Bibliographic record
Abstract
Abstract This paper examines the classical seat allocation problem under competition between two airlines with different cost structure. The cost asymmetry that has been ignored in the yield management literature can be caused by either operations or distributions. We investigate the decision problem of two airlines offering two identical fare classes under both the simultaneous and sequential allocations. For both allocation cases, we show the existence, uniqueness and stability of pure‐strategy Nash equilibrium under a reasonable condition on the ratios of relative profit margins of the two fare classes. We find that there will be fewer seats protected for the full‐fare class if the discount seats can be booked first. We found that the asymmetry in costs has two effects on the equilibrium solutions: (a) an airline behaves aggressively for the fare class where it enjoys a cost advantage; (b) an airline tends to balance the trade‐offs internally when it has absolute cost advantage in both fare classes. In deriving the collusive solution for both cases for comparative purposes, we discover new insights by solving the two‐flight, two‐fare seat allocation problem with different cost structures on the two flights. In particular, we show that rivalry in full‐fare seat protection leads to a Prisoners' Dilemma for the carriers. Finally, a numerical example is used to illustrate various analytical results.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".