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Qualifying the Complexity Theory
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How complex is the Complexity Theory? Why is the world so interesting to many of us and why are we so interested in trying to figure out how to understand the natural world around us? We study things such as the forces of nature, how they are involved in the universe, and when, where, and why certain things happen. From here we form equations representing the data we have discovered and the relationships of the forces we are studying. Then we try to calculate future states of the force or it’s surrounding and begin to form hypothesized theories that are consistent with what we have found. Yet still the questions of epistemology remain: How do we really know these things? How certain are we about knowing what we think? And are there things that we just can’t ever know? The scientific significance of the Complexity theory is unlike humanities and art we can take numbers from random and put them into mathematical equations that we produce to get the results of a system. What the Complexity Theory is, is a way of looking at natural systems throwing out the traditional techniques used in scientific work today. But can this be called science or is it the basis for us to consider it a theory? Lets breakdown the theory and look at the essential components of a complex system and see if we can qualify the theory. To define the Complexity Theory it has been said that a complex system is a functional whole that consists of interdependent and variable parts (Kauffman, 1995). What this means is that unlike normal systems where parts and variables have fixed relationships and behaviors, a complex system is something that doesn’t have or need fixed relationships or behaviors, the individual functions are what make up and form the system and it’s relationships and behaviors. Within this theory there are four types of complexity, which need to be broken down. First is static complexity, the simplest form in the theory and is generally used by both mathematicians and scientists. This type of complexity makes the assumption that what we are interested in is like a photograph in that it doesn’t change over time or space (Kauffman, 1995). So mathematicians and scientists look at systems by knowing they are complex but it doesn’t mean they can’t compare and relate to other systems to determine the complexity of the system. What this means is that looking at a life form they make measurements by number of cells and genes, but this doesn’t answer the question of complexity and why one system with 25 cells differs from another with the exact same amount. This is why the second type of complexity brings in what might be considered the fourth dimension. The second type of complexity is called dynamic complexity and it brings the dimension of time, which improves and also makes the situation worse (Kauffman, 1995). In making the situation better it recognizes set patterns such as a heartbeat, but it doesn’t recognize the identifications we can give the system such as placing systems into categories or classifying them. How this makes the situation worse is by science relying so heavily on being able to test and confirm what might be going on, we must be able to know whether a system is either static or some kind of cycle.
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