The Dynamics of Incentives, Productivity, and Operational Risk
Bibliographic record
Abstract
Abstract This paper develops a dynamic principal-agent model and applies it to understand changes in labor productivity and operational risk. Our analysis demonstrates the importance of matching the terms of the job contract to the technology. Such issues would be especially important in service industries and in the knowledge-based economy where discretionary effort tends to play a greater role. We show that the production technology needs to be characterized by at least two parameters: one parameter which measures output independent of the worker’s effort and a second parameter which measures the effect of the effort. We solve for the Renegotiation-Proof Nash Equilibrium. We show that there can be a tension between increasing expected productivity and controlling costs per worker. Our analysis also adds to the growing interest in “operational risk”, which is associated with human actions. The closed form solutions provided by our model provide a natural way to consider the impact and possibility of this type of risk. Our analysis demonstrates why the effect of a negative event should be considered relative to a concept of normal which is based on an equilibrium, that uncertainty in the external environment enables (but does not cause) operational risk events and that both the equilibrium and the effects vary with the production technology.
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.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| 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".