This paper presents an object-oriented approach that tightly integrates multiple modeling techniques and GIS in order to investigate route selections made by pedestrians in an ancient urban environment. Our applied method allows the integration of agent-based (ABM) and human metabolic models in order to enable different agent types, based on age and sex cohorts, to select street network routes that access different urban structures. The case study we apply our method to is a settlement in Turkey that has a known street network and building structures; however, data on social decisions affecting route selection by pedestrians are missing. Despite the lack of social data, the results provide researchers with a useful and reasonable assessment of past pedestrian traffic volume. Such results can then be used to determine areas of potential archaeological significance and direct further investigations through field excavations or other archaeological techniques. We provide initial fieldwork validation of our simulation results, demonstrating our technique’s utility for addressing realistic research goals.

Archaeologists and other scholars interested in the past have begun to explore issues in ancient societies by incorporating new computer simulation techniques in their research. Among the popular approaches, agent-based methodologies are commonly used to answer questions involving the interactions of natural and social systems. Building upon such efforts, a new object-oriented simulation framework is being developed for ancient Mesopotamia that can assemble and execute highly complex simulation scenarios. This framework can be used to investigate socioecological interactions over a broad range of social, spatial, and temporal scales, allowing for a wide range of past socioecological issues to be concurrently addressed. Such capabilities are enabled by simulation tools that are flexible, scalable, and highly expressive. Perhaps the greatest promise of these tools for studying the past is their potential for enabling scholars to gain new insights by testing various theoretical hypotheses and perspectives in social-natural systems in a rich socioecological simulation context.

Agent-based models (ABMs), perhaps used along with other methods, are increasingly being applied to address problems involving social-ecological systems (SES). However, systematic and standardized techniques for organizing data requirements and collecting information for such models are generally not applied. Defining the types of data that need to be collected is a critical step in instantiating relevant models and detailing behavioral patterns. By organizing data needs into pertinent categories, researchers can focus efforts to make data collection more efficient, provide a clear record of gathered data, conserve project resources, and create models more easily. In this paper, we present a formal method, which we call Delineate, Structure, and Gather (DSG), that has been applied to guide the determination of data requirements, structure data needs, and enable data collection for the development of ABMs addressing SES. The presented framework continues and supplements existing efforts that provide standardization and protocol techniques for constructing ABMs. An active project is presented in order to demonstrate how DSG can be implemented to address SES issues.

Understanding patterns of conflict and pathways in which political history became established is critical to understanding how large states and empires ultimately develop and come to rule given regions and influence subsequent events. We employ a spatiotemporal Cox regression model to investigate possible causes as to why regions were attacked by the Neo-Assyrian (912-608 BCE) state. The model helps to explain how strategic benefits and costs lead to likely pathways of conflict and imperialism based on elite strategic decision-making. We apply this model to the early 9th century BCE, a time when historical texts allow us to trace yearly campaigns in specific regions, to understand how the Neo-Assyrian state began to re-emerge as a major political player, eventually going on to dominate much of the Near East and starting a process of imperialism that shaped the wider region for many centuries even after the fall of this state. The model demonstrates why specific locations become regions of conflict in given campaigns, emphasizing a degree of consistency with which choices were made by invading forces with respect to a number of factors. We find that elevation and population density deter Assyrian invasions. Moreover, costs were found to be more of a clear motivator for Assyrian invasions, with distance constraints being a significant driver in determining where to campaign. These outputs suggest that Assyria was mainly interested in attacking its weakest, based on population and/or organization, and nearest rivals as it began to expand. Results not only help to address the emergence of this empire, but enable a generalized understanding of how benefits and costs to conflict can lead to imperialism and pathways to political outcomes that can have major social relevance.