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/Iss. 115(2) National Academy of Sciences Washington, D.C Published In Pages: 144-151
By Turchin, Peter, Currie, Thomas E., Whitehouse, Harvey, François, Pieter, Feeney, Kevin, Mullins, Daniel, Hoyer, Daniel, Collins, Christina, Grohmann, Stephanie, Savage, Patrick E., Mendel-Gleason, Gavin, Turner, Edward, Dupeyron, Agathe, Cioni, Enrico, Reddish, Jenny, Levine, Jill, Jordan, Greine, Brandl, Eva, Williams, Alice, Cesaretti, Rudolf, Krueger, Marta, Ceccarelli, Alessandro, Figliulo-Rosswurm, Joe, Tuan, Po-Ju, Peregrine, Peter N., Marciniak, Arkadiusz, Preiser-Kapeller, Johannes, Kradin, Nikolay, Korotayev, Andrey V., Palmisano, Alessio, Baker, David, Bidmead, Julye, Bol, Peter, Christian, David, Cook, Connie, Covey, Alan, Feinman, Gary M., Júlíusson, Árni Daníel, Kristinsson, Axel, Miksic, John, Mostern, Ruth, Petrie, Cameron, Rudiak-Gould, Peter, ter Haar, Barend, Wallace, Vesna, Mair, Victor, Xie, Liye, Baines, John, Bridges, Elizabeth, Manning, Joseph, Lockhart, Bruce, Bogaard, Amy, Spencer, Charles


Using the compiled database "Seshat: Global History Databank," researchers sampled 30 societies from 10 distinct regions of the world, testing 51 variables that were condensed into 9 "complex characteristic" variables. Researchers tested for correlates in how societies evolve structurally. Utilizing principal component analysis it was demonstrated that the complex characteristic variables were strongly associated, leading to theorization of structural and social evolution predictability.


Variables were correlated with each other, coefficients ranging from 0.49 - 0.88 (146). 9 principal component analyses resulted in 1 principal component, which explains 77.2 +/- .04% of variance.


Sample Used Coded Data Comment
Seshat: Global History DatabankResearcher's own

Documents and Hypotheses Filed By:noah.rossen