Mixed Oligopoly under Demand Uncertainty
Bibliographic record
Abstract
In this paper we introduce product demand uncertainty in a mixed oligopoly model and reexamine the nature of sub-game perfect Nash equilibrium (SPNE) when firms decide in the first stage whether to lead or follow in the subsequent quantity-setting game. In the non-stochastic setting, Pal (1998) demonstrated that when a public firm competes with a domestic private firm, multiple equilibria exist but the efficient equilibrium outcome is for the public firm to follow. Matsumura (2003a) proved that when the public firm's rival is a foreign private firm, leadership of the public firm is both efficient as well as SPN equilibrium. Our stochastic model shows that when the leader must commit to output before the resolution of uncertainty, multiple SPNE is possible. Whether the equilibrium outcome is public or private leadership hinges upon the degree of privatization and market volatility. More importantly, Pareto-inefficient simultaneous production is a likely SPNE. Our results are driven by the fact that the resolution of uncertainty enhances the profits of the follower firm in a manner that is well known in real option theory.
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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 | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".