The late A.D. 1200s depopulation of the Mesa Verde region of the American Southwest is one of the great mysteries of American archaeology. Many mechanisms have been proposed to account for this rapid out-migration of regional populations. Most suggest increasingly severe resource imbalances across a densely populated landscape. Some accepted research, however, shows that potential maize production was sufficient to support the estimated populations of the time. If these populations emigrated due to resource scarcity, then scarcity of other resources must have contributed to decisions to leave. On the other hand, there are hints of important changes in sociopolitical organization just prior to the depopulation.
This famous depopulation is one of the riddles that the Village Project addresses. The project was undertaken to examine the interaction of simulated agrarian households with their natural environment taking in to account the production and consumption of various natural resources essential for everyday life. By evaluating the possibility of crises in factors such as potable water, woody fuels, and protein, this research will help determine whether resource factors were in fact critical in these decisions, or whether social factors may have largely influenced the exodus.
See our Research Plan for other problems we are addressing.
VEPII North and South Maize Growing Niches
This is a video of the maize growing niches in the VEPII North and South study areas. The upper panels are VEPII North, and the lower panels are VEPII South. The leftmost panels show water year (October -- September) precipitation; the color break and black contour are at 30 cm, commonly considered the minimum water year precipitation for dry land farming.
Drain your DEMs!Submitted by Kyle Bocinsky on Fri, 09/27/2013 - 14:49
Since the 1990s there has been a marked increase in interest in computational approaches—including simulation—by social science researchers. This appears to be driven both by a cross-disciplinary interest in the sciences of complexity and the ever-increasing computational capacity at our disposal.
In the past, due to the complexity of the phenomena involved, we have been forced to use simplistic world models. Today we are able to study a world in which most important phenomena emerge from the non-linear interaction of many agents (physical, biological, or social) in systems that are rarely at equilibrium.
This vision promotes a method—agent-based modeling—that provides a computational environment in which the behaviors of such systems can be studied.