SATS: A Universal Spectrum Auction Test Suite
Bibliographic record
Abstract
For the past 17 years, much of the work on combinatorial auctions (CAs) has used the Combinatorial Auction Test Suite (CATS) by Leyton-Brown et al. (2000). However, CATS does not include a good model for spectrum auctions, which have become the most important application of CAs. In this paper, we make four contributions. First, we propose the Multi-Region Value Model (MRVM) which captures the difficult to model geographic complementaries of large US and Canadian auctions. Second, we also encode our model as a MIP, making the auction's winner determination problem tractable. Third, we introduce a new spectrum auction test suite (SATS), and release it to the public under an open-source license. SATS includes our new MRVM model, as well as six previously introduced value models from the literature. Fourth, using SATS, we evaluate our MRVM model experimentally: after fitting the model parameters to the bidding data from the 2014 Canadian auction, we show that the MRVM model can represent this auction well.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.012 |
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; both teacher heads agree on what is shown here.
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".