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原文連結
論文資訊
- 類型：已發表論文
- 日期：2025
摘要
Dunbar's framework highlights the challenge of maintaining large, stable 社會 網絡s given 認知 constraints. Expanding on this, I propose that 碎形 社會 網絡s function as lossy compression algorithms, efficiently reducing the complexity of 社會 storage and retrieval. Rather than tracking all relationships explicitly, individuals rely on hierarchical abstractions and transitive inference, shifting storage complexity from O(N-2) to O N (log N). This insight suggests broader implications for 認知 演化, institutional organization, and 人工智慧.
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