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jguillen's picture

Langston's Ant and Random Balls

  A couple of things from our last discussion struck me as very interesting. Langston's Exhibit, which at first seemed to be a relatively easy system and later on confused us by its path and the introduction of barriers, ended up highlighting an important idea-that what seems complex can arise from relatively simple interactions. It was very interesting trying to figure out the number and types of rules that were operating the system. After learning that there were simply two rules, we were able to see that this system contained an entity that was basically interacting in a very simple way and that even when a barrier was introduced the entity still kept on moving because it was simply operating in accordance to two rules. Now, an interesting question came out of the ant's movement. We asked if there was an internal goal in the entity and whether or not there needs to be a representation of the goal in the entity. What we seemed to conclude was that the entity was not learning and that it was just following two basic rules. Therefore, it seems that there does not need to be learning or a goal involved in a system in order for there to be complexity in a system. Something as simple as two rules can result in the emergence of complexity. Relatively simple interactions across different levels of organization give rise to what we consider as complex.

I decided to continue exploring this idea of interactions and complexity by looking at a model on Netlogo. I decided to look at the Random Balls Model, which simulates a frictionless billboard table. There are green balls that bounce off the walls, but not each other and which can be controlled in terms of their speed and number. In addition, the model includes a center of mass drawn in white and whose movement is traced by a red line. Without the red line, the pattern of motion is not as obvious. The gravity ball is pulled in the direction that contains most of the moving balls. If the number of balls in the box is increased, the gravity ball moves much less and seems to stay in the same general area. Its movement is now much smaller and the outlined red line path is significantly smaller than if you have less number of balls. Conversely, decreasing the number of balls significantly to about 13 results in much greater movement of the white gravity ball. With less number of balls, it seems that it is much easier for all of them to move to one corner than it is when there are 800 balls moving. Pressing setup changes the position of the green balls. If you change the set-up, but keep the number of balls the same, you get a different trajectory and pattern for the gravity ball. This means that there are many motion possibilities for the gravity ball even with the same number of starting balls. I would have liked to control the movement of the balls to one direction to see how this would affect the motion of the center ball, but I was unable to do so.

In both of these models, there is a sense of constant random change. The possibilities of both models seem endless. However, it seems like there is more predictability in this model than in the Ant's Exhibit because no matter how many balls there are or how fast the balls are moving, the movement of the gravity ball remains in the center of the square. In this sense, I found the Random Balls model to be missing the element of "surprise". In the Ant's Exhibit, the movement of the entity could be drastically modified with the introduction of a barrier. However, what this model and Langston's Ant Exhibit both illustrate is that the gravity ball and the entity (in Langston's Ant Exhibit) do not need to learn or have a goal in order to move or respond to the conditions. They are both simply following basic rules, which results in something that looks more complicated than it really is.


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