Nash-stackelberg game perspective on pricing strategies for ride-hailing and aggregation platforms under bundle mode
Bibliographic record
Abstract
The growing popularity of aggregation platforms has attracted widespread attention in the ride-hailing market in recent years. In order to obtain additional orders by charging commissions and slotting fees, many ride-hailing platforms choose to bundle with aggregation platforms. Unlike traditional reseller electronic channels, the bundle channels may affect pricing of platforms, service levels of drivers, market demands and they may further impact on profits. These different attitudes raise an interesting and key question about the influence of bundle channels in ride-hailing platforms. In this paper, we propose an analytical framework for pricing strategies of ride-hailing and aggregation platforms under bundle mode and analyze their pricing process from the perspective of Nash and Stackelberg games, where the platforms serve as leaders to determine optimal prices through Nash equilibrium and the drivers serve as followers to provide optimal service levels. Through sensitivity analysis of service levels and costs, we capture the distribution trends of profits between the platforms. Based on some numerical examples and results analysis, some interesting managerial insights on pricing of ride-hailing and aggregation platforms are gained.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".