M. Willis Monroe, Rachel Spicer, Gino Canlas, Travis Chilcott, Stephen Christopher, Megan Daniels, Andrew J. Danielson, Matthew Hamm, Caroline Arbuckle MacLeod, William Noseworthy, Ian Randall, Robyn Faith Walsh, Michael Muthukrishna, Edward Slingerland. “On the Category of ‘Religion’: A Taxonomic Analysis of a Large-Scale Database,” (PDF) Journal of the American Academy of Religion (in press)

Edward Slingerland, M. Willis Monroe and Michael Muthukrishna. “The Database of Religious History (DRH): Ontology, Coding Strategies and the Future of Cultural Evolutionary Analyses.” Religion, Brain and Behavior (published online May 28 2023).

Beheim, Bret, Quentin Atkinson, Joseph Bulbulia, Will Gervais, Russell Gray, Joseph Henrich, Martin Lang, M. Willis Monroe, Michael Muthukrishna, Ara Norenzayan, Benjamin Purzycki, Azim Shariff, Edward Slingerland, Rachel Spicer, Aiyana Willard. 2021. “Treatment of missing data determines conclusions regarding moralizing gods,” (PDF) Nature 595: E29-34. *

This Matters Arising critiques a 2019 Nature article by Whitehouse, et al. (since retracted) that used the Seshat archaeo-historical databank to argue that beliefs in moralizing gods appear in world history only after the formation of complex “megasocieties” of around one million people. Inspection of the authors’ data shows that 61% of Seshat data points on moralizing gods are missing values, mostly from smaller populations below one million people, and during the analysis the authors re-coded these data points to signify the absence of moralizing gods beliefs. When we confine the analysis only to the extant data or use various standard imputation methods, the reported finding is reversed: moralizing gods precede increases in social complexity.

Slingerland, Edward, Quentin D. Atkinson, Carol Ember, Oliver Sheehan, Michael Muthukrishna, Joseph Bulbulia, and Russell D. Gray. 2020. “Coding Culture: Challenges and Recommendations for Comparative Cultural Databases,” Evolutionary Human Sciences 2: e29. *

Considerable progress in explaining cultural evolutionary dynamics has been made by applying rigorous models from the natural sciences to historical and ethnographic information collected and accessed using novel digital platforms. However, future progress requires recognition of the unique challenges posed by cultural data, such as recognising the critical role of theory, selecting appropriate units of analysis, data gathering and sampling strategies, winning expert buy-in, achieving reliability and reproducibility in coding, and ensuring interoperability and sustainability of the resulting databases. We conclude by proposing a set of practical guidelines to meet these challenges.