The Wisdom of Crowds¶
Surowiecki, J. (2004). The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations. Doubleday.
Cited by¶
10 citations across 10 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Bottom-Up Perspectives
This sourceBibliography-only (tier C); existence and details verified.
- Conformity
- This is cognitively cheap and often locally rational, which is exactly why it is hard to suppress.
This sourceArgues accurate collective judgment requires diversity, independence, decentralization, and aggregation, and treats conformity/herding as the failure mode that destroys crowd wisdom. Used here for the 'conformity as cheap heuristic' sentence, where it is a weak/contrast fit (see flag). Live-verified (Internet Archive).
- This is cognitively cheap and often locally rational, which is exactly why it is hard to suppress.
- Diversity
- Efficient Market Hypothesis (EMH)
- EMH reasoning generalizes wherever decentralized agents pool information into a single price-like signal—Surowiecki (2004) calls this the "wisdom of crowds" pattern, and the same {information set, risk model, test} triple applies across the contexts mapped below.
This sourceAggregation of diverse, independent, decentralized signals yields accurate collective estimates — supports marker 218 (the 'wisdom of crowds' generalization of price-as-information-aggregator).
- EMH reasoning generalizes wherever decentralized agents pool information into a single price-like signal—Surowiecki (2004) calls this the "wisdom of crowds" pattern, and the same {information set, risk model, test} triple applies across the contexts mapped below.
- Herding Behavior
- Information Cascade
- Paradox of Unanimity
- Not `wisdom_of_the_crowds`. Wisdom-of-crowds says aggregating independent estimates beats any single one.
This sourceAggregating independent estimates outperforms almost any individual — the independence-held claim of which the paradox of unanimity is the boundary condition.
- Not `wisdom_of_the_crowds`. Wisdom-of-crowds says aggregating independent estimates beats any single one.
- Wisdom of the Crowds
Domain-specific¶
- False Consensus Effect
- False consensus is the opposite — correlated, self-anchored errors that do not cancel because every estimator leans the same direction relative to their own trait.
This sourceArgues that averaging independent individual errors cancels noise — the favorable regime that correlated, self-anchored errors (false consensus) break.
- False consensus is the opposite — correlated, self-anchored errors that do not cancel because every estimator leans the same direction relative to their own trait.
Verification¶
This reference passed the adversarial substantiation pipeline: it was checked to exist and to support the claim it is attached to. See how references were verified.
Links previously used in the corpus¶
Before the registry existed this work was also linked 2 other ways.
- https://archive.org/details/wisdomofcrowdswh0000suro ×1
- https://openlibrary.org/books/OL7440777M/The_Wisdom_of_Crowds ×1
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