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Applying Gregory Johnson’s Concepts of Scalar Stress to Scale and Information Thresholds in Holocene Social Evolution
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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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Applying Gregory Johnson’s Concepts of Scalar Stress to Scale and Information Thresholds in Holocene Social Evolution
)
Abstract
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.
References(43)
L. V. Bertalanffy, An outline of general system theory, The British Journal for the Philosophy of science, vol. 1, no. 2, pp. 134–165, 1950.
L. V. Bertalanffy, General system theory – A new approach to unity of science, Human Biology, vol. 23, no. 4, pp. 303–361, 1951.
H. A. Simon, The proverbs of administration, Public Administration Review, vol. 6, pp. 53–67, 1946.
K. J. Meier and J. Bohte, Ode to Luther Gulick: Span of control and organizational performance, Administration&Society, vol. 32, no. 2, pp. 115–137, 2000.
P. E. Slater, Contrasting correlates of group size, Sociometry, vol. 21, pp. 129–139, 1958.
K. V. Flannery, The cultural evolution of civilizations, Annual Review of Ecology and Systematics, vol. 3, no. 3, pp. 399–426, 1972.
H. T. Wright and G. A. Johnson, Population, exchange, and early state formation in Southwestern Iran, American Anthropologist, vol. 77, no. 2, pp. 267–289, 1975.
R. I. Dunbar, Coevolution of neocortical size, group size and language in humans, Behavioral and Brain Sciences, vol. 16, no. 4, pp. 681–694, 1993.
R. A. Hill and R. I. M. Dunbar, Social network size in humans, Human Nature, vol. 14, no. 1, pp. 53–72, 2003.
G. Alberti, Modeling group size and scalar stress by logistic regression from an archaeological perspective, PLOS One, vol. 9, no. 3, p. e91510, 2014.
M. S. Bandy, Fissioning, scalar stress, and social evolution in early village societies, American Anthropologist, vol. 106, no. 2, pp. 322–333, 2004.
W. Bernardini, Kiln Firing Groups: Inter–household economic collaboration and social organization in the Northern American Southwest, American Antiquity, vol. 65, no. 2, pp. 365–377, 2000.
W. Bernardini, Transitions in social organization: A predictive model from southwestern archaeology, Journal of Anthropological Archaeology, vol. 15, no. 4, pp. 372–402, 1996.
J. Shin, M. H. Price, D. H. Wolpert, H. Shimao, B. Tracey, and T. A. Kohler, Scale and information–processing thresholds in Holocene social evolution, Nature Communications, vol. 11, no. 1, pp. 1–8, 2020.
P. Turchin, Fitting dynamic regression models to Seshat Data, Cliodynamics:The Journal of Quantitative History and Cultural Evolution, vol. 9, no. 1, pp. 25–58, 2018.
P. Turchin, T. E. Currie, H. Whitehouse, P. François, K. Feeney, D. Mullins, et al., Quantitative historical analysis uncovers a single dimension of complexity that structures global variation in human social organization, Proceedings of the National Academy of Sciences, vol. 115, no. 2, pp. E144–E151, 2018.
P. Turchin, R. Brennan, T. Currie, K. Feeney, P. Francois, D. Hoyer, et al., Seshat: The global history databank, Cliodynamics:The Journal of Quantitative History and Cultural Evolution, vol. 6, no. 1, pp. 77–107, 2015.
O. E. Williamson, Hierarchical control and optimum firm size, Journal of Political Economy, vol. 75, no. 2, pp. 123–138, 1967.
S. A. Crabtree, R. K. Bocinsky, P. L. Hooper, S. C. Ryan, and T. A. Kohler, How to make a polity (in the Central Mesa Verde region), American Antiquity, vol. 82, no. 1, pp. 71–95, 2017.
M. Ember, The relationship between economic and political development in nonindustrialized societies, Ethnology, vol. 2, pp. 228–248, 1963.
P. Brown and A. Podolefsky, Population density, Population density, agricultural intensity, land tenure, and group size in the New Guinea highlands, Ethnology, vol. 15, pp. 211–238, 1976.
M. M. Dow and E. A. Eff, Cultural trait transmission and missing data as sources of bias in cross–cultural survey research: Explanations of polygyny re–examined, Cross–Cultural Research, vol. 43, no. 2, pp. 134–151, 2009.
M. M. Dow and E. A. Eff, Multiple imputation of missing data in cross–cultural samples, Cross–Cultural Research, vol. 43, no. 3, pp. 206–229, 2009.
E. A. Eff and M. M. Dow, How to deal with missing data and Galton’s problem in cross–cultural survey research: A primer for R, Structure and Dynamics, vol. 3, no. 3, pp. 223–251, 2009.
J. N. Wulff and L. Ejlskov, Multiple imputation by chained equations in praxis: Guidelines and review, The Electronic Journal of Business Research Methods, vol. 15, no. 1, pp. 2017–2058, 2017.
S. Van Buren and K. Groothuis–Oudshoorn, MICE: Multivariate Imputation by Chained Equations in R, Journal of Statisctical Software, vol. 45, no. 3, pp. 1–67, 2011.
A. M. Wood, I. R. White, and P. Royston, How should variable selection be performed with multiply imputed data? Statistics in Medicine, vol. 27, no. 17, pp. 3227–3246, 2008.
J. R. Van Ginkel, Significance tests and estimates for R2 for multiple regression in multiply imputed datasets: A cautionary note on earlier findings, and alternative solutions, Multivariate Behavioral Research, vol. 54, no. 4, pp. 514–529, 2019.
G. E. Hendershot, Population size, military power, and Antinatal policy, Demography, vol. 10, no. 4, pp. 517–524, 1973.
C. R. Hinings and A. Bryman, Size and the administrative component in churches, Human Relations, vol. 27, no. 5, pp. 457–475, 1974.
W. R. Haas Jr, C. J. Klink, G. J. Maggard, and M. S. Aldenderfer, Settlement–size scaling among prehistoric hunter–gatherer settlement systems in the New World, PlOS One, vol. 10, no. 11, p. e0140127, 2015.
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Acknowledgements
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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