Analysis of Airfare during Pandemic: A Multi-Agent Based Modeling Approach
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
The impact of the pandemic on the airline industry has been severe. Various factors such as lockdowns, travel bans, travel restrictions and passenger footfall led to changes in the airfare. This is not limited to a few years of the pandemic as there is a possibility of a similar situation recurring in the future. To address this situation and to assess future possibilities, this paper is an attempt to apply multi-agent simulation and modeling on airfare in pandemic conditions. The objective of this paper is to develop a multi-agent model for airfare during the pandemic. We also ran simulation on the developed model based on the pandemic information available from news articles. The proposed multi-agent model has long-term utility and can be used by the airline industry, the travelers, the governments, academia, and research organizations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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