Sandeford David S
School of Human Evolution and Social Change, Arizona State University, Tempe, AZ 85281, USA.
R Soc Open Sci. 2018 May 2;5(5):171137. doi: 10.1098/rsos.171137. eCollection 2018 May.
The relationship between organizational complexity and demographic scale is an enduring research problem at the intersection of the natural and social sciences and has far reaching implications for the study of social evolution, particularly the emergence and collapse of complex social organizations such as chiefdoms, states and empires. Anthropological models of social evolution universally assume that population growth plays a critical role in the development of organizational complexity; however, the relationship between organizational complexity and demographic scale has not been formalized and cross-culturally validated. There is a rich yet unsystematized body of diachronic organizational and demographic data describing the evolution of organizational complexity in 10 archaeologically known cases of primary state formation. Using this dataset, this essay proposes and tests a complex network model that describes state societies as discrete, self-similar, hierarchical social networks. The model accurately describes how organizational complexity and population scale in all cases. The complex network architecture of state societies suggests that further advances in our understanding of modern social organization may be found by a deeper investigation of the role of human nature in the evolution of human societies.
组织复杂性与人口规模之间的关系是自然科学与社会科学交叉领域一个长期存在的研究问题，对社会进化研究有着深远影响，尤其是对诸如酋邦、国家和帝国等复杂社会组织的兴起与衰落的研究。社会进化的人类学模型普遍认为，人口增长在组织复杂性的发展中起着关键作用；然而，组织复杂性与人口规模之间的关系尚未得到形式化且跨文化的验证。有大量历时性的组织和人口数据，虽丰富但未系统化，描述了10个考古学上已知的原生国家形成案例中组织复杂性的演变。利用这个数据集，本文提出并测试了一个复杂网络模型，该模型将国家社会描述为离散的、自相似的、分层的社会网络。该模型准确描述了所有案例中的组织复杂性和人口规模情况。国家社会的复杂网络架构表明，通过更深入地研究人性在人类社会进化中的作用，我们对现代社会组织的理解可能会取得进一步进展。
