The search for new drugs: a theory of R&D in the pharmaceutical industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to use a dynamic model of optimal patent design and, in the presence of information externalities, to study the evolution of technological progress in the context of a pharmaceutical industry. Design/methodology/approach A theoretical analysis approach is adopted to drive the paper’s findings. Findings Pharmaceutical firms with an active drug discovery program behave strategically in their R&D and in the product markets. It is shown that a firm holding an earlier-expiring patent only chooses to proceed with R&D activates when the patent it holds expires if the expected discounted payoff net of R&D costs yielded by this action is positive. The expected discounted payoff net of R&D costs obtained by this firm is then decreasing in R&D costs, increasing in the cumulative quality discovered in the past R&D activates, and decreasing in the number of past R&D activities, etc. Originality/value The preceding literature on the topic works with only one brand, the brand with the highest quality. As well, the demand is assumed to be completely inelastic. In the conventional models of patent design, the role of competitive fringe firms is discussed implicitly. The model presented in this research is a rigorous continuous in-time dynamic model. It considers several differentiated products. Furthermore, the demand for a brand is taken to be a function of income, its price, and the prices of other brands. The interaction of the fringe firm with other patent-holding firms is also explicitly considered under this framework.
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.
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 | 0.001 |
| 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 | 0.001 | 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 it