Multi-Seller, Multi-Buyer Spectrum Markets: A Self-Declared Valuation Framework
Bibliographic record
Abstract
This paper studies the interaction between multiple infrastructure providers (InPs), with long-term spectrum licenses, and a number of service providers (SPs), new market entrants in search of spectrum. Our motivation stems from the observation that spectrum is often underutilized by its long-term holders, while the new market entrants have demand for, and place a high value on, bandwidth. Here, InPs act as potential sellers/lessors within a secondary market and SPs are the prospective buyers/lessees. A central challenge is the propensity of InPs to set prices to maximize their own profits, ignoring the viability of the SPs and overall market efficiency. To mitigate this issue, we introduce a regulatory framework that imposes taxes on the InPs’ self-declared valuations of their available bandwidth. We include a tax discount for InPs that lease spectrum to SPs, thereby encouraging participation by InPs. Our analytical results, based on wireless channel models, suggest that this regulatory strategy encourages InPs to lower their asking prices, thus enhancing the efficiency of resource allocation. Our supporting numerical results not only validate the proposed regulatory framework’s effectiveness in encouraging InPs to join the market but also illustrate the benefits of improved market efficiency.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".