A Dynamic Duopoly Investment Game under Uncertain Market Growth
Bibliographic record
Abstract
We model investments in capacity in a homogeneous product duopoly facing uncertain demand growth. Capacity building is achieved through adding production units that are durable and lumpy and whose cost is irreversible. There is no exogenous order of moves, no first-mover or second-mover advantage, no commitment, and no finite horizon; while building their capacity over time, firms compete `a la Cournot in the product market. We investigate Markov Perfect Equilibrium (MPE) paths of the investment game, which may include preemption episodes and tacit collusion episodes. However, when firms have not yet invested in capacity, the sole pattern that is MPEcompatible is a preemption episode with firms investing at different times, but both have equal value. The first such investment may occur earlier, and therefore be riskier, than socially optimal. When both firms hold capacity, tacit collusion episodes may be MPE-compatible with firms investing simultaneously at a postponed time (generating an investment wave in the industry). We show that the emergence of such episodes is favored by higher demand volatility, faster market growth, and lower discount rate (cost of capital).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".