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Karen Tesson writes:
Systems theory is a term coined by Bertalanffy in the 1940s (Bertalanffy, 1968), it deals with whole systems, rather than their disassembled parts. By contrast, the focus in systems theory is on the interactions within and between parts of a system, and on the interactions between a system and its environment. Context is therefore given some recognition in systems theory. Bertalanffy believed that there are general “systems” principles that apply to many different kinds of system, be they biological, physical, chemical etc. To study a system using systems theory was intended to bring these general principles to light. Some of the principles that Bertalanffy identified as “systems concepts” include the way that systems deal with inputs and outputs through processes, and how information can be viewed as a currency of communication. One of the key points raised by Bertalanffy was the difference between closed and open systems. Closed systems are isolated from their environment; they have no inputs or output exchanges with anything outside of their boundaries. The boundaries of a closed system are completely impermeable, and as such they define the extent of the system’s reach. Until the development of systems theory, most of physics dealt only with closed systems. Thermodynamics specifically states that its laws only apply to a system that is closed. A closed system will tend to move towards greater order; to increase its entropy until a maximum level whereupon a state of equilibrium is reached. In a closed system this equilibrium state is achieved, and maintained, without the input of further energy than the system already contains. Upon reaching a state of equilibrium, however, the system is effectively “fixed” and unable to perform any “work” (Bertalanffy, 1968). In the living world however, no system is closed; living systems have permeable boundaries, and are engaged in constant dynamic exchanges with their environments. According to Bertalanffy, living systems tend toward a “steady state”, which is a state of dynamic equilibrium. An open system that is maintaining itself at a steady state is apparently in equilibrium, yet it is not actually in a state of true physical, chemical or energetic equilibrium. Rather, it is maintained dynamically in a non-equilibrium state, at a point that is not the level of maximum entropy were the system to be made closed. In order to maintain this state of dynamic equilibrium, an open system requires energy input. The benefit however, is that an open system is capable of performing “work” (Bertalanffy, 1968). At around the same time that Bertalanffy was working on systems theory, a group of mathematicians, neuroscientists, social scientists and engineers began work on a novel system model that became known as cybernetics. The word cybernetics is derived from the Greek term kybernetes, which means “steersman”. Cybernetics has been defined as the “science of control and communication in the animal and the machine” (Wiener, 1948). During the 1940s, this initially diffuse group of researchers organized a series of now famous meetings known as the Macy Conferences, which were held in New York City. In these meetings, this group of researchers, who included Gregory Bateson (a biologist/ecologist), Margaret Mead (a social scientist) and Norbert Wiener (a mathematician), collaboratively developed the theoretical framework of cybernetics. Central to cybernetic theory are the concepts of feedback, and feedback loops. The principle is that in any autonomous system, processes are controlled and self-regulated by causal cycles, where the outputs of processes are connected together in cycles, with the output of each process becoming the input for the next. The nature of the connection between each process determines whether it is a positive or negative feedback relationship. This is illustrated in the figure below.
Figure 1. Feedback relationships. When the output of process A amplifies the reaction of process B, and the output of process B in turn amplifies the reaction of process A, then the relationship between them is known as a positive feedback relationship; when the output of process A augments the reaction of process B, but the output of B diminishes the reaction of process A, the relationship is known as a negative feedback relationship. Closely connected to cybernetics is Shannon and Weaver’s “information theory” (Shannon and Weaver, 1949). In information theory, information is viewed as a measure of uncertainty or entropy; the greater the amount of information, the lower the uncertainty. Shannon and Weaver’s model deals specifically with how information is transmitted. They postulated that for information to be transmitted, it has to undergo several stages. Firstly information originating at a source is converted into a message. A transmitter then translates this message into signals that can be transmitted along lines or channels to the receiver. This receiver then converts the signals into a message again that is decoded and interpreted. This model of information transmission was originally targeted at engineering communication, but in subsequent years it has been applied in many different domains, from telecommunications to biology, social sciences and human dialogue. Henri Atlan has made connections between information theory and biology, specifically in terms of the immune system (see Atlan and Cohen 1998). In animals, elements known as antigens and antibodies are key components involved in an immune response. An antigen is any molecule that triggers an immune response; an antigen might be a molecule on the outside of a pollen spore, or a cold virus for example. An antibody is a molecule produced by the immune system that attaches to and neutralises antigens. The relationship between antigen and antibody is highly specific, often with a single type of antibody being produced to target a specific antigen molecule. In their (1998) paper titled “Immune information, self-organization and meaning”, Atlan and Cohen explain that the relationships between antigens and their associated antibodies could be viewed as a means of transmitting information, where the antigen is treated as a transmitter and the antibody as the receiver. Although Atlan bases his model on Shannon and Weaver’s information theory, he does make some important distinctions. Significantly, Atlan argues that the way in which extraneous information or “background noise” applies to a biological context is quite different from that of the engineering context in which Shannon and Weaver originally applied their model. In an engineering context, noise is seen as a factor that reduces the quality of the information and which should be cut out for the quality of the information to be maintained. Atlan however, argues that in a biological environment, noise is vital to the system as a means of providing complexity from which genetic mutations might occur, enabling the system to change and adapt. As Atlan points out, this ability for information to be adapted or to be added to is something that is not accounted for in Shannon and Weaver’s original theory. Systems theory, cybernetics and information theory therefore represented radical moves away from the classical mechanistic models of the natural world that had held sway until then. In contrast to the Cartesian/Newtonian preference for studying objects in isolation from one another, these new models considered the processes and relationships between objects in a system to be as significant, if not more so, than the objects themselves. The systems view only made sense if one looked at the system as a whole. The classical reductionist methodologies were of little use in a systems theory paradigm, and so these new models required the development of a whole new set of tools. Some of these tools appeared in the form of the new domain of non-linear mathematics. Non-linear techniques provided a new way of dealing with the mathematics of complex natural systems; systems that didn’t fit neatly into the classical Newtonian paradigm. Rather than normalising data so that it would fit into a traditional linear mathematical model, non-linear techniques allowed iterations and feedback loops to be accounted for. Iterative and self-similar patterns are very commonly encountered in the natural world, so the new non-linear mathematics had direct relevance to real-life natural systems. The study of non-linear mathematics soon gave rise to two new fields of science: “chaos theory” and “complexity theory”. (Self-similar patterns are patterns that have structural similarity at different levels of magnification.) On to Part 4: Chaos theory, complexity theory and emergence Back to Table of Contents |
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