Archeological theory and AI: the potential past through agentic systems
TL;DR
Using the phenomenology of agent-based modeling (ABM), it is argued that the incorporation of agentic AI methods that facilitate the automation and minimal human intervention in the application of social behavior represents the next challenge and stage of AI in understanding the past.
Abstract
The increasing capabilities of AI in automation and integration within existing methodology present a new opportunity for disciplines to address long-standing theoretical challenges. This is evident in archaeology, where persistent theoretical debates and interpretive variability present prime opportunities for applying AI-based techniques to address uncertainty. Using the phenomenology of agent-based modeling (ABM), we propose the incorporation of agentic AI methods that facilitate the automation and minimal human intervention in the application of social behavior, where perceptions, values, interaction, and cognition evolve within applied systems. We argue this represents the next challenge and stage of AI in understanding the past, one where applications can move beyond the case-specific and narrow technical results that generative and discriminative AI have been mostly applied toward. For AI to better understand potential pasts, new emergent behavioral outcomes that are better able to address long-standing theoretical debates are needed. Practical applications related to a variety of sub-areas within archeology, including chaîne opératoire , lived experience, logistics, governance, and other applications, are just some examples where closer ABM–AI integration using agentic techniques could better address.