Managing Airfares Under Competition: Insights from a Field Experiment
Bibliographic record
Abstract
Airfares evolve dynamically, giving rise to a so-called price path. This price path is controlled via two levers: (i) a fare ladder, which defines a set of airfares before the selling season, and (ii) revenue management algorithms, which control how fares evolve along the ladder during the season. We hypothesize that the current policies to control both levers—which do not account for quality differences between competing airlines—give rise to an inefficient price path and, accordingly, a loss of potential revenue. We substantiate this hypothesis via a field experiment. By partnering with an airline, we introduced quality considerations in the design of fare ladders, across 5,000 itineraries, to show that current ladder-design policies indeed lead to a suboptimal price path. We also show that this inefficiency can be mitigated by incorporating quality differences between competing airlines. This creates a smoother (and more profitable) price path. This paper was accepted by Vishal Gaur, operations management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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; both teacher heads agree on what is shown here.
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".