Strategic Capacity Leasing to a Market-Clearing MVNO in Heterogeneous Wireless Markets
Bibliographic record
Abstract
This study constructs a detailed analytical model to analyze strategic interactions between a Mobile Network Operator (MNO) and a Mobile Virtual Network Operator (MVNO) in a diverse wireless market. By integrating group-specific sensitivity to data rates and pricing, along with unobserved user heterogeneity, we encapsulate the intricacies of user behavior and operator decision-making. In contrast to previous studies, our formulation incorporates an MVNO that employs a market-clearing technique, guaranteeing complete market coverage. The MNO can substantially increase its revenue by strategically leasing capacity to the MVNO, fulfilling the MVNO’s net revenue demands and catering to all customer categories. Our numerical results confirm that this approach can outperform traditional, MNO-only solutions, thereby facilitating both inclusive and profitable wireless services.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".