The Inverse Cournot Effect in Royalty Negotiations with Complementary Patents
Bibliographic record
Abstract
It has been commonly argued that the decision of a large number of inventors to license complementary patents necessary for the development of a product leads to excessively large royalties. This well-known Cournot-complements or royalty-stacking effect would hurt efficiency and downstream competition. In this paper we show that when we consider patent litigation and introduce heterogeneity in the portfolio of different firms these results change substantially due to what we denote the Inverse Cournot effect. We show that the lower the total royalty that a downstream producer pays, the lower the royalty that patent holders restricted by the threat of litigation of downstream producers will charge. This effect generates a moderation force in the royalty that unconstrained large patent holders will charge that may overturn some of the standard predictions in the literature. Interestingly, though, this effect can be less relevant when all patent portfolios are weak making royalty stacking more important.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".