Understanding Expert Choices Using Decision Time
Bibliographic record
Abstract
Laboratory experiments find a robust relationship between decision times and perceived values of alternatives. This paper investigates how these findings translate to experts' decision making and information acquisition in the field. In a stylized model of expert choice between two alternatives, we show that (i) less-commonly chosen alternatives are more likely to be chosen later than earlier; (ii) decision time is higher when the likelihood of choosing each alternative is closer to fifty 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 the editorial setting, where we observe proxies for paper quality and signals available to editors. We document that (i) the probability of a positive decision rises with decision time; (ii) average decision time is higher when our estimated probability of a positive decision is closer to fifty percent; and (iii) paper quality is positively (negatively) related to decision time for papers with Reject (R&R) decisions. Structural estimates show that the additional information acquired in editorial delays is modest, and has little impact on the quality of decisions.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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; both teacher heads agree on what is shown here.
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".