Forecast Accuracy and Consistent Preferences for the Timing of Information Arrival
Bibliographic record
Abstract
ABSTRACT We study a principal's choice of whether to produce an imperfect forecast about a firm's outcome either before or after an agent's effort choice. The early forecast affects the agent's effort choice, which means the forecast can also be used to infer information about the effect of the agent's effort on outcome. The late forecast is more accurate because, by working hard, the agent also learns about productivity, implying that the late forecast has an additional performance measurement role. With verifiable information, the principal prefers a late forecast when the agent's effect on the accuracy of the forecast is either large or small. The agent has consistent preferences when the agent's effect on the accuracy of the late forecast is not too large. With unverifiable information, the agent's information rents imply that the principal cannot use either forecast as a performance measure. Thus, the accuracy of the late forecast has no effect on the principal's preference. However, if the accuracy of the early forecast is low and its decision‐making function is diminished, the principal prefers a late signal.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".