Belief in moralizing high gods (MHGs) has been theorized as a response to unfavorable environments, as a way to normalize behavior. In this study, researchers test the theory by creating a model for predicting the distribution of MHGs. They run many alternative models, testing the effects of resource abundance, climate stability, and pertinent social factors on the occurrence of belief in MHGs. Based on the ten most supported models, they create an average model that predicts MHGs within cultures with “excellent” accuracy.

Jared Diamond previously argued that the East-West orientation of Eurasia allowed for the spread of substantial cultural, political, technological, and military innovations, due to environmental homogeneity across the continent. The authors of this article test this theory through quantitative analyses. Their results suggest that ecology can indeed influence the potential for cultural transmission, consistent with Diamond's theory. However, the authors do not find support for Eurasia having lower environmental barriers to cultural transmission in comparison to other continents.

The researchers, using principal component analysis, generalized additive models across 12 agriculture origin locations, and a model predicting hunter-gatherer population density, evaluate hindcasted population density trends to suggest predictors of the development of agriculture. Using domestication as an indicator of agriculture, they test 3 competing hypotheses regarding agriculture development. Their results are consistent with the "surplus" hypothesis, indicating that agriculture arose as population densities increased along with environmental capabilities.

In this article, the authors seek to investigate why some societies reject agriculture despite its many benefits. By modeling data regarding ecological fitness and cultural transmission, the authors found predictors for the degree to which a society relies on agriculture. The authors conclude that the degree of fitness a local environment had for early domesticates as well as the degree of contact with neighboring societies strongly predicts levels of dependence on agriculture.

Researchers examined both why so many language are spoken today, and why they are so unevenly distributed geographically. Instead of looking at correlative tests, this study uses a process-based simulation model that attempted to predict both the number or precolonial languages in Australia as well as the number of languages per unit of land. The model was based upon three basic assumptions: 1) humans fill unoccupied spaces; 2) rainfall limits population density; 3) groups divide after reaching a maximum population. While researchers used the model strictly on the Australia continent, it was able to correctly explain 56% of spatial variation in language richness, and predict the total number of languages across the continent. The accuracy of this model concludes that climatic conditions and changes in group size are important factors in shaping language diversity patterns and therefore global human cultural diversity.

Using multiple logistic regression, the researchers compare the relative strength of predictors of land ownership across 102 societies. The analysis finds significant predictive power in factors such as neighbors' property system, population density, and geography.

In this article, the authors seek to determine cross-culturally valid predictors of dominant types of human subsistence around the world. They did this by formulating multiple models that incorporate different combinations of environmental, geographic, and social factors. These models were then used to test various hypotheses posed throughout the anthropological literature surrounding factors that determine dominant subsistence strategies.

The article discusses the role of land ownership in natural resource management and social-ecological resilience, and explores the factors that determine ownership norms in human societies. The study tests long-standing theories from ecology, economics, and anthropology regarding the potential drivers of land ownership, including resource defensibility, subsistence strategies, population pressure, political complexity, and cultural transmission mechanisms. Using cultural and environmental data from 102 societies, the study found an increased probability of land ownership in mountainous environments and societies with higher population densities. The study also found support for the idea that neighboring societies might influence land ownership. However, there was less support for variables associated with subsistence strategies and political complexity.

This study asks the following research questions: What are the dynamics of land ownership norms over time? Do these changes follow predetermined trajectories? The authors utilize biogeographical and evolutionary analyses to explore temporal and spatial patterns in land ownership norms within a sample of 73 Bantu societies. For the first component of the research, they test three prominent hypotheses regarding evolutionary trajectories: rectilinear, unilinear, and shift without restriction. For the second component, they use a multi-model inference approach to evaluate three hypotheses regarding the possible spatial patterns of land ownership. The results show evidence for the unilinear trajectory in type, but not with a consistent decrease in group ownership in size. Land ownership is more likely in areas with neighboring landowners and predictable resource productivity, while subsistence type is not significantly correlated with type of land ownership.

This study uses macroecological approaches to test the impact of climate, social environment, inter-group borrowing and cultural history on vernacular house architecture among 1140 societies. The authors suggest that certain features will be influenced: wall materials, ground plan, roof shape, and floor placement. They use mixed binary and multinominal regressions models to test these predictions. The results strongly support that climatic drivers, cultural continuity, and inter-group borrowing predict three out of the four features: wall materials, roof shape, and floor placement. Social drivers are a strong predictor of every feature tested.