In this study, the authors examine pathways to social inequality, specifically social class hierarchy, in 408 non-industrial societies. In a path model, they find social class hierarchy to be directly associated with increased population size, intensive agriculture and large animal husbandry, real property inheritance (unigeniture) and hereditary political succession, with an overall R-squared of 0.45. They conclude that a complex web of effects consisting of environmental variables, mediated by resource intensification, wealth transmission variables, and population size all shape social inequality.

In this study, the authors examine pathways to social inequality, specifically social class hierarchy, in 408 non-industrial societies. In a path model, they find social class hierarchy to be directly associated with increased population size, intensive agriculture and large animal husbandry, real property inheritance (unigeniture) and hereditary political succession, with an overall R-squared of 0.45. They conclude that a complex web of effects consisting of environmental variables, mediated by resource intensification, wealth transmission variables, and population size all shape social inequality.

In this study, the authors examine pathways to social inequality, specifically social class hierarchy, in 408 non-industrial societies. In a path model, they find social class hierarchy to be directly associated with increased population size, intensive agriculture and large animal husbandry, real property inheritance (unigeniture) and hereditary political succession, with an overall R-squared of 0.45. They conclude that a complex web of effects consisting of environmental variables, mediated by resource intensification, wealth transmission variables, and population size all shape social inequality.

Researchers investigated further into why and how humans speak so many languages across the globe, and why they are spread out unevenly. Using two different path analyses, a Stationary Path analysis and a GWPath, researchers tested the effect of eight different factors on language diversity. Out of the eight variables (river density, topographic complexity, ecoregion richness, temperature and precipitation constancy, climate change velocity, population density, and carrying capacity with group size limits), population density, carrying capacity with group size limit, and ecoregion richness had the strongest direct effects. Overall, the study revealed the role of multiple different mechanisms in shaping language richness patterns. The GWPath showed that not only does the most important predictor of language diversity vary over space, but predictors can also vary in the direction of their effects in different regions. They conclude that there is no universal predictor of language richness.

Researchers investigated further into why and how humans speak so many languages across the globe, and why they are spread out unevenly. Using two different path analyses, a Stationary Path analysis and a GWPath, researchers tested the effect of eight different factors on language diversity. Out of the eight variables (river density, topographic complexity, ecoregion richness, temperature and precipitation constancy, climate change velocity, population density, and carrying capacity with group size limits), population density, carrying capacity with group size limit, and ecoregion richness had the strongest direct effects. Overall, the study revealed the role of multiple different mechanisms in shaping language richness patterns. The GWPath showed that not only does the most important predictor of language diversity vary over space, but predictors can also vary in the direction of their effects in different regions. They conclude that there is no universal predictor of language richness.

Researchers investigated further into why and how humans speak so many languages across the globe, and why they are spread out unevenly. Using two different path analyses, a Stationary Path analysis and a GWPath, researchers tested the effect of eight different factors on language diversity. Out of the eight variables (river density, topographic complexity, ecoregion richness, temperature and precipitation constancy, climate change velocity, population density, and carrying capacity with group size limits), population density, carrying capacity with group size limit, and ecoregion richness had the strongest direct effects. Overall, the study revealed the role of multiple different mechanisms in shaping language richness patterns. The GWPath showed that not only does the most important predictor of language diversity vary over space, but predictors can also vary in the direction of their effects in different regions. They conclude that there is no universal predictor of language richness.

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 authors examine multiple ecological variables as possible predictors of language diversity in North America using path analysis, mechanistic simulation modelling, and geographically weighted regression. They conclude that many of the variables do not predict language diversity, but rather are mediated by population density. The authors also find that the variables' ability to predict is not universal across the continent, but rather more regional.

This study examines the relationship between equality and democracy, focusing on social stratification and political participation as the primary measures. Results suggest that equality strengthens some aspects of democracy, but several other factors such as industrialization are involved in the relationship.

In this paper, the authors analyzed data on 20 hunter-gatherer groups in order to understand the factors that influence the diversity and elaborateness of their tool assemblages. They used data collected by a variety of ethnographers to draw inferences about the complexity of implement assemblages and how it is affected by ecological constraints, modes of resource procurement, group movement, and population size. Regression analysis showed that the two strongest predictors of implement complexity were growth season (GS) (as a proxy for risk) and the number of annual residential moves (NMV). With the understanding that NMV and GS are likely not independent, the authors created addition and interaction models to understand how these variables may work in tandem to influence implement diversity and elaborateness. The results show that a shorter growing season (higher risk) and a lower number of moves are correlated with greater implement complexity. This analysis also divided the hunter-gatherers into two subgroups: a subgroup characterized by higher diversity of complex implements and more elaborate individual implements than predicted by the model, and a subgroup characterized by lower diversity and less elaborateness than predicted. These subgroups were found to correspond with the distinction between foragers (groups that move more-or-less as a unit while gathering) and collectors (groups that gather (logistically from a more-or-less fixed settlement), with the higher diversity subgroup being made up mostly of collectors and the lower diversity subgroup being made up mostly of foragers. Finally, the authors suggest that under conditions where population growth leads to increased density, foraging strategies will tend to shift to collector strategies in conjunction with increased elaborateness of implements to exploit resources with greater intensity.