”Machines take me by surprise with great frequency.” Alan Turing, found here. When thinking about emergence, my mind ping pongs between three different issues. The first two, my overwhelming distrust of Wolfram and explorations of the top down dichotomy, I will save for another post. Since we are still discussing agent based modeling, I will stick to my third fury. From the first day of class, I have been troubled by the idea that in making computer models, our objective “is to be ‘surprised’, to ‘surprise’ others, to establish that some pattern/phenomenon that is presumed to depend on complexity/planning/a directive element can be produced without that. To show what might be, rather than what is.” This has been a recurring theme in lecture and it just does not sit well with me. Why is it that models cannot be used to solve problems? Why don’t they portray what is? This rankles me. If the modeling method has no utility beyond surprise, then it is little more than an intellectual jack in the box: entertaining and beautiful but not appropriate for solving problems in a science class (see my icon? ). But modeling IS useful for more than just surprises. And I’m not the only one who thinks so. I went on a hunt for people solving real world problems with these models. I found this article quite helpful. They touch on a lot of the interdisciplinary aspects of modeling. In this study, modeling is used to evaluation very pertinent social problems.This group even focuses on applications of modeling in the social sciences. There are tons of ways in which models are useful for thinking about everyday life. After all, I guess that’s why we’re studying emergence in this class. To me, modeling is more akin to a pure thought experiment (The general relativity creeps in everywhere). A thought experiment, loosely defined is a way of testing a hypothesis without doing a physical experiment. A lot of special and general relativity relies on thought experiments used in place of actual experiments, since we do not have the ability to directly observe a lot of these phenomena easily. Like thought experiments and mathematics, aren’t models also used as a tool to extend the possibilities of thought? Do they translate into pure thought? After all, as I read in several social science sites, models also provide a way of simulating real world social situations. Are they the next best thing to a human (instead of particle) accelerator? All of these uses are fascinating and seem to me very important. At the very least, they widen the definition and purpose of models from the minimalist jack in the box. Maybe my problem with the idea of surprise is just semantics. After all, if Alan Turing (above) thought that computational surprises were worth mentioning, well then they probably are. But the importance of the surprise seems to me overshadowed by the importance of its implications. I definitely think that limiting the goal/definition of modeling to its ability to surprise is, well, limiting. It may be a characteristic of some models, but it’s not the purpose of the discipline. I'm afaid I may be obsessing over a detail, but the topic of surprise has come up so much, I just had to get it out of my head.