Timeliness, Accuracy, and Relevance in Dynamic Incentive Contracts
Bibliographic record
Abstract
We examine managerial performance measures from the perspective of timeliness, accuracy, and relevance in multi-period incentive problems. Although many insights are general, we employ a simple linear framework where managerial actions do not affect risk. We compare and contrast consumption risk for a manager’s preferences with single and multiple consumption dates, respectively. We consider both full commitment to and renegotiation of long-term contracts. Under full commitment, timely and accurate information is usually relevant and desirable; the only differences arise from the modeling of managerial preferences, through the manager’s consumption risk. In particular, the timeliness of performance reports can be irrelevant; then, delaying reports is desirable if it can increase their accuracy. Under renegotiation of long-term contracts, the timeliness of information release relative to renegotiation is essential. Any information released prior to renegotiation is incorporated into an ex post efficient (renegotiated) contract and is particularly useful in insuring the manager against future consumption risk. Delayed reporting destroys this insurance value and can make late reports irrelevant, independent of the modeling of managerial preferences. But timely reports can create ex ante inefficient action incentives for managers, and then accuracy can be costly as well.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".