Do cultural ancestry and diffusion impact human development? New statistical approaches to tackling Galton’s problem

Abstract

Human behavioural patterns vary widely across the globe, despite relative genetic homogeneity. By learning attitudes, beliefs and behaviours from the people around us, we form behaviourally similar groups, commonly known as cultures. Traits that develop within a culture can increases the survival and ultimately increase fitness of the group, allowing humans to inhabit environments from the Arctic Circle to the Sahara desert. Researchers have tried to explain the evolution of cultural traits as the result of ecological adaptations or as a progression from other behaviours. In 1889, Sir Francis Galton famously argued that patterns of cultural variation could arise from ancestral inheritance or the diffusion of ideas between groups, questioning the statistical validity of cross-cultural analysis that ignores these processes. Recently, statistical models have become available that allow the modelling of ancestral and diffusional relationships between cultures, while controlling for other possible predictors of variation. I discuss a number of these models, and test two; the Phylogenetic Generalised Least Squares spatial (PGLS-Spatial) model and the Spatial Auto-Regressive disturbance (SAR-Disturbance) model. Using these models I assessed the influence of ancestry and diffusion on UN indicators of international development with the aim of elucidating some of the processes behind international development. There was no consensus on the influence of ancestry and diffusion between the methods, which prompted a simulation study to explore why discrepancies occurred. Both methods correctly identified ancestral relationships from the simulated data, but PGLS-Spatial shows more power than the SAR-Disturbance model. However, when testing geographically distributed data PGLS-Spatial correctly identified spatial relationships but incorrectly identified ancestral relationships. I discuss a number of weaknesses with each method, as well as issues regarding the creation of the relationship matrices used to represent ancestry and diffusion within each analysis. The results of this study aim to start the process of including ancestral and diffusional relationships in the analysis of cross-cultural data, which improve the validity of statistical analysis in cross-cultural data and enable new hypotheses of cultural evolution to be tested.