Moralizing Gods Database Analysis

Neither has been established by this line of work. A 2019 Nature paper reported that social complexity tended to precede moralizing gods, but the paper was retracted in 2021 after critics demonstrated that the finding depended on coding unknown (missing) values as 'absent.' The corrected analyses did not robustly support the original precedence claim. The honest current position is that the temporal and causal relationship remains contested; the method provides a framework for testing it, not a settled answer. Any presentation of the 2019 result should note its retraction.

Seshat: the Global History Databank is a structured, expert-curated collection of time-coded data on hundreds of historical polities, described by Turchin and colleagues in 2015. For each polity at each time it records standardized variables - population, territory, hierarchy, infrastructure, information systems, ritual, and religious features - each tied to documented sources and explicit uncertainty codes. It is central because it makes systematic cross-cultural, longitudinal comparison feasible and auditable; the quality of any moralizing-gods analysis is inherited from how carefully these codes, and especially their uncertainty, are handled.

Because the historical record is full of gaps, and whether you treat a gap as 'the feature was absent' or 'we do not know' can change the answer. Coding all unknowns as absences artificially pushes the apparent first appearance of a feature earlier or later and distorts timing comparisons. This is exactly what led to the 2021 retraction of the 2019 Nature paper. Best practice keeps an explicit unknown category, never equates missing with absent, uses principled imputation, and reports how conclusions shift under alternative assumptions through sensitivity analyses.