Core-Selecting Secondary Spectrum Auctions
Bibliographic record
Abstract
In a secondary spectrum market, the utility of a secondary user often depends on not only whether it wins, but also which channels it wins. Combinatorial auctions are a natural fit here to allow secondary users to bid for combinations of channels. In this context, the VCG mechanism constitutes a generic auction that uniquely guarantees both truthfulness and efficiency. There also exists related auction design that relaxes efficiency due to perceived complexity issues, and focuses on truthfulness. Starting with new empirical evidences on the complexity issue, we propose to design core-selecting auctions instead, which resolve VCG's vulnerability to collusion and shill bidding, and improve seller revenue. While the VCG type of auctions are unique in guaranteeing both efficiency and truthfulness, we prove that our core-selecting auctions are unique in guaranteeing both efficiency and shill-proofness, and always outperform VCG auctions in terms of seller revenue generated. Employing linear programming and quadratic programming techniques, we design two payment rules for minimizing the incentives of bidders to deviate from truth telling.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".