Settlement pattern data for primary states from Organizational complexity and demographic scale in primary states

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.

组织复杂度与人口规模之间的关联，是自然科学与社会科学交叉领域的长期性研究课题，对社会演化研究——尤其是酋邦、国家、帝国等复杂社会组织的兴起与衰亡——具有深远影响。人类学社会演化模型普遍假定，人口增长在组织复杂度的发展进程中发挥关键作用；然而，二者的关联尚未被形式化建模，也未通过跨文化效度检验。当前存在一套丰富却未系统化的历时性组织与人口数据集，涵盖10个经考古证实的原生国家形成实例中组织复杂度的演化历程。本研究基于该数据集，提出并验证了一种复杂网络模型，该模型将国家社会视作离散、自相似且层级化的社会网络。该模型可精准刻画所有案例中组织复杂度与人口规模的对应关系。国家社会的复杂网络架构表明，若能深入探究人性在人类社会演化中的作用，或将进一步深化我们对现代社会组织的认知。