Their comments helped me to emend an earlier, drier version of this work that also had hints of raspberries and an oaky aftertaste, which I am afraid have been lost.
The growing use of computational models of social, physical, and biological systems raises many questions and concerns. Platforms such as SWARM enable researchers to construct detailed, robust computational models.
The availability of SWARM-like platforms will speed the pace of the computational revolution and open new areas of research. In this brief, tongue-in-cheek commentary, I discuss 26 topics pertaining to these computational models. Unbelievably, each of the 26 subject headings begins with a different letter! Given this fortuitous fact, I have chosen to arrange the topics alphabetically. Though containing bits of levity, this article should be read seriously both as a social scientist's commentary on a nascent field and as a guide to future research.
Applications: Michael Cohen has said that complexity research must move beyond the "festival of bad metaphors." With that in mind, I'll begin with a bad metaphor. During a recent trip across the country, I stopped in South Dakota where I saw legions of tourists filming Mount Rushmore. If videotaping a fixed object strikes you as odd (maybe they hoped to catch Washington's nose falling off on tape), consider the opposite: analyzing the flow of the Mississippi from a single aerial photo. (Incidentally, see "Rising Tide" by John Barry [1] for a detailed description of the madness that is the Mississippi.) In other words, the adage "don't saw a board with a hammer, and don't pound in a nail with a saw" also applies to scientific inquiry.
Computational models enable the study of complex, dynamic worlds because they themselves are dynamic. To understand complex, dynamic processes, it makes sense to construct dynamic models. 'Nuff said. It follows then that computational models should prove their worth in analyzing complex situations: environments