Dynamic Production Teams with Strategic Behavior
Bibliographic record
Abstract
We analyze the extent to which intergenerational teams provide information on workers' productivity in the long run. We use a dynamic stochastic framework where wages are reputation-based and consider three possible work arrangements. When agents can only work by themselves we show that some uncertainty persists on their productivity at the steady state. Next, our results indicate that when the same technological shocks affect all teammates, then forcing workers to work together reveals their productivity in the steady state. However, some uncertainty on agents' productivities persist in the long run when technological shocks differ across teammates. We also allow workers to choose between working on their own or in a team. In this case the problem falls in the class of dynamic games. We compute the Nash-equilibrium work strategies, the direction of inter-workers transfers and the steady-state distribution of wages and utility. Elective teams are preferred by high-productivity young workers when technological shocks are specific to each teammate, and maximize the expected utility of a young worker when shocks are perfectly correlated
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".