Incentive Contracting Under Ambiguity Aversion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper studies a principal-agent model in which the information on future firm performance is ambiguous and the agent is averse to ambiguity. We show that if firm risk is ambiguous, while stocks always induce the agent to perceive a high risk, options can induce him to perceive a low risk. As a result, options can be less costly in incentivizing the agent than stocks in the presence of ambiguity. In addition, we show that providing the agent with more incentives would induce the agent to perceive a higher risk, and there is a discontinuous jump in the compensation cost as incentives increase, which makes the principal reluctant to reset contracts frequently when underlying fundamentals change. Thus, compensation contracts exhibit an inertia property. Lastly, the model sheds some light on the use of relative performance evaluation, and provides a rationale for the puzzle of pay-for-luck in the presence of ambiguity.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it