Article describes how primate and human social groups exhibit a fractal structure that has a very limited range of preferred layer sizes. The authors calculate the size dependence of the scaling properties of complex social network models and argue that this aggregate behavior exhibits a form of collective intelligence.
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Article describes how primate and human social groups exhibit a fractal structure that has a very limited range of preferred layer sizes. The authors calculate the size dependence of the scaling properties of complex social network models and argue that this aggregate behavior exhibits a form of collective intelligence.
Physical Description
15 p.
Notes
Abstract: Primate and human social groups exhibit a fractal structure that has a very limited range of preferred layer sizes, with groups of 5, 15, 50 and (in humans) 150 and 500 predominating. In non-human primates, this same fractal distribution is also observed in the distribution of species mean group sizes and in the internal network structure of their groups. Here we demonstrate that this preferential numbering arises because of the critical nature of dynamic self-organization within complex social networks. We calculate the size dependence of the scaling properties of complex social network models and argue that this aggregate behavior exhibits a form of collective intelligence. Direct calculation establishes that the complexity of social networks as measured by their scaling behavior is non-monotonic, peaking globally around 150 with a secondary peak at 500 and tertiary peaks at 5, 15 and 50. This provides a theory-based rationale for the fractal layering of primate and human social groups.
Publication Title:
Proceedings of the Royal Society A
Volume:
479
Issue:
2274
Peer Reviewed:
Yes
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West, Bruce J.; Culbreth, Garland; Dunbar, Robin I. M. & Grigolini, Paolo.Fractal structure of human and primate social networks optimizes information flow,
article,
June 7, 2023;
(https://digital.library.unt.edu/ark:/67531/metadc2201685/:
accessed July 16, 2024),
University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu;
crediting UNT College of Science.
