General scientific and systemic concepts

The accumulation of scientific knowledge may be considered to be one of the most extensive intellectual processes of humanity. The organization of the enormous material, a science in itself, is influenced by systemic principles. (See Namilov and the systems view of science, page 189.) A survey of the content in a specific knowledge area is best carried out using a top-down approach, beginning with the area’s prevalent world view. For readers unfamiliar with the scientific vocabulary related to the hierarchic organization of scientific knowledge, some main concepts are presented below. According to the scientific tradition, theories should be explicit (not based on interpretation or intuition), abstract (not referring to concrete examples), and universal (valid in every place and at anytime). This implies that a theory regarding the behaviour of certain physical particles therefore relates to every individual particle in the universe, without exception. In spite of this, science can always be regarded as an asymptotic approach to the truth.

A theory may be considered a compressed description of the world. Thus, the length of its initial assumption is much shorter than a detailed description of the events themselves. Typical in this respect is physics whose theories are just mathematics in the shape of formulas.

A world view is

a grand paradigm including the beliefs and philosophical preferences of the general scientific community.

A paradigm is

a common way of thinking, held by the majority of members of a specific scientific community.

A theory is

a broad coherent assembly of systematic explanatory schemes, consisting of laws, principles, theorems and hypotheses.

A law is

a generalization founded on empirical evidence, well-established and widely accepted over a long period of time.

A principle is

a generalization founded on empirical evidence but not yet qualifying for the status of a law.

A theorem is

a generalization proven in a formal mathematical, logical way.

A hypothesis is

a proposition which is intuitively considered to be true but which has to be falsified or verified.

An axiom is

impossible to prove or deduce from something else, but is a starting point for the hierarchy of scientific abstractions presented here.

It is important to understand that present scientific ‘truth’ descends from observation and experiments. These are also the starting point for the construction of a theory which hopefully corresponds to the observations. The theory itself must be considered as an instrument to handle a formal symbolic system in order to exceed the limitations of thought. If so, this does not, however, prove its truth; it is ‘only’ the best we have for the moment. The truth of science is always provisional, and accordingly, the theory must be subject to change as new information appears on the horizon. The search for a better theory is a perpetual challenge for new generations of scientists.

A concept closely related to the theory is the model, which can be considered a link between theory and reality. To use a model is to visualize a theory or a part of it. A closer look at the model tells us that it is a phenomenon which somehow mimics or represents another primary entity. It may also be expressed as ‘one thing we think we hope to understand in terms of another that we think we do understand’ (Weinberg 1975). As a theoretical construct it fits the known, available facts into a neat and elegant package. It is an imitation or projection of the real world, based on the constructor’s problem area of interest. In this simplified version of reality certain features are stereotypical. The model brings out certain characteristic features in the object of study, simultaneousely excluding others. The quality of a model can only be judged against the background of the purpose of its origin.

Models are employed to develop new knowledge, to modify existing knowledge or to give knowledge new applications. From a pedagogical point of view, models are used to render theories more intelligible. Models can also be used to interpret a natural phenomenon or to predict the outcome of actions. Through the use of models it becomes possible to know something about a process before it exists. The model can be subjected to manipulations that are too complex or dangerous to perform in full-scale. Also, to use a model is less costly than direct manipulation of the system itself would be.

When a model does not work in reality this can sometimes be ascribed to the fact that the model has been confused with reality. The tool must be separate from the solution and the method from the result. Models are nevertheless in a sense indispensable as most often reality is far too complex to be understood without their help.

Models are commonly classified as iconic, analogue, symbolic, verbal and conceptual. Iconic, or physical, models look like the reality they are intended to represent. One example is a scale model of a ship’s hull, used to collect information concerning a proposed design. Full-scale models are always iconic; they are used for the same purpose although their dimensions coincide with those of the real object. Even a living mannequin is a full-scale iconic model.

Analogue models represent important qualities of reality through similarity in relations between entities expressed in entirely different forms, that are easier to handle. Such models behave like the reality they represent without looking like it. An example is a mathematical graph or a terrain map.

Symbolic models use symbols to denote the reality of interest. Normally general and abstract, they are often more difficult to construct but easier to use than other models. Examples are mathematical, linguistic or decision-making models. A schematic model reduces a state or event to a diagram or chart. A circuit diagram of an electronic amplifier exemplifies a schematic model of the actual hardware. Another kind is a flow chart describing the order of events in different processes.

A mathematical model uses mathematical symbols to describe and explain the represented system. Normally used to predict and control, these models provide a high degree of abstraction but also of precision in their application. A warning regarding the inevitable dilemma associated with mathematical models has, however, been given by Einstein (1921) when he says: ‘When mathematical propositions refer to reality they are not certain; when they are certain, they do not refer to reality.’

A verbal model depicts reality through the use of verbal statements that set forth the relationships between the concepts. Conceptual models are theoretical explanations; in accordance with their final purpose these models are prescriptive, predictive, descriptive or explanatory.

A model of an as yet untested construction can be used to predict how it will behave initially. Similarly, to establish what kind of properties a non-existing original will possess, reality can be imitated by using the model in a simulation. With regard to the time aspect, models may be either static or dynamic. Models which exclude the influence of time are typically static, while those including time are dynamic. In a dynamic simulation, a model is rapidly exposed to a continuous series of inputs as it passes through artificial space and time. Simulation is only possible if there exists a mathematical model, a virtual machine, representing the system being simulated. Today this machine is represented by the computer.

A special kind of simulation is gaming which most often involves decision making in critical situations. The decisions relating to hypothetical conditions are taken by real decision makers. Sometimes the situation includes a counter-measure team which increases the degree of difficulty.

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

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