Bibliographic record
Abstract
The precompetitive R&D literature has viewed cooperative and noncooperative R&D as substitutes. In this paper a more realistic approach is taken, where both cooperative and noncooperative R&D are performed in parallel. In the first stage firms determine the optimal investments in both types of R&D, and in the second stage they compete in output. It is found that information sharing between cooperating firms contributes not only to cooperative R&D, but also to noncooperative R&D. The two types of R&D reinforce each other. The level of cooperative R&D may be higher or lower than noncooperative R&D. In a Cournot duopoly, the share of cooperative R&D lies between 20% and 80% of total R&D, and this share increases with spillovers and information sharing. It is always optimal to subsidize half the costs of cooperative R&D, while the subsidy to noncooperative R&D is unchanged form the standard model. Consumers prefer intermediate levels of spillovers and information sharing, while firms prefer higher levels of spillovers, which entail lower levels of information sharing. La littérature sur la R&D préconcurentielle a toujours considéré la coopération et la non-coopération comme des substituts. Dans ce papier, on adopte une approche plus réaliste, où la R&D coopérative et non-coopérative sont effectuées en parallèle. Dans la première étape, les firmes investissent dans les deux types de R&D. Dans la deuxième étape, elles se concurrencent en quantités. Il est démontré que le partage d'information entre les firmes contribue à la R&D non-coopérative, en plus de contribuer à la R&D coopérative. Chaque type de R&D renforce l'autre, impliquant une complémentarité entre les deux. L'investissement en R&D coopérative peut être supérieur ou inférieur à l'investissement en R&D non-coopérative. Dans un duopole de Cournot, la part de la R&D coopérative se situe entre 20% et 80% de la R&D totale, et cette part augmente avec les externalités de recherche et le partage d'information. Il est optimal de subventionner la moitié des coûts de la R&D coopérative, alors que la subvention à la R&D non-coopérative est inchangée par rapport au modèle standard. Les consommateurs préfèrent des niveaux intermédiaires d'externalités de recherche et de partage d'information, alors que les firmes préfèrent des niveaux plus élevés d'externalités, ce qui implique des niveaux très faibles de partage d'information.
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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 | 0.003 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".