Should Online Content Providers Be Allowed To Subsidize Content?—An Economic Analysis
Bibliographic record
Abstract
Internet service providers (ISPs) are experimenting with a business model that allows content providers (CPs) to subsidize Internet access for end consumers. In this study, we develop a game-theoretical model to analyze the effects of this sponsorship of consumer data usage. We find that the ISP’s optimal network management choice of data sponsorship crucially depends on market conditions, such as the revenue rates of CPs and the fit cost of consumers. If the fit cost is low, the ISP will either allow both CPs to subsidize consumers’ Internet access, or will allow only the more competitive CP to subsidize, depending on the per-consumer revenue generation rates of CPs. If the fit cost is high, it is in the ISPs interest not to allow any subsidization. We also identify conditions under which the ISP’s network management choices of data sponsorship deviate from social optimum. These results should be of interest to the telecom industry as it searches additional revenue models, and to online CPs competing for customer loyalty. It should also be of interest to policymakers investigating into this issue.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".