Understanding Expert Choices Using Decision Time
Bibliographic record
Abstract
Laboratory experiments show a robust relationship between decision time and the perceived value of the selected alternative.We ask how these findings translate to decision-making in the field when delay arises from an endogenous decision to collect further information.In a stylized model of choice between two alternatives, we show that: (i) the less commonly-chosen alternative is more likely to be selected after a longer delay; (ii) decision time peaks when the probability of either choice is 50 percent; and (iii) the ultimate quality of the chosen alternative may increase or decrease with decision time, depending on whether earlier or later signals are more informative.We test these predictions in three settings.The first is an editorial setting where we observe proxies for paper quality and some of the signals available to editors.We document that (i) the probability of a positive decision rises with decision time; (ii) average decision time has an inverse-U shaped relation to the predicted probability of a positive decision, with a peak near fifty percent; and (iii) paper quality is positively (negatively) related to decision time for papers with Reject (R&R) decisions.We present corroborating evidence from two other settings: one involving the decisions of loan officers; the other involving young adults' responses to a survey question on marriage expectations.
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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 | 0.014 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".