Research Joint Ventures with Asymmetric Spillovers and Symmetric Contributions
Bibliographic record
Abstract
The paper proposes a new type of R&D cooperation between firms endowed with asymmetric spillovers, which we call symmetric Research Joint Venture (RJV) cartelization, based on reciprocity in information exchange. In this setting, firms coordinate their R&D expenditures and also share information, but such that the asymmetric spillover rates are increased through cooperation by equal amounts. It is found that this type of cooperation reduces R&D investment by the low spillover firm when its spillover is sufficiently low and the spillover of its competitor is sufficiently high. But it always increases the R&D of the high spillover firm, as well as total R&D (and hence effective cost reduction and welfare). A firm prefers no cooperation to symmetric RJV cartelization if its spillover rate is very high and the spillover rate of its competitor is intermediate. The profitability of symmetric RJV cartelization relative to other modes of cooperation is analyzed. It is found that symmetric RJV cartelization constitutes an equilibrium for a very wide range of spillovers, namely, when asymmetries between spillovers are not too large. As these asymmetries increase, the equilibrium goes from symmetric RJV cartelization, to RJV cartelization, to R&D competition, to R&D cartelization.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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) | 0.004 | 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".