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What problem does a fractal social network solve?

Published online by Cambridge University Press:  27 November 2025

Marcus J. Hamilton Open the ORCID record for Marcus J. Hamilton [Opens in a new window]
Marcus J. Hamilton*
Affiliation:
Department of Anthropology, University of Texas at San Antonio , San Antonio, TX, USA School of Data Science, University of Texas at San Antonio, San Antonio, TX, USA Santa Fe Institute, Santa Fe, NM, USA marcus.hamilton@utsa.edu
*
*Corresponding author.
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Abstract

Dunbar’s framework highlights the challenge of maintaining large, stable social networks given cognitive constraints. Expanding on this, I propose that fractal social networks function as lossy compression algorithms, efficiently reducing the complexity of social storage and retrieval. Rather than tracking all relationships explicitly, individuals rely on hierarchical abstractions and transitive inference, shifting storage complexity from $O\left( {{N^2}} \right)$ to $O\left( {N\log N} \right)$. This insight suggests broader implications for cognitive evolution, institutional organization, and artificial intelligence.

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Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 48 , 2025 , e168
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© The Author(s), 2025. Published by Cambridge University Press

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References

Barenblatt, G. I., & Isaakovich, B. G. (1996). Scaling, self-similarity, and intermediate asymptotics: Dimensional analysis and intermediate asymptotics. Cambridge University Press.CrossRefGoogle Scholar
Bettencourt, L. M. A. (2021). Introduction to urban science: Evidence and theory of cities as complex systems. MIT Press.CrossRefGoogle Scholar
Dunbar, R. I. M., Carron, P. M., & Shultz, S. (2018). Primate social group sizes exhibit a regular scaling pattern with natural attractors. Biology Letters 14, 20170490. https://doi.org/10.1098/rsbl.2017.0490 CrossRefGoogle ScholarPubMed
Hamilton, M. J. (2022). Collective computation, information flow, and the emergence of hunter-gatherer small-worlds. Journal of Social Computing 3, 18–37.CrossRefGoogle Scholar
Hamilton, M. J., Milne, B. T., Walker, R. S., Burger, O., & Brown, J. H. (2007). The complex structure of hunter–gatherer social networks. Proceedings of the Royal Society B: Biological Sciences 274, 2195–2203. https://doi.org/10.1098/rspb.2007.0564 CrossRefGoogle ScholarPubMed
Hamilton, M. J., Walker, R. S., Buchanan, B., & Sandeford, D. S. (2020). Scaling human sociopolitical complexity. PLOS ONE 15, e0234615. https://doi.org/10.1371/journal.pone.0234615 CrossRefGoogle ScholarPubMed
Hill, R. A., Bentley, R. A., & Dunbar, R. I. (2008). Network scaling reveals consistent fractal pattern in hierarchical mammalian societies. Biology letters 4, 748–751.CrossRefGoogle ScholarPubMed
Sandeford, D. S. (2018). Organizational complexity and demographic scale in primary states. Royal Society Open Science 5, 171137.CrossRefGoogle ScholarPubMed
West, G. (2017). Scale: The universal laws of growth, innovation, sustainability, and the pace of life in organisms, cities, economies, and companies. Penguin.Google Scholar
Zhou, W-. X., Sornette, D., Hill, R. A., & Dunbar, R. I. (2005). Discrete hierarchical organization of social group sizes. Proceedings of the Royal Society of London B: Biological Sciences 272, 439–444.Google ScholarPubMed