Strategic Decision Making under Uncertainty: R&D and Pricing Strategies in Biopharmaceuticals
Bibliographic record
Abstract
This symposium explores the antecedents, drivers, and impacts of product decision making in the biopharmaceutical industry. While research in strategy and economics has explored how policies impact new product development and how firms compete, many gaps remain in terms of our understanding of decision making and performance. The papers featured are aimed at addressing these gaps. This session will feature the presentation of three papers focusing on: (1) the effect of intellectual property policy change on neglected disease research, (2) the relationship between research portfolio depth and breadth and firm performance, and (3) the drivers of price increases of patented pharmaceuticals. A discussant will unify the themes presented across the papers, provide developmental commentary, and suggest related avenues from future research on product development and portfolio management in the biopharmaceutical sector. Firm-Level Pricing Strategy: When Irrelevant Products Are Relevant Presenter: David Ridley; Duke U. Presenter: Colleen M Cunningham; Duke U. Search Processes and Product Development Success: Evidence from the Global Pharmaceutical Industry Presenter: Nilanjana Dutt; Bocconi U. Presenter: Elena Vidal; Baruch College-The City U. of New York Intellectual Property Rights and Research on Neglected Diseases Presenter: Keyvan Vakili; London Business School
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".