Сравнение методов

	Agency Detection Task	Moralizing Gods Database Analysis
Область	Religious Studies	Religious Studies
Семейство	Process / pipeline	Regression model
Год появления	2000	2015
Автор метода	Justin L. Barrett (building on Stewart Guthrie)	Peter Turchin and the Seshat: Global History Databank team
Тип	Signal-detection experiment for agency attribution	Cross-cultural quantitative database analysis
Основополагающий источник	Barrett, J. L. (2000). Exploring the natural foundations of religion. Trends in Cognitive Sciences, 4(1), 29-34.	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.
Другие названия	HADD Experiment, Agency Detection Bias Task, Hypersensitive Agency Detection Device Test, Agency Attribution Paradigm	Big Gods Database Analysis, Seshat Moralizing-Gods Analysis, Cross-Cultural Big Gods Modeling, Moralizing High Gods Coding
Связанные	3	3
Сводка	The agency detection task is an experimental method in the cognitive science of religion that measures the human tendency to attribute ambiguous events to intentional agents - a tendency Justin Barrett named the Hyperactive (or Hypersensitive) Agency Detection Device, or HADD. Building on Stewart Guthrie's argument that people anthropomorphize the world, Barrett proposed in 2000 that an evolved bias to err on the side of detecting agents (better to mistake the wind for a predator than the reverse) provides a natural cognitive foundation for belief in gods, spirits, and ghosts. The task presents participants with ambiguous motion, sounds, or images and uses signal-detection theory to separate genuine sensitivity to agents from a liberal response criterion, then relates the resulting over-detection bias to supernatural belief.	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.