Bibliographic record
Abstract
The paper analyses the relationship between technological collaboration and collusion. Firms can collude or defect on the output market, but with the novelty that there is a possibility of developing a new technology jointly. The development of the new technology is conditional on prior collusion by firms. The paper derives three main results. First, it is found that technological collaboration facilitates product market collusion, but only in the short-run, by creating an equilibrium where firms collude initially in order to develop the new technology, and then defect afterwards. Nonetheless, there is less defection compared with the standard trigger strategy model. Second, it is shown how the equilibrium depends on the discount factor; in particular, delayed defection is sustained for intermediate ranges of the discount factor. Finally, the comparative statics analysis shows how the equilibrium is affected by changes in the environment. An increase in the scope of the market, an increase in the number of firms, and a lower quality technology all contribute to making collusion more difficult. Whereas permanent collusion is affected only by changes in the number of firms, delayed defection is affected also by the scope of the market and the quality of the new technology. The results are shown to extend to Bertrand competition, although in the latter case there is less delayed defection than under Cournot competition.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".