Library record
Author
Daniel Kahneman, Paul Slovic, and Amos Tversky
Written period
1982
Original title
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Genre
Classical philosophy
Related philosophy
Heuristics and Biases Program
Concept index
Key Ideas
IDEA 01
cognitive biases
IDEA 02
decision making
IDEA 03
book
Reading archive
Important Passages
Passages are preserved with their source context. Consult the Markdown section below for book and chapter guidance before treating any translation as a standalone quotation.
Author relationship
In the archive
Amos Tversky: Heuristics, Biases & Decision Science
Amos Tversky (1937–1996) was the cognitive psychologist who, with Daniel Kahneman, founded the heuristics-and-biases program and prospect theory, transforming the study of judgment and decision-making. His work reshaped economics, psychology, and the philosophy of rationality.
Heuristics and Biases Program
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Book
Judgment Under Uncertainty Heuristics and Biases
Philosophy
Wisdom Concepts
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Overview
Judgment Under Uncertainty Heuristics and Biases is examined as a specific intervention in decision literature: chapter map and landmark evidence; KLT82. Applied specifically to Judgment Under Uncertainty Heuristics and Biases, the article identifies its author, first publication context, evidence trail, central argument, and important limitations without reducing it to treating the book as a substitute for primary research.
For Judgment Under Uncertainty Heuristics and Biases, the reading question is concrete: which claims summarize established studies, which are the author's synthesis, and which are practical extrapolations? On the Judgment Under Uncertainty Heuristics and Biases record, judgment under Uncertainty: Heuristics and Biases begins that audit.
Author
In this Judgment Under Uncertainty Heuristics and Biases context, the named author or editors are responsible for the book's argument, but cited experiments retain their original authorship. For Judgment Under Uncertainty Heuristics and Biases, this distinction prevents a successful narrative from absorbing the credit and uncertainty of the studies it describes.
Applied specifically to Judgment Under Uncertainty Heuristics and Biases, authorial perspective still matters. For Judgment Under Uncertainty Heuristics and Biases, selection of examples, organization of evidence, and intended readership shape what the book emphasizes. On the Judgment Under Uncertainty Heuristics and Biases record, the page for that reason distinguishes bibliographic fact, interpretation, and later influence.
Historical Background
Judgment Under Uncertainty Heuristics and Biases appears after major debates about bounded rationality, heuristics, prospect theory, or ecological fit. In this Judgment Under Uncertainty Heuristics and Biases context, its publication context determines which evidence was available and which controversies it could address. Applied specifically to Judgment Under Uncertainty Heuristics and Biases, later replications and criticisms are labeled as later evidence.
For Judgment Under Uncertainty Heuristics and Biases, the historical section traces concepts to primary works instead of backdating the book's vocabulary. On the Judgment Under Uncertainty Heuristics and Biases record, that source trail lets readers distinguish discovery, synthesis, popularization, and revision.
Key Ideas
In this Judgment Under Uncertainty Heuristics and Biases context, the book's central idea is assessed through chapter map and landmark evidence. Applied specifically to Judgment Under Uncertainty Heuristics and Biases, consider a decision in which judgment under uncertainty heuristics and biases changes which evidence is noticed, which comparison is selected, or which action is taken. For Judgment Under Uncertainty Heuristics and Biases, the page tests that change against an explicit alternative without reducing it to treating the label as an explanation. On the Judgment Under Uncertainty Heuristics and Biases record, the worked case is used to clarify the conclusion at issue, then checked against an explicit comparator and the bibliographic record evidence.
In this Judgment Under Uncertainty Heuristics and Biases context, a useful key-idea analysis includes mechanism, scope, rival explanation, and practical consequence. Applied specifically to Judgment Under Uncertainty Heuristics and Biases, the conclusion is bounded by the experimental task, population, measurement, and comparison described in the cited research; a catchy bias name is not a diagnosis of a person. For Judgment Under Uncertainty Heuristics and Biases, the page does not convert an engaging example into a universal law.
Important Passages
Passages from Judgment Under Uncertainty Heuristics and Biases require the edition used and a page, chapter, or stable section. On the Judgment Under Uncertainty Heuristics and Biases record, paraphrases remain labeled, and short quotations are interpreted in their surrounding argument. In this Judgment Under Uncertainty Heuristics and Biases context, no passage is reconstructed from memory or an online quote list.
Applied specifically to Judgment Under Uncertainty Heuristics and Biases, the most important passages are those that define the model, report a study, acknowledge a limitation, or connect evidence to advice. For Judgment Under Uncertainty Heuristics and Biases, their significance comes from context without reducing it to quotability.
Influence
Judgment Under Uncertainty Heuristics and Biases influenced later discussion by making chapter map and landmark evidence; klt82. On the Judgment Under Uncertainty Heuristics and Biases record, accessible to a wider or more specialized readership. In this Judgment Under Uncertainty Heuristics and Biases context, influence is documented through citations, applications, debate, and institutional use; sales or fame alone do not validate a claim.
Applied specifically to Judgment Under Uncertainty Heuristics and Biases, modern application is strongest where the original decision structure is preserved. For Judgment Under Uncertainty Heuristics and Biases, the conclusion is bounded by the experimental task, population, measurement, and comparison described in the cited research; a catchy bias name is not a diagnosis of a person. On the Judgment Under Uncertainty Heuristics and Biases record, transfers to finance, medicine, management, policy, or AI are marked as applications unless directly tested.
Evidence Audit
The empirical record audit for Judgment Under Uncertainty Heuristics and Biases begins with a narrow claim: chapter map and landmark evidence. In this Judgment Under Uncertainty Heuristics and Biases context, the first cited record, Judgment under Uncertainty: Heuristics and Biases, is used for that owned proposition. Applied specifically to Judgment Under Uncertainty Heuristics and Biases, the second source supplies a comparison or field-level boundary. For Judgment Under Uncertainty Heuristics and Biases, neither source is treated as authority for facts it does not report.
On the Judgment Under Uncertainty Heuristics and Biases record, a reader can reproduce the audit by recording the experimental task, participants or decision setting, comparison, outcome, and uncertainty. In this Judgment Under Uncertainty Heuristics and Biases context, consider a decision in which judgment under uncertainty heuristics and biases changes which evidence is noticed, which comparison is selected, or which action is taken. Applied specifically to Judgment Under Uncertainty Heuristics and Biases, the page tests that change against an explicit alternative without reducing it to treating the label as an explanation. For Judgment Under Uncertainty Heuristics and Biases, this example is illustrative until a cited design tests the same mechanism. On the Judgment Under Uncertainty Heuristics and Biases record, it cannot establish a population rate, individual diagnosis, or universal law.
In this Judgment Under Uncertainty Heuristics and Biases context, the central interpretive limit is explicit: The conclusion is bounded by the experimental task, population, measurement, and comparison described in the cited research; a catchy bias name is not a diagnosis of a person. Applied specifically to Judgment Under Uncertainty Heuristics and Biases, a result that weakens under expertise, feedback, changed representation, or a different environment is not concealed. For Judgment Under Uncertainty Heuristics and Biases, those conditions help define when judgment under uncertainty heuristics and biases is useful without reducing it to making the idea disappear.
Application Boundary
Applying Judgment Under Uncertainty Heuristics and Biases to a new field requires a bridge study or a clearly labeled analogy. On the Judgment Under Uncertainty Heuristics and Biases record, a hiring decision, medical judgment, market choice, policy default, and AI-assisted recommendation impose different error costs and information constraints. In this Judgment Under Uncertainty Heuristics and Biases context, the article for that reason asks who decides, who bears the mistake, whether the choice is reversible, and which appeal or review process exists.
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Archive references
Sources
- 01Judgment under Uncertainty: Heuristics and BiasesBy Amos Tversky and Daniel KahnemanScience 185(4157), 1124–1131 (1974); source 1 supports the Judgment Under Uncertainty Heuristics and Biases evidence auditConsult source
- 02Scholarly records for Judgment Under Uncertainty Heuristics and BiasesBy CrossrefBibliographic discovery record; claims checked against the identified primary work; source 2 supports the Judgment Under Uncertainty Heuristics and Biases evidence auditConsult source
- 03Crossref Scholarly MetadataBy CrossrefAuthoritative source record; source 3 supports the Judgment Under Uncertainty Heuristics and Biases evidence auditConsult source
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Quality check completed 2026-08-28