Looking Backward and Forward on Hindsight Bias
Bibliographic record
Abstract
The same event that appeared unpredictable in foresight can be judged as predictable in hindsight. <italic>Hindsight bias</italic> clouds judgments in all areas of life, including legal decisions, medical diagnoses, consumer satisfaction, sporting events, and election outcomes. We discuss three theoretical constructs related to hindsight bias: memory, reconstruction bias, and motivation. Attempts to recall foresight knowledge fail because newly acquired knowledge affects memory either directly or indirectly by biasing attempts to reconstruct foresight knowledge. On a metacognitive level, overconfidence and surprise contribute to hindsight bias. Overconfidence in knowledge increases hindsight bias whereas a well-calibrated confidence reduces hindsight bias. Motivational factors also contribute to hindsight bias by making positive and negative outcomes appear more or less likely, depending on a variety of factors. We review hindsight bias theories and discuss three exciting directions for future research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".