Bibliographic record
Abstract
Major technological changes driving the Fourth IndustrialRevolution combine complementary inventions to form complex innovations.These include the Internet of Things (IoT), 5G mobile communications, artificial intelligence (AI), cloud computing, data analytics, autonomous vehicles, additive manufacturing, and augmented/virtual reality.This article shows that negotiation of patent license contracts fully eliminates many influential antitrust concerns about complementary inventions, including "royalty stacking," "SEP hold-up," "patent thickets," "blocking patents," the "Tragedy of the Anti-Commons," and "regulatory patent pools."Negotiation of patent license contracts implies that total royalties will be less than those charged by a bundling monopoly.Negotiation of patent license contracts in a competitive market avoids distortions from royalties per unit of output and eliminates the multiple-marginalization problem.Negotiation generates contract provisions consistent with contingent royalty arrangements.Negotiation also has important implications for antitrust policy toward patent pools.The analysis shows that patent pools serve to mitigate transaction costs rather than to regulate total royalties.This article suggests that antitrust policy makers should continue to be neutral between negotiation of patent license contracts and patent pools.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".