ntory, or whatever other variable is changed in the factory you would get widely differing results on a day to day basis from what was predicted. That is because a factory is a complex nonlinear system, like most systems found in nature. When most natural systems are modeled, their mathematical representations do not produce straight lines on graphs, and that the system outputs are extremely difficult to predict. Before the chaos theory was developed, most scientists studied nature and other random things using linear systems. Starting with the work of Sir Isaac Newton, physics has provided a process for modeling nature, and the mathematical equations associated with it have all been linear. When a study resulted in strange answers, when a prediction usually came true but not this one time, the failure was blamed on experimental error or noise. Now, with the advent of the Chaos theory and research into complex systems theory, we know that the "noise" actually was important information about the experiment. When noise is added to the graph results, the results are no longer a straight line, and are not predictable. This noise is what was originally referred to as the chaos in the experiment. Since studying this noise, this chaos, was one of the first concerns of those studying complex systems theory, Glieck originally named the discipline Chaos Theory. Another word that is vital to understanding the Complexity theory is complex. What makes us determine which system is more complex then another? There are many discussions of this question. In Exploring Complexity, Nobel Laureate Ilya Prigogine explains that the complexity of the system is defined by the complexity of the model necessary to effectively predict the behavior of the system. The more the model must look like the actual system to predict system results, the more complex the system is considered to be. The most complex system example is the weather, which, as demonstrated by Edw...
