Three Essays on R&D Competition with Spillovers: Theory and Experiment
Bibliographic record
Abstract
This thesis consists of three chapters. The first chapter reports a laboratory experiment on dynamic patent races in an indefinite horizon with complete information. In the experiment, we examine how the players react to a leader/follower or symmetric/asymmetric position as well as the distance between the initial knowledge stock and the target. Our results show that the individual average effort is highest for the players who are in a tie position, second highest for the leaders and lowest for the followers and the spillovers in the previous round significantly increase the players’ investment in the current round. By comparing the first and second half of the session, we observe an overall learning effect on the pure-strategy equilibrium play, but efficiency loss remains throughout the session. \n \nThe second chapter investigates the effect of R&D subsidies on the innovating firms’ quality investment choices and profits as well as social welfare in a duopoly market with product substitutability, demand spillovers and consumers’ quality sensitivity. Taking the non-cooperative and cooperative scenarios into account, the optimal R&D subsidy levels are solved in a way to maximize the social welfare. Compared with no-subsidy, the firms are better off under the R&D subsidy policy. Furthermore, it is always socially beneficial to subsidize the non-cooperative regime or the cooperative agreement. \n \nThe third chapter considers a two-stage strategic R&D model in a duopoly market. In the first stage, two firms decide simultaneously whether to compete or to cooperate by choosing the level of R&D investment that might decrease the investing firm’s production cost and the rival’s cost through the absorptive capacity. In the second stage, after observing the R&D outcome, the two firms play the classical Cournot in order to maximize their own profits. Under the stochastic R&D technology with low or high symmetric absorptive capacity, I find that the difference between the optimal R&D expenditures under defection and those under cooperation becomes larger as the probability of success increases. Regardless of whether the absorptive capacities of the two firms are same or different, except at the critical threshold, the R&D outcomes always align with the prisoner's dilemma situation.
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 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".