Dynamic R&D with Strategic Behavior
Bibliographic record
Abstract
We consider a two-player infinite-horizon discrete-time game where the players invest in R&D in order to develop a new technology to reduce production costs. We compute firms' equilibrium R&D investment strategy as a function of the level of knowledge in the economy. The latter changes endogenously with firms' decisions to invest in R&D. We show that firms do not invest in R&D if the knowledge level is too low, while both firms do R&D when the level of knowledge is high. However, there is an intermediate knowledge region where there are two pure Nash equilibria: either no firm does R&D or both firms do R&D. Multiplicity of equilibria leads generally to a challenging selection problem. In our context, it is shown that the case of both firms investing in R&D can be Pareto-dominating for both players. It follows that government actions which allow an economy to increase the level of knowledge above a threshold may be welfare enhancing.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".