Solving the Station Repacking Problem
Bibliographic record
Abstract
Over 13 months in 2016–17, the US government held an innovative “incentive auction” for radio spectrum, in which television broadcasters were paid to relinquish broadcast rights via a “reverse auction”, remaining broadcasters were repacked into a narrower band of spectrum, and the cleared spectrum was sold to telecommunications companies. The stakes were enormous: the auction was forecast to net the government tens of billions of dollars, as well as creating massive economic value by reallocating spectrum to more socially beneficial uses (Congressional Budget Office 2015). As a result of both its economic importance and its conceptual novelty, the auction has been the subject of considerable recent study by the research community, mostly focusing on elements of the auction design (Bazelon, Jackson, and McHenry 2011; Kwerel, LaFontaine, and Schwartz 2012; Milgrom et al. 2012; Calamari et al. 2012; Marcus 2013; Milgrom and Segal 2014; Dütting, Gkatzelis, and Roughgarden 2014; Vohra 2014; Nguyen and Sandholm 2014; Kazumori 2014). After considerable study and discussion, the FCC has selected an auction design based on a descending clock (FCC 2014c; 2014a). Such an auction offers each participating station a price for relinquishing its broadcast rights, with this price offer falling for a given station as long as it remains repackable. A consequence of this design is that the auction must (sequentially!) solve hundreds of thousands of such repacking problems. This is challenging, because the repacking problem is NP-complete. It also makes the performance of the repacking algorithm extremely important, as every failure to solve a single, feasible repacking problem corresponds to a lost opportunity to lower a price offer. Given the scale of the auction, individual unsolved problems can cost the government millions of dollars each.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".