Theoretical Applications in Sociology: Critical Theory vs. Systems Theory
Exciting advances are being made in the development and application of sociological theories to social work practices. Two of the foremost theories are systems theory -- currently undergoing an architectural evolution due to the implications of the chaos and complexity disciplines -- and critical theory, which seeks to change the current systematical frameworks of society for the better. For the purposes of this paper, I will compare and contrast the two theories, highlighting the similarities between them as well as the segments of departure and their practical implications. I will then apply both theories to a single case study in order to show the strengths and weaknesses of each theory in practice.
In "New Directions in Systems Theory: Chaos and Complexity," authors Warren, Franklin and Streeter examine how advances in the chaos and complexity disciplines are instigating an evolution of traditional systems theory. While in the past, systems theorists have typically argued that systems are rational, orderly, and essentially stable as governed by "boundaries, homeostasis, and equilibrium" chaos and complexity theorists argue that the system only becomes rational and orderly after a preceding stage of chaos, and that the most complex systems -- such the human family -- exist "on the edge of chaos and order" as opposed to a completely chaotic or completely orderly sphere (Warren, Franklin & Streeter, 1998). The chaos and complexity disciplines arose from the need to understand changes in systems that don't necessarily adhere to linear cause and effect models, i.e. nonlinear dynamics. Rather than A leading to a proportional B. As is the case in a linear cause and effect model, nonlinear dynamics refers to cases in which A and B. are disproportional and essentially unpredictable. Warren et al. put forth the example of a mounting argument between a child and parent. While in a linear system, the child's rise in pitch would result in a proportional rise in pitch in the parent's voice, in a nonlinear system, the child's rise in pitch could result in silence, no change in the parent's voice, or a disproportionally high rise in the pitch of the parents voice. The argument here is that the tiniest difference in pitch as perceived by the parent could result in any number of reactions, hence the erratic and unpredictable nature of nonlinear systems. Put in more general terms, the smallest change or modification of A -- the cause -- can result in a number of possible altercations of B -- the effect -- from the truly proportional to the outlandishly disproportional.
Another example of the often disproportional relationship of cause and effect was the introduction of VHS vs. Beta videotape players in the 1980s. As Warren et al. explain, while VHS and Betas was introduced to the consumer market at around the same time and sales were initially proportional between them, at some point VHS sales slightly exceeded Beta player sales, resulting in more people buying VHS videotapes for their new VHS players, which in turn resulted in video stores stocking more VHS tapes than Beta tapes, which in turn led to more people buying VHS players in order to watch the VHS tapes now available at their local video stores. In the end, the Beta videotape and videotape player disappeared from the market altogether, and the VHS prevailed as the predominant format of home movie viewing. This is an example how the slightest change in a cause can inevitably snowball, resulting in a dramatic and disproportional change in its effect (Warren et al., 1998).
In more human terms, Warren et al. cite the human learning process as an example of a change in a cause snowballing to a disproportional effect. According to child psychologist Jean Paiget, as the mind of a child develops it continually integrates new knowledge as received from its environment with existing knowledge, resulting in the practiced ability to acquire knowledge more easily, with in turn results in the ability to perform higher thought processes, further enhancing the ability to learn. In other words, the more you learn, the easier it is to learn, which in turns allows you learn more. This is an example of chaos and complexity theorists call "nonlinear growth" (Warren et al., 1998)
The process of nonlinear growth does not continue forever. Rather, after continuing on in this chaotic and predictable fashion for a certain time, the system with eventually attempt to stabilize and reorganize itself, resulting...
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