Agent-based modeling and complexity science: the next step in archaeological theory?
Stefani A Crabtree
Abstract Agent-based modeling is on the rise in archaeology; yet how do we decide what questions are appropriate to ask when using this tool? Are all study areas, and indeed all questions, best approached through an agent-based modeling framework? If not, which questions are best answered through this approach? In this paper I place complexity science approaches within the framework of canons of archaeological literature. I then explore which questions are best asked, and answered, through agent-base modeling approaches. Finally I demonstrate how a simple agent-based model of southern France helps clarify standing questions. Finally, I briefly introduce a more complex model to show how agent-based modeling can articulate with both simple theoretical models and complex realistic models. Processual archaeology favored an approach that looked at how variables, such as societies, or levels of hierarchy, or maximal size of community, led to the construction of the archaeological record. Post-processualism, on the other hand, favored exploring the individual experience in the past, which can confuse an understanding of overarching structures. Complexity approaches, however, honor the individual experience by showing how individual decisions do matter, yet allow these individuals to interact to create larger overarching structures. This articulation of the micro-scale to the macro-scale feeds into how to approach archaeology questions with complexity tools. I take a step-by-step approach to showing which questions agent-based modeling can directly examine, and how they can be useful for answering questions that had been posed by processualists and post-processualists alike. I then demonstrate these questions through two models: a simple model of resource trade in southern France, and a complex model of the development of hierarchy in the U.S. Southwest. These two models are on the opposite spectrum from each other in terms of intricacy, yet they succinctly demonstrate how ABM approaches can examine diverse questions.
