Issues of Representation and Interpretation for Agent-based Models of Complex Adaptive Spatial Systems

What we know of the world around us is, in large measure, the product of reductionist science. The basic tenets of this approach tell us that truth can be found through an understanding of individual system components; a system is the sum of its parts. While this approach served us well through much of the 20 century, many scientists now believe that a reductionist approach alone is insufficient for the study of natural and social systems. These scientists promote a new approach based on complexity theory and complex adaptive systems (CAS) that is focused as much on the linkages among system components as the components themselves; a science where the underlying assumption is that a system can be more than the some of its parts. Traditional scientific methods, however, are often ill-suited to the study of complex systems characterized by feedbacks and non-linear dynamics, path dependency, adaptation, cross-scale interaction, self-organization, emergent behavior, and dissipative processes. Agent-based modeling (ABM) has been highly touted as an appropriate technique for the study of complex adaptive spatial systems (CASS).