Thinking Exponentially: Settlement Scaling and Archaeological Data

Author(s): Scott G. Ortman

Year: 2017


Archaeologists are used to thinking linearly, where sample measures can be well-characterized by a mean, a standard deviation or a proportion. Settlement scaling theory requires us to think exponentially, where all these summary measures change with the scale of the settlement from which they derive. This sounds like a big problem, but once one gets used to it many traditional concerns about the quality of archaeological data turn out to not be all that important, and the archaeological record becomes a strong resource for the investigation of human societies as complex systems. In this paper, I consider the ramifications of thinking exponentially for the data requirements of scaling research in archaeology. I discuss issues related to the development of population proxies, the way error in measurement works at logarithmic scales, and the variety of proxies for socioeconomic rates that are potentially obtainable from archaeological contexts. I also illustrate these issues using data from across the New World. My goal is to show that typical archaeological data are actually surprisingly good, in the big picture, for scaling research.

Cite this Record

Thinking Exponentially: Settlement Scaling and Archaeological Data. Scott G. Ortman. Presented at The 81st Annual Meeting of the Society for American Archaeology, Vancouver, British Columbia. 2017 ( tDAR id: 429173)

Spatial Coverage

min long: -115.532; min lat: 30.676 ; max long: -102.349; max lat: 42.033 ;

Record Identifiers

Abstract Id(s): 14390