Broad Cross-License Agreements andPersuasive Patent Litigation: Theory andEvidence from the Semiconductor Industry
Bibliographic record
Abstract
In many industries broad cross-license agreements are considered a useful method to obtain freedom to operate and to avoid patent litigation. In this paper I study the previously neglected dynamic trade-off between litigating and cross-licensing that firms face to protect their intellectual property. I present a model of bargaining with learning in which firms’ decisions to litigate or crosslicense depend on their investments in technology specific assets. In particular the model predicts that where firms’ sunk costs are higher, their incentive to litigate and delay a cross-license agreement is lower. In addition, the bargaining game shows how firms with intermediate values of asset specificity tend to engage in inefficient "persuasive litigation". Using a novel dataset on the US semiconductor industry I obtain empirical results consistent with those suggested by the model. Combining model intuition with some empirical figures, I evaluate possible effects of the currently debated patent litigation reform.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".