NASER_1



In relation to complexity, I find this to be a good example of surprising results achieved by a simple set of rules. The discontinuous nature of the arc segments gives a sense of disorder. It also gives the viewer a better understanding of the structure behind the drawing than looking at a single instance of the system. What is interesting about this model is the input and output that the viewer has total control of; in addition, the amount of input and output that is been taken total control of, can take the model from its original state to a completely different state yet still maintain its balanced grid structure, which through the change of a very small numerical value via the communication between the arcs results in a massive change in the field.



Another example that I found interesting, which is this intersecting circles. The circles in this model initiate with a radius of a very minute number and gradually increase to a much larger size. The circles are drawn with small moving dots along the perimeter. In regards to translation, the intersecting dots are rendered as glowing orbs. Different from the first example, in this case the circles are actually free to move around from the restricting grid; as a result, the communication flow is not continuous, it actually breaks at points and overlaps at other. What I find very complex about this model the two scales that are playing at the same time as the circles are maneuvering around. Also, the overlapping/intersecting action that is happening is allowing the circles to create a build up of orbs translated in the over glow as an expression and the disappearance/fading of these orbs at some instances because the highlighted intersections takes over and becomes the foreground communicating layer. In other words, as this model was transitioning from state to state, it dislodged a layer of information and allowed a new one to come in, which in a sense, it had to destroy to change and survive.



In this example, attraction/gravitation is the motive that permits change whether in terms of input/addition or output/elimination. Complexity in this case is resorting in the idea of having multiple attraction points which ultimately means that the input/addition and output/elimination happens several times at the same moment, which causes to have the model being interrupted. Now consequently, that interruption may become the vehicle for this model to find stability given the time that allows that, or unfortunately becomes unstable and never resolved.