Essays in estimating air transport demand processes and the formation of oligopolies
Bibliographic record
Abstract
This dissertation studies three different topics in estimating air transport demand processes and the formation of oligopolies. Chapter 1 provides an overview of the thesis. Chapter 2 investigates the sensitivity of demand for air travel by singleton passengers, couples, and families. It examines how the demand for air travel by these groups is potentially different. In this study, a compound Poisson structure of the demand of different passenger groups is considered and aggregate demand observations and decompounding techniques are used to estimate demand sensitivity of each group of customers to price, time, season, and the economic cycle. The methodology is applied to Canadian market data and the results indicate there are significant differences among the different groups of customers. In Chapter 3 a new decompounding procedure based on rudimentary number theory is developed. The advantages and disadvantages of this new framework are discussed, and the efficiency of these methodologies for certain class of problems is demonstrated. The framework is capable of decompounding when group sizes are either pairwise co-prime or composed of two elements. Under some conditions, the methodologies are generalized to cases where data are recorded in non-equal intervals. It is also not dependent on a restrictive assumption of having some zero observations that exists in conventional decompounding algorithms such as the Panjer recursion algorithm. Chapter 4 shows how hierarchical decision making and franchising is used as a fine-tuned strategy for brands to both compete aggressively and softly. In a hierarchical decision making process, as a part of long-run plan, the head office of a brand first decides on how many franchises they will grant. At the second stage, the flagships or company owned divisions decide on their level of output and lastly franchises decide how much to produce. We show brands can use this strategy both as a commitment not to compete fiercely with other brands who share the same cost efficiency and to credibly threaten or possibly keep the inefficient brands out of the market. The efficient brands’ incentive to pre-empt the competition is high when either the market size is small or their cost advantage is substantial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".