Bibliographic record
Abstract
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). …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".