CONCEPT
Fitness Model
Bianconi and Barabási's 2001 extension of preferential attachment in which each node has an intrinsic <em>fitness</em> — a capacity to attract connections that is independent of when it entered the network.
Pure preferential attachment cannot explain why younger nodes sometimes overtake older ones — why Google displaced AltaVista, why some 2023 AI startups have already surpassed incumbents a decade older. Bianconi and Barabási's fitness model introduces a per-node quality parameter that multiplies the attachment probability. A new node with sufficiently high fitness can accumulate links faster than an older node with lower fitness, and given enough time can become the dominant hub. In the AI economy, fitness is what You On AI calls judgment, taste, and vision — the capacity to produce work that attracts attention, builds trust, and generates connection. AI equalizes capability but does not equalize fitness.
In The You On AI Field Guide
The model was developed to explain a specific empirical puzzle: certain young websites in the late 1990s had grown faster than the pure preferential attachment model predicted. Adding a fitness parameter — drawn from a distribution — resolved the discrepancy. The striking theoretical result was that under certain fitness distributions, the
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