Although Gregory Johnson’s models have influenced social theory in archaeology, few have applied or built upon these models to predict aspects of social organization, group size, or fissioning. Exceptions have been limited to small case studies. Recently, the relationship between a society’s scale and its information-processing capacities has been explored using the Seshat Databank. Here, I apply multiple-linear regression analysis to the Seshat data using Turchin and colleagues’ 9 “complexity characteristics” (CCs) to further examine the relationship between the hierarchy CC and the remaining 8 CCs which include both aspects of a polity’s scale and aspects of what Kohler et al. call “collective computation”. The results support Johnson’s ideas that stratification will generally increase with increases in a polity’s scale (population, territory); however, stratification is also higher when polities increase their developments in information-processing variables such as texts.
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First, the author would like to thank Tim A. Kohler, David H. Wolpert, and Darcy Bird for their encouragement to participate in this working group and subsequent special issue. Kohler and Bird provided many constructive comments on an earlier draft which significantly improved the quality of this paper. The author would also like to thank Erin Thornton and Andrew Duff for their comments. The author would also like to thank two anonymous reviewers for their constructive feedback. Most importantly, the author commends all of those involved in the production and curation of the Seshat Databank, without whom this research would not have been possible. This research was supported by a dissertation fellowship from the Graduate School at Washington State University and by the National Science Foundation (No. SMA-1620462) to the Santa Fe Institute and Washington State University. All R code and related data files are available for download via Github (https://github.com/LJEllyson/JSocCo_SpecialIssue).
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