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Moralizing Gods Database Analysis

Moralizing gods database analysis is a cross-cultural quantitative method that codes the presence of moralizing or 'big' supernatural enforcers and measures of social complexity across many historical polities over time, then models their relationship. The exemplary infrastructure is the Seshat: Global History Databank, introduced by Peter Turchin and colleagues in 2015, which records hundreds of polities on standardized variables - population, territory, hierarchy, infrastructure, information systems, and religious features - with explicit sources and uncertainty codes. A high-profile 2019 Nature paper using Seshat data argued that complex societies tend to precede moralizing gods; that paper was retracted in 2021 over its treatment of missing data. The method is therefore best understood not as a settled finding but as a databank-driven analytical pipeline whose results depend critically on coding decisions, missing-data handling, and modeling of temporal and phylogenetic dependence.

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Sources

  1. Turchin, P., Brennan, R., Currie, T., et al. (2015). Seshat: The Global History Databank. Cliodynamics: The Journal of Quantitative History and Cultural Evolution, 6(1), 77-107. DOI: 10.21237/C7clio6127917
  2. Whitehouse, H., et al. (2019). Complex societies precede moralizing gods throughout world history. Nature, 568(7751), 226-229. [RETRACTED 2021]. DOI: 10.1038/s41586-019-1043-4
  3. Whitehouse, H., et al. (2021). Retraction Note: Complex societies precede moralizing gods throughout world history. Nature, 595, E9. DOI: 10.1038/s41586-021-03656-3

How to cite this page

ScholarGate. (2026, June 23). Cross-Cultural Database Analysis of Moralizing Gods and Social Complexity. ScholarGate. https://scholargate.app/en/religious-studies/moralizing-gods-database-analysis

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ScholarGateMoralizing Gods Database Analysis (Cross-Cultural Database Analysis of Moralizing Gods and Social Complexity). Retrieved 2026-06-24 from https://scholargate.app/en/religious-studies/moralizing-gods-database-analysis · Dataset: https://doi.org/10.5281/zenodo.20539026