Drawing on the richness of data and questions that arose out of agent-based simulations of the Artificial Anasazi Project that originated at SFI in the mid-1990s, SFI External Professor George Gumerman and Alan Swedlund (UMass Amherst) have taken their simulation one step further in a revised model they call the Artificial Long House Valley model.
Developed in collaboration with Lisa Sattenspiel and Amy Warren (both of the University of Missouri), the Long House Valley model, with its disaggregated, individual-level demographic processes, will lead to a better understanding of human-environment interactions and deeper insight into population structure in the Anasazi settlement in today’s northeastern Arizona.
“This second model focuses on individuals,” explains Swedlund, “so we’re able to address complex demographic processes in more dynamic ways.”
The researchers met at SFI recently to discuss and refine the new model as part of an SFI working group. The first Artificial Anasazi simulation relied on empirical archaeological and paleoenvironmental/climatological data to chart human population, distribution, growth, and decline in the Long House Valley region of the Colorado Plateau from 800-1350 AD. It provided a means for combining abundant multivariate data and testing scenarios with theoretical models for population growth and land abandonment under climatic stress.
With their new model, by building households from realistic probabilities based on individual-level data, the researchers aim to provide a more accurate and generalizable model for predicting outcomes in this and other archaeological populations. It’s also a model they hope to share with other researchers.
“We believe our continued efforts will help to address some of the grand challenges in archaeology, as outlined in a recent SFI workshop,” says Swedlund, “and that this individual-level, agent-based modeling approach will have real world applications in addressing questions about human responses to environmental stress.”
It might even offer the ability to better track processes such as disease risk, selection, and migration, he adds.