Revising probability estimates: Why increasing likelihood means increasing impact.
Bibliographic record
Abstract
Forecasted probabilities rarely stay the same for long. Instead, they are subject to constant revision-moving upward or downward, uncertain events become more or less likely. Yet little is known about how people interpret probability estimates beyond static snapshots, like a 30% chance of rain. Here, we consider the cognitive, affective, and behavioral consequences of revisions to probability forecasts. Stemming from a lay belief that revisions signal the emergence of a trend, we find in 10 studies (comprising uncertain events such as weather, climate change, sex, sports, and wine) that upward changes to event-probability (e.g., increasing from 20% to 30%) cause events to feel less remote than downward changes (e.g., decreasing from 40% to 30%), and subsequently change people's behavior regarding those events despite the revised event-probabilities being the same. Our research sheds light on how revising the probabilities for future events changes how people manage those uncertain events. (PsycINFO Database Record
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".