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General Systems Theory

Updated on April 4, 2017

Part Four

Continued from:

“Systems theory or systems science is the interdisciplinary study of systems, with the goal of discovering patterns and elucidating principles that can be discerned from and applied to systems at every level of nesting and in every field of research.” - WIKI

We see that it is a general approach that paints broad strokes across diverse disciplines. Systems theory looks for patterns and principles that can be applied to every area of scientific research. I use the word scientific loosely here as the current focus appears to be mostly related to structures of human organizations; society, culture, language, corporate structure. Definitions are either not provided, are NOT scientific, or they are used inconsistently.

Early on, general systems theory looked mostly at “self-regulating” systems that maintained themselves via feedback. These included climate and physiological systems. Hence we see the same terms such as information and decision used whether discussing neurons or tornadoes.

Let’s take a very brief look at systems theory, its history, some of its proponents and their use of Key Terms.

In 1928, biologist Ludwig von Bertalanffy proposed that a system is depicted by how its components interact nonlinearly. In the early 1950s he applied his theory to living things, and later general systems theory was made popular by electrical engineer Lotfi Zadeh. Many others picked up Bertalanffy’s General Systems Theory and applied it to sociological systems, to psychology, to math, game theory, and more.

Unlike many of his contemporaries, physicist Thomas Kuhn actually defined many of his terms. Here are a couple of them:

  • object: a pattern as it exists at a given moment in time
  • event: a change in a pattern over time
  • system: any pattern whose elements are related in a sufficiently regular way to justify attention where a change in one component induces a change in another component

Khun describes both controlled and uncontrolled systems referring to what he calls the detector, selector, and effector which concerns functions involved in communication and decision within the system and transaction between systems. There are various ways of looking at a single system internally, between subsystems, and how a system exchange information with its environment. Matter and energy may also be exchanged between a system and its environment or between systems (called input and output).

When there is no longer change within a system, it is in a state called static equilibrium. Dynamic equilibrium occurs when there is change among elements, but one or more variables are within a certain range. All systems “tend towards equilibrium,” according to Kuhn, with both positive and negative feedback within the system working towards restoring elements to an “initial value” or driving them away from it.

A closed system is one where interactions are only within the system, whereas an open system exchanges information, matter, or energy with its environment. Open systems can move towards higher levels of organization but closed systems remain the same or become more chaotic.

Chaos Theory is an attempt to make sense of seemingly random events culminating in organized systems such as weather patterns. In the early 60s a meteorologist by the name of Lorenz simulated weather patterns using a computer and noticed very small differences of input lead to very diverse types of weather pattern simulations. Sadly, for optimistic weather monkeys, this did not lead to the ability to manipulate the weather, or predict long-term weather forecasts.

A mathematician presented the idea of phase space where a changing system’s dynamics could be mapped out and complete knowledge of the system could be gained. Every point in the trajectory represented various states in time. Although it takes many dimensions to represent these dynamic state changes, and computers can apparently do this, the poor hairless ape can not easily conceive of phase space beyond three dimensions. Gee, I wonder why?

See Rational Scientific Method Vol. II, Chapter Twelve, Dimensions, Chapter Thirteen, Dimensions of Reality and Chapter Fourteen, The Three Dementia of Geometry

A Poincare Map can aid understanding of 3D systems by overlaying 2D segments forming patterns. Patterns change depending on where a reference line is drawn representing the Y axis. In other words, there really is no order apart from the observer’s perception and what he or she believes about it!

Add three variables, and as a system grows it becomes more and more chaotic. Looking at an infinite number of points in space, it’s no wonder that most differential equations can not be solved.

Mandelbrot hypothesized that changes happen in discrete steps and some things tend to persist. He noted that how long a coastline is depends on the observer and how it is being measured. He proposed that the length is actually infinite because as one uses smaller and smaller measurements the length increases. Dimension of “roughness” is in the eye of the beholder! Applying fractals to the coastline shows “self-similarity.” Mandelbrot’s fractals were applied to the study of earthquakes, fluid dynamics, and the human body. Fractal branching of capillaries and of neurons, for example, is the body’s efficient way of delivering blood to cells, and ordering the structural development of the brain. Fractals seem to be a universal factor in the morphological development of all structures animate or inanimate.

Systems tend to move towards a stable point, or oscillate regardless of the original condition. Some non-linear systems tend to have two steady states, like a pendulum, and the study of this phenomenon is called fractal basin boundaries. Little is understood about this process and others, such as, how a small disturbance to a system can often cause chaos.

What is a self organizing system?
It is a system that can reproduce or maintain itself by the use of its own component parts. Order arises from chaos, and new order emerges from an old system by virtue of its interacting elements. The whole is greater than its parts. The system is delineated from its environment by a border, its behavior is complex and difficult to predict, arising from interactions between the number of elements and connections between them within that border.

Self organizing systems are characterized by many things. Among them are the following:

Certain parameters influence the behavior; when particular values of the parameters are reached the system becomes unstable; the system imports entropy and dissipates energy as a result; a self- organizing system is similar to other systems but also unique in some ways; small internal fluctuations intensify initiating order; causes and effect are non-linear; the system chooses from alternate paths during its development; emerging properties can not be reduced to the properties of the individual elements of the system.

Self-organizing systems are found throughout biology, chemistry, and physics. In the literature, there is typically no clear distinction between living or non-living self organizing systems

Not defining terms, using terms inconsistently, or using unscientific terms, then placing the observer front and center of the research is at the heart of the problem. Confusing objects with concepts, thinking in terms of discrete particles, and creating isolated systems where there are none makes understanding systems, and especially self-organizing systems, impossible.

Although researchers are very good at describing systems, since all atoms are interconnected by an underlying physical mechanism, there really are no truly open, closed, or isolated systems. There is a fundamental difference between living and non-living systems, all things are interconnected, and it is the ability of living objects to move on their own against gravity and against or along with the path of least resistance which needs to be considered in order to move beyond mere descriptions into understanding the difference.

We’ll cover far more on systems in general, and self organizing systems in particular as we look at the laws of thermodynamics and how everything relates.

Up next: The Laws of Thermodynamics:


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