promoted by the discovery of analogies and isomorphisms and the new science should be a tool with which to handle complex systems. Analogies are explanations done by relating something not yet understood to something understood. Isomorphism exists when common characteristics, structures, formulas and form of organization are in accordance in different systems. That is, when formally identical laws governing the functioning of materially different phenomena exist. A partial accordance is generally referred to as homomorphism. The use of isomorphism made possible the indirect study of systems in terms of other systems (simulation) and the use of content-independent methods within different scientific areas.

Step by step a theory was established: the General Systems Theory or GST. As a basic science, it deals, on an abstract level, with general properties of systems, regardless of physical form or domain of application, supported by its own metaphysics in Systems Philosophy. GST provides a way to abstract from reality; simplifying it while at the same time capturing its multidimentionality. As an epistemology it structures not only our thinking about reality but also our thinking about thinking itself.

General Systems Theory was founded on the assumption that all kinds of systems (concrete, conceptual, abstract, natural or man-made) had characteristics in common regardless of their internal nature. These systems could serve to describe nature and our existence. General Systems Theory is, however, not another discipline — it is a theory cutting across most other disciplines linking closely e.g. generalized concept of organization, to that of information and communication. GST uses various ways in classifying different types of systems — most of them offering an intuitive classification of systems ranked in increasing order of complexity. Here each level include, in some way, the lower levels but have its own, new, emergent properties. The process of emergence results from the interaction of independent parts when they stop being independent and start to influence each other. In the various levels of the taxonomy, it can be seen, that it is the relationships between components in the system and not the nature of its individual components, that proliferate its properties and behaviour.

Expressed in more precise terms, the goal of General Systems Theory can be specified as follows:

  • To formulate generalized systems theories including theories of systems dynamics, goal-oriented behaviour, historical development, hierarchic structure, and control processes.
  • To work out a methodological way of describing the functioning and behaviour of systems objects.
  • To elaborate generalized models of systems.

As an applied science, GST became Systems Science, a metadiscipline with a content capable of being transferred from discipline to discipline. As such, it is knowledge regarding knowledge structures and attempts to add and integrate those aspects that seem not to be adequately treated in older science (but also to engage in continuous cross-fertilization of various disciplines). Systems science become the science of synthesis and integration. The management scientist Russ Ackoff (1972) has defined the difference between the synthetic thinking of a metadiscipline and the analytical thinking of a discipline.

In systems science, the equivalent to the classical laboratory became the computer. Instead of designing experiments with real materials, the computer itself became a viable substrate for experimentation. The use of computers as instruments for calculations, simulations and the creation of a non-existing reality thus brought about a new phenomenon that is neither actual nor imaginary, a phenomenon or mode that was called virtual. The computer is a virtual reflection of a non-existing mechanical adding machine. To be precise, it is an abstract entity or process that has got physical expression. In itself, it is a simulation, a simulation which is not necessarily a simulation of anything actual. ‘Virtual’ is thus a mode of simulated existence, resulting from computation. When creating theories regarding the information world and complex living systems, different kinds of virtual worlds are necessary. There, the computer works as laboratory and in its digital universe artificial intelligence and artificial life is created. Research in many areas like astronomy, aerodynamics, biology, chemistry etc. is today performed by computers through virtual simulation. Such simulations have the advantage that unneccesary details regarding individual components can be excluded at which overall connections and complex interactions appear. By use of computers, new knowledge can be generated without dangerous and ecologically harmful full-scale tests e.g in the area of nuclear fission. Another example is how politicians can practice crash- landing an economy without taking hundred of millions of people along for the ride.

The aim of systems science was, however, not to replace, but to complement traditional science. The systems perspective naturally acquired greater significance with the growing complexity of all systems, including and embracing man. Gerald Weinberg (1975) says about systems science, that it has ‘…taken up the task of helping scientists to unravel complexity, technologists to master it, and others to learn to live with it.’ General systems thinking based on systems theory became its hallmark with the aim of fostering generalists qualified to manage today’s problem better than the specialists. Specific individual methods were developed, many of which included modelling, simulation and gaming. Focusing on problems of complexity, systems thinking applied as systems science has taken the task of being a science of modelling par excellence.

One of these methods, the Systems Approach, in reality an application of Systems Theory, operates in an integrated framework of modern organizational knowledge and management science. The Systems Approach is based on the fundamental principle that all aspects of a human problem should be treated together in a rational manner. It is an attempt to combine theory, empiricism and pragmatics and looks at a system from the top down rather than from the bottom up. In particular, when the phenomenon under study concerns functions, goals, and purposes of live organisms or human beings, the whole behaviour is better explained by the ends than by the means.

Another method, Systems Analysis, adopting a strictly systemic outlook on complex organizations, entered the scientific scene to ensure that no important factors in the structure were excluded. Problems of identifying, reconstructing, optimizing, and controlling an organization, while taking into account multiple objectives, constraints and resources were worked out. Possible courses of action, together with their risks, costs and benefits were presented. Systems analysis can thus be considered an interdisciplinary framework of the common problem-view.

An extension of this method, called Anasynthesis, was introduced with the implicit assumption that the more views one can apply to it, the better a problem can be understood. When using this method, modelling, simulation, gaming, analysis and synthesis are all applied to the development of a system. The method is used iteratively at both the macro and micro levels of large-scale systems. Normally, the outcome is more organized, structured and responsive to real-life requirements than the outcomes of other methods.

Then there is System Engineering, a method by which the orderly evolution of man-made systems can be achieved. Hereby the four Ms — money, machines, materials and men — are used in making complex systems in their totality. Somtimes three more Ms are added, generated by information and denoting messages, methods and measurements.

A much-discussed method of a more theoretical kind is System Dynamics. Developed by Jay Forrester (1969) it uses dynamic computer models which change in a network of coupled variables. It has been employed to prognosticate the growth of the modern city (Urban dynamics), the development of Western industry (Industrial dynamics), and the global resource depletion (World dynamics).

Closely connected to the above-presented methods, and including them all, is the conviction that man is more the creator of reality than its discoverer. The future has become too complex to foretell or to be planned; it has to be created. That one cannot manage change, only be ahead of it is not relevant for systems thinking. Embracing such a pragmatic view on reality, design or redesign becomes the key concept of the systems perspective when it is about to change the world for the better by building new or improved systems. The vast majority of human systems have not been designed at all — they just happened. Design replaces the guesswork by model building and  optimization. It is concerned with how things ought to be, with combining resources to attain goals. This involves processes necessary to understand the problem, to generate solutions and to test solutions for feasibility. Here, design is a creative process, questioning the assumptions upon which earlier structures have been built and demanding a completely new outlook. Systems design (or systems synthesis) is a formal procedure where human resources, artefacts, techniques, information and work procedures are integrated into a system in order to facilitate its performance.

Its working procedure rests on the following steps:

  • The future environment of the system has to be forecasted.
  • A model has to be build and used to simulate its function.
  • From the simulation, a choice must be made as to what is the best (thus optimizing the system).

Systems design is the opposite of systems improvement, the policy of recovering old systems (/. van Gigch 1978).

A more recent perspective when investigating systems is that of teleology, the doctrine that behaviour and structure are determined by the purpose they fulfil. Teleology does not exist in non-living nature but is universal in the living world. It indicates that systems are guided not only by mechanical forces but also move toward certain goals of self-realization. Here organizations and organisms have their own purposes, while artefacts, e.g. machines, serve the purpose of others but have no such purpose of their own. The search for knowledge can thus be founded both on the hunt for causes and purposes.

Complex systems can be studied from many points of view which are seen as complementary rather than competitive. The choice of theoretical approach depends mainly on the type of insight which is sought. A common quality of the named methods is the generation of knowledge necessary for the solving of the problem. The characteristic tools of the domain — computers, telecommunication networks, databases, etc. — are to be found in informatics.

One effect of the new approach was that subsets of traditional scientific areas amalgamated, forming new disciplines. A fresh example is the science of complexity, where biological organization, computer mathematics, physics, parallel network computing, nonlinear system dynamics, chaos theory, neural networks and connectionism were brought together. In practice, complexity science is the study of the behaviour of large collections of simple units which have the potentially to evolve. This stimulated the definition of new reciprocal systemic qualities: complexity/simplicity and  simulative/non-simulative.  A new quantification of complexity was also introduced: the complexity of something should be defined as the length of the shortest possible description (algorithm) of this something. An alternative definition is in terms of the number-of mathematical operations needed to solve it. Computer scientists use the term algorithmic complexity, which is defined as the the length of the shortest program that will execute the computation (although one cannot, in general, prove that it is the shortest. A shorter one may always exist).

Laws of complexity generate much of the order of the natural world and its emergent properties. Complexity theory tries to describe how complicated rules sometimes produce simple and organized behaviour, e.g. the ability of living systems to become ever more organized. Its working methodology is non- reductionist: a system is viewed as a network of interacting parts, nearly all the fine details of which are ignored. Regularities and common patterns valid across many different systems are carefully examined. Of specific interest are those conditions which ensure the emergence of evolutionary, self-organizing and self-complicating behaviour. Complexity theory operates somewhere in the zone between the two extremes of complete order and complete chaos. To study complexity is to study systems and particularly the sort of systems behaviour which cannot be predicted from its individual components. Complexity concerns the system-fact that the whole always is greater than the sum of its parts. As a discipline its task is to come to grips not only with certain complex phenomena but also with the universal features of complexity itself.

Also, disciplines more directly related to systems science, such as cybernetics, bionics and C3I, merit presentation. They make possible a broader perspective concerning the basic underlying principles of structure and behaviour in systems.

Cybernetics was defined in 1948 in a book by Norbert Wiener: Cybernetics or Control and Communication in the Animal and the Machine. In cybernetics, living systems are studied through analogy with physical systems.

Bionics, the study of living systems in order to identify concepts applicable to the design of artificial systems, was introduced by Major Steele in 1958. The amalgamation of biology and technique is recognizable in the term. Bionics realizes physical systems through analogy with living systems. Cybernetics and bionics are often said to be the two sides of the same coin.

The acronym C3I stands for command, control, communication and intelligence. During the past ten years, interest in the operations of social, military and business organizations has grown. Modern managerial systems are based on an interchange between people, organizational entities and technical support. The decision-making situation has often such an innate complexity that in the initial phase it is not possible to define what kind of information is important; the decider usually demands more information than will be useful.

In the extended acronym C4I2 the extra C stands for computer and the extra I for integration, emphasizing the close interconnection between man and computer. Here, it is impossible to separate social from technical factors and the human being is always a part of the problem as well as a part of the solution. The adaptation man/machine is a key issue and the system has to be designed around man, his potential and his needs. In spite of access to high-tech decision support, a main point must be the training of human ability to handle the unexpected. Reality always tends to deliver a situation never met before.

Systems science applied as a problem solver in business organizations is sometimes called management cybernetics. As such, it is often occupied with design of an appropriate organizational structure which includes:

  • Specification of the organization’s sub-tasks and partition of work.
  • Design of communication between the subsystems.
  • Definition of areas of decision-making and authority.
  • Design and development of control systems and co-ordination of efforts toward the organizational goal.

The efforts of management cybernetics are sometimes summed up with the acronym ‘The Seven Ss’. These stands for strategy, staff, style, skills, systems (of communication), structure and shared values.

The emergence of the systems movement can now be recapitulated with some often-cited words of Kenneth Boulding from 1956:

‘General Systems Theory is the skeleton of science in the sense that it aims to provide a framework or structure of systems on which to hang the flesh and blood of particular disciplines and particular subject matters in an orderly and coherent corpus of knowledge.’

To that must be added that one of the most important contributions of the systems area is that it provides a single vocabulary and a unified set of concepts applicable to practically all areas of science.

Let us finally remind ourselves that the systems age in which we are now living, is the result of the impact of the following five revolutions:

  • The agrarian revolution — the product of the tribe’s collective work, extended our food access.
  • The scientific revolution — the product of European collective thinking, extended our knowledge capacity.
  • The industrial revolution — the product of European collective technology, extended our musculature.
  • The electronic revolution — the product of Global collective technology, extended our nervous system.
  • The computer revolution — the product of Global collective synthesis, extended our intelligence.

Source: Skyttner Lars (2006), General Systems Theory: Problems, Perspectives, Practice, Wspc, 2nd Edition.

Leave a Reply

Your email address will not be published. Required fields are marked *