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Applying a suite of tools from artificial intelligence and data mining to existing archaeological data from Monte Alban, a prehistoric urban center, offers the potential for building agent-based models of emergent ancient urban centers. The authors use decision trees to characterize location decisions made by early inhabitants at Monte Alban, a prehistoric urban center, and inject these rules into a socially motivated learning system based on cultural algorithms. They can then infer an emerging social fabric whose networks provide support for certain theories about urban site formation. Specifically, we examine the period of occupation associated with the emergence of this early site. Our goal is to generate a set of decision rules using data-mining techniques and then use the cultural algorithm toolkit (CAT) to express the underlying social interaction between the initial inhabitants.