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# The Random Forest of Society

**[Medium](https://daily.dev/sources/medium_js)** · 17 min read · 0 upvotes · 0 comments

## Summary

Using the random forest algorithm as a structural metaphor, this essay argues that a healthy society depends on maintaining genuine cognitive diversity among its members — not for moral reasons, but as an engineering constraint. Just as ensemble models outperform individual models only when their errors are decorrelated, a society's collective intelligence degrades when members are fed the same inputs (algorithmic feeds, filter bubbles) and begin erring in the same direction. The essay distinguishes between two failure modes: homogenization into one correlated mass, and fragmentation into sealed, mutually incompatible clusters. It introduces two governing concepts — 'proximity' (connectability between different minds) and 'unexpendency' (the arrival of what current models cannot yet classify) — and argues that the only legitimate pruning is harm, never mere difference. It closes by noting that unlike a real random forest, society has no fixed ground truth; its aggregate doesn't converge on truth but generates the conditions for the next round, which is why no emergent order is automatically legitimate.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://medium.com/@h.yura.a/the-random-forest-of-society-2392485216fe>

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Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#random-forest](https://daily.dev/tags/random-forest)

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