The agent-based model of an ant colony provides us with a “glass box” (as opposed to a “black
box”), through which we can examine and observe the operations of the ants, and discuss and
test whether or not hypothesized mechanisms are valid.
As I stated in class, I’m not one of those students that is simply satisfied with being told to use an equation. I have to know why it works. I have to build it using what I know to be true. If I can derive the equation from scratch I know I can do it again any time I wish. If I cannot derive the equation I know that there is a piece of information that I lack and I learn it in the process. If I had simply memorized the equation I would not have found this lack of information. This is probably why I had a deep distrust of physics.
Most physics require the user to not only memorize and equation, but memorize the conditions when such an equation would be accurate. I remember when I was learning about velocity and the force of gravity. Given a force and an angle I was told that I could accurately predict where a projectile would fall. To illustrate this we used nerf guns and predicted where the darts would lie. I tried that dammed experiment several times and the dart never hit my estimate. When I asked the professor about the anomaly he simply stated that it was because we had left out several factors. There was air resistance, the force of friction of the dart against the gun, and the fact that the force used was just an estimate of the true force of the gun. He explained that if I was in a world with frictionless guns and no air that the dart would always hit my estimate. He failed to mention that these conditions do not exist, and, more importantly, that without air I would probably die.
The second paper touched on this concern. While models are a great approximation of the world they often do not take into account things like air resistance. This is troublesome since our world will never lack air. I think that the idea of using sensors to make models true to their natural equivalent is an excellent step in the right direction and keeps these models from assuming things that would never really occur.