Bibliographic record
Abstract
Abstract We study a two‐player two‐fare‐class static single‐period capacity allocation game with complete information. Nonnested (partitioned) booking limit policies are investigated in both noncooperative and cooperative situations. We show the existence of unique Nash equilibrium in the noncooperative situation. In the cooperative game, we analyze the cost saving of the two players and investigate the concavity of the objective function. For both noncooperative and cooperative settings, we assume the demands to be a truncated normal distribution and provide a comprehensive sensitivity analysis to discover the effects of unit revenue, rejection cost, and transfer rate on the equilibrium solution. Our numerical experiments show that the nonnested model can be a good approximation to the nested booking limit model. For the cooperative setting, we identify conditions that give rise to improvements in the total system revenue. Finally, under each game‐theoretic setting, we present the managerial implications of our solutions along with numerical examples.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 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 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".