Social status does not predict in-camp integration among egalitarian hunter-gatherer men

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Abstract

In the last few decades, there has been much research regarding the importance of social prestige in shaping the social structure of small-scale societies. While recent studies show that social prestige may have important health consequences, little is known about the extent to which prestige translates into actual in-person interactions and proximity, even though the level of integration into such real-life social networks has been shown to have important health consequences. Here, we determine the extent to which two different domains of social prestige, popularity (being perceived as a friend by others), and hunting reputation (being perceived as a good hunter), translate into GPS-derived in- and out-of-camp proximity networks in a group of egalitarian hunter-gatherer men, the Hadza. We show that popularity and hunting reputation differ in the extent to which they are translated into time spent physically close to each other. Moreover, our findings suggest that in-camp proximity networks, which are commonly applied in studies of small-scale societies, do not show the full picture of Hadza men’s social preferences. While men are in camp, neither popularity nor hunting reputation is associated with being central in the proximity network; however, when out of camp, Hadza men who are popular are more integrated in the proximity networks while men with better hunting reputations are less integrated. Overall, our findings suggest that, to fully understand social preferences among hunter-gatherers, both in-camp and out-of-camp proximity networks should be considered.

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Collective dynamics of 'small-world' networks.

D. Watts, S Strogatz (1998)

Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.