Bibliographic record
Abstract
Patent holdup can arise when circumstances enable a patent owner to extract a larger royalty ex post than it could have obtained in an arm's length transaction ex ante. While the concept of patent holdup is familiar to scholars and practitioners—particularly in the context of standard-essential patent (SEP) disputes—the economic details are frequently misunderstood. For example, the popular assumption that switching costs (those required to switch from the infringing technology to an alternative) necessarily contribute to holdup is false in general, and will tend to overstate the potential for extracting excessive royalties. On the other hand, some commentaries mistakenly presume that large fixed costs are an essential ingredient of patent holdup, which understates the scope of the problem. In this article, we clarify and distinguish the most basic economic factors that contribute to patent holdup. This casts light on various points of confusion arising in many commentaries on the subject. Path dependence—which can act to inflate the value of a technology simply because it was adopted first—is a useful concept for understanding the problem. In particular, patent holdup can be viewed as opportunistic exploitation of path dependence effects serving to inflate the value of a patented technology (relative to the alternatives) after it is adopted. This clarifies that factors contributing to holdup are not static, but rather consist in changes in economic circumstances over time. By breaking down the problem into its most basic parts, our analysis provides a useful blueprint for applying patent holdup theory in complex cases.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.056 |
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; both teacher heads agree on what is shown here.
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".