The complex structure of hunter-gatherer social networks

被引:237
作者
Hamilton, Marcus J. [1 ]
Milne, Bruce T.
Walker, Robert S.
Burger, Oskar
Brown, James H.
机构
[1] Univ New Mexico, Dept Anthropol, Albuquerque, NM 87131 USA
[2] Univ New Mexico, Dept Biol, Albuquerque, NM 87131 USA
[3] Univ Colorado, Dept Anthropol, Denver, CO 80217 USA
[4] Hlth Sci Ctr, Denver, CO 80217 USA
[5] Santa Fe Inst, Santa Fe, NM 87501 USA
关键词
hunter-gatherers; social networks; self-similarity; generalized Horton analysis; population structure; complex systems; ALLOMETRIC SCALING LAWS; RIVER NETWORKS; LIFE; EVOLUTION; DEMAND; ORIGIN; TERMS; SIZE;
D O I
10.1098/rspb.2007.0564
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
In nature, many different types of complex system form hierarchical, self-similar or fractal-like structures that have evolved to maximize internal efficiency. In this paper, we ask whether hunter-gatherer societies show similar structural properties. We use fractal network theory to analyse the statistical structure of 1189 social groups in 339 hunter-gatherer societies from a published compilation of ethnographies. We show that population structure is indeed self-similar or fractal-like with the number of individuals or groups belonging to each successively higher level of organization exhibiting a constant ratio close to 4. Further, despite the wide ecological, cultural and historical diversity of hunter-gatherer societies, this remarkable self-similarity holds both within and across cultures and continents. We show that the branching ratio is related to density-dependent reproduction in complex environments and hypothesize that the general pattern of hierarchical organization reflects the self-similar properties of the networks and the underlying cohesive and disruptive forces that govern the flow of material resources, genes and non-genetic information within and between social groups. Our results offer insight into the energetics of human sociality and suggest that human social networks self-organize in response to similar optimization principles found behind the formation of many complex systems in nature.
引用
收藏
页码:2195 / 2202
页数:8
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