Bibliographic record
Abstract
We provide a model of know‐how sharing between competing firms in which each of two firms gets a stochastic innovation in its stock of know‐how in every period. Separately considering the cases when innovations are indivisible and divisible, we examine the nature of the subgame perfect sharing agreements that can obtain. We discover that both stochasticity and indivisibility undermine the ability to support sharing. Furthermore, we find that there are equilibria in which know‐how sharing can be intermittent and that small innovations are more likely to be shared than large ones, when innovations are divisible but not necessarily when they are indivisible. JEL Classification: O30, O33 Partage du savoir faire quand les innovations sont stochastiques. Les auteurs proposent un modèle de partage du savoir‐faire entre entreprises concurrentes dans lequel chacune des deux entreprises obtient une innovation stochastique dans son stock of savoir‐faire à chaque période. En considérant séparément les cas où les innovations sont divisibles et non‐divisibles, on examine la nature des accords de partage parfait qui peuvent se produire dans le sous‐jeu. On montre que la stochasticité et l'indivisibilité minent la possibilité de maintenir le partage. De plus, on découvre que des solutions d'équilibre avec partage de savoir‐faire peuvent jouer par intermittence, et qu'on est davantage susceptible de partager les fruits des petites innovations plus que des grandes quand les innovations sont divisibles, mais pas nécessairement quand elles sont indivisibles.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".