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.

Type

Book

Intellectual base

Review or monograph

Year

2004

Link

https://archive.org/details/wisdomofcrowds0000suro

Cited from

behavioral_economics, biology_ecology, economics_finance, psychology, statistics_experimental_design, systems_cybernetics

Cited by

12 citations across 12 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Bottom-Up Perspectives
  • Listed in the references but not attached to a specific claim.
• 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).

• Correlated-Source Attribution Failure
  • This is also what links the prime to wisdom-of-crowds reasoning, which presupposes the very source independence the prime warns is violated — the prime is the structural explanation for why naive aggregation of correlated sources underdelivers exactly when it looks most corroborated.

    This sourceAggregation of many estimators works only when they are independent (independence is one of the four named conditions); correlated (e.g., commonly briefed) sources under-deliver, the structural reason naive aggregation fails when it looks most corroborated. (

• Diversity
  • A systems ecologist familiar with ecosystem stability might recognize the parallel to portfolio optimization; a portfolio manager might see the connection to ensemble methods in machine learning; a machine-learning practitioner might recognize the same principle in team composition research, a cross-domain transfer Surowiecki (2004) popularized in The Wisdom of Crowds by drawing the same structural lesson across markets, juries, and prediction systems.
• 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.

• Herding Behavior
  • Herding is thus a compression heuristic for navigating complex decision environments; actors rationally substitute observation of aggregate behavior for impossible full-information aggregation.
• Information Cascade
  • The same phenomenon (many people converging on a choice) can reflect genuine collective intelligence or cascade fragility, depending on whether signals remain private or become public.
• 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.

• Wisdom of the Crowds
  • Adding more correlated voices does nothing; adding more uncorrelated voices drives error toward zero, a structural result the statistician's law of large numbers makes precise only under the independence assumption.

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.

Mechanisms

• Blind Independent Estimates
  • Independence is the entire load-bearing condition; if it leaks, the procedure collapses into ordinary consensus.

    This sourceTreats independence of individual judgments as a necessary condition for obtaining wisdom-of-crowds benefits.

• Blind Independent Forecast Round
  • Its strength is that it protects the one thing a beauty-contest destroys: an aggregate of genuinely independent judgments, which is only informative because the errors were uncorrelated before anyone saw the crowd.

    This sourceTreats aggregation of independent judgments as informative because independence prevents individual errors from becoming correlated.

Verification

Does it exist? Not checked yet. This entry carries no identifier to resolve. It was extracted from the citation as written in the article, normalized, and deduplicated against the rest of the registry.

Does it back the claim? Not recorded. Neither this nor any other of the 12 citations of this work carries a recorded support check.

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Links previously used in the corpus

Before the registry existed this work was also linked 4 other ways.

• https://archive.org/details/wisdomofcrowdswh0000suro ×1
• https://books.google.com/books/about/The_Wisdom_of_Crowds.html?id=hHUsHOHqVzEC ×1
• https://openlibrary.org/books/OL7440777M/The_Wisdom_of_Crowds ×1
• https://search.worldcat.org/title/182615446 ×1

Registry ID ref:af2b66bd624f · see in the full table