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

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