Facing Up Fare War: Generating Competitive Price Models With Gene Expression Programming
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the airline industry, the Revenue and Pricing teams generally spend a considerable amount of time analysing and interpreting the actions of their competitors. Most of the time the analysts have to use their analytical skills to create ad-hoc methods to interpret or find patterns in the fares. In this field, it is key to automate the process, avoid human errors, and add new features that provide accurate fares. Considering this, a gene expression programming algorithm is proposed to carry out this process, returning an interpretable rule set which acts as a recommender system to ease the daunting process done by the pricing teams manually. The proposal was applied to a real scenario with the information provided by the Air Canada airline for five months in Canadian markets. The experimental analysis revealed, by means of non-parametric statistical tests, that the proposed gene expression programming algorithm was key to getting the appropriate features and, hence, accurate and highly interpretable results. The proposal obtained extremely accurate results (around 96% in both accuracy and F1 measure) with a reduction of around 50% in the rule set in many cases.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it