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Record W2751714796

Thinking Fast and Slow

2012· article· en· W2751714796 on OpenAlexvenueaboutno aff
Iain F Gow

Bibliographic record

Venue˜The œinnovation journal · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsnot available
Fundersnot available
KeywordsProspect theoryArgument (complex analysis)SociologyComputer sciencePsychologyEpistemologyPositive economicsCognitive psychologyEconomicsPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Daniel Kahneman Thinking Fast and Slow Toronto, Doubleday Canada 2011This is an important book by only psychologist ever to win Nobel Prize for economics. In it Daniel Kahneman, emeritus professor of psychology at Princeton University, summarizes work that he and late Amos Tversky did on human cognition and decision-making. It has some cautionary implications for students of innovation.The argument is that humans have two systems for problem solving and decision-making. System 1 (S1) is rapid, intuitive and emotional. This is system described by Malcolm Gladwell in Blink. System 2 (S2) is slow, deliberative and logical. Only S2 can apply rules. S1 is obviously useful, even necessary, for survival when a rapid decision may mean safety and success and a slow one danger or failure. However, dominance of S1 goes much further in determining our decisions and our behaviour. It demands explanations and excels at constructing best possible story that incorporates ideas currently activated (85), but it doesn't allow for information that we don't have. It is a machine for jumping to conclusions.S2 is much more shrewd, but it gets tired easily and is lazy. It has a limited attention span. Moreover, Kahneman says when we S2 is otherwise engaged, we will believe almost anything (81). S2 is capable of correcting errors made hastily by S1, but sustained effort can lead to the well-known phenomenon of ego depletion where there is a loss of motivation and mental energy (41). Kahneman calls S2 lazy controller.More fundamentally, S1 is behind our insatiable desire for narrative. We want coherent explanations, and favour thinking over statistical reasoning. When uncertain, S1 bets on an answer and bets are graded by (77). This leads to illusion of understanding (199). Unfortunately, in this process, we give too much weight to small numbers and overrate importance of details (153). We are unwilling to believe that much of what we see is random: causal explanations of chance events are inevitably wrong (118). Thus thinking prevails over statistics, and we prefer stories to base rates. We overrate small risks: after 9/11 people avoided flying and numbers of more probable highway deaths increased.The results of this kind of thinking are devastating. Kahneman tells stories, but they are not anecdotes, they are summaries of studies in many different areas. Israeli parole judges were much more likely to grant parole to cases that came before them early in day or right after lunch but as time went on they returned to lower mean. Psychologists observing trials of recruits in Israeli army were completely unable to predict which participants would make good officer material. Guidance counsellors and university admissions officers were similarly inept, which led Kahneman to say that admission interviews lowered validity of admissions. Various kinds of financial experts had dismal results. A bank of 11,600 market forecasts by chief financial officers of a large number of private corporations collected at Duke University proved to be quite worthless. Financial advisers and experts asked to pick promising stocks did no better than rolling dice. In realm of political predictions, Kahneman found that most knowledgeable experts were less realistic than reasonably well-informed amateurs. He even turned his eye to his own profession and challenged idea that students' names should appear on their examinations, so that professor can put their answers into context. Kaheneman calls this halo effect and found that results were considerably different when names were omitted.So Kahneman is skeptical of experts. They overrate value of their knowledge and, even when faced with overwhelming evidence to contrary, they persist in what they do. Practitioners value experience over statistics; there is a deep resistance to demystification of expertise (288). …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.059
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.007
Scholarly communication0.0080.007
Open science0.0010.003
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0380.016

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.159
GPT teacher head0.391
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2012
Admission routes2
Has abstractyes

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Same venue˜The œinnovation journalSame topicComplex Systems and Decision MakingFrench-language works237,207