A Stackelberg Game Approach for Sponsored Content Management in Mobile Data Market With Network Effects
Bibliographic record
Abstract
A sponsored content policy enables a content provider (CP) to pay a network service provider (SP), and thereby mobile users (MUs) can access contents from the CP through network services from the SP with a lower charge. Thus, more users want to access the contents which potentially generates more profit gain to the CP. In this article, we study the interactions among three entities under the sponsored content policy, namely, the network SP, which is referred to as SP for brevity, the CP and MUs. We model the interactions as a hierarchical Stackelberg game, where the SP and the CP act as the leaders determining the pricing and sponsoring strategies, respectively, and the MUs act as the followers deciding on their content demand. The model incorporates the network effects in a social domain and congestion in a network domain which enables us to obtain insights from the sponsored content policy. In the model, we investigate the mutual interplay between the SP and the CP in three scenarios: 1) sequential competition, where the SP first optimizes its pricing strategy for maximizing its revenue, and then the CP optimizes its sponsoring strategy for maximizing its profit sequentially; 2) simultaneous competition, where the CP and the SP optimize their individual strategies separately and simultaneously; and 3) cooperation, where both providers jointly optimize their strategies with the purpose of maximizing their aggregate payoff. Through backward induction, we derive the unique Nash equilibrium among the MUs. Furthermore, the existence and uniqueness of the Stackelberg equilibrium under three proposed scenarios are validated analytically. Via extensive simulations, it is shown that the network effects significantly improve the utilities of MUs, the profit of the CP, and the revenue of the SP.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 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".