Sealed-bid versus ascending spectrum auctions
Bibliographic record
Abstract
Abstract We compare the first-price sealed-bid (FPSB) auction and the simultaneous multiple-round auction (SMRA) in an environment based on the recent sale of 900 MHz spectrum in Australia. Three bidders compete for five indivisible items. Bidders can win at most three items and need to obtain at least two to achieve profitable scale, i.e. items are complements. Value complementarities, which are a common feature of spectrum auctions, exacerbate the “fitting problem” and undermine the usual logic for superior price discovery in the SMRA. We find that the FPSB outperforms the SMRA across a range of bidding environments: in terms of efficiency, revenue, and protecting bidders from losses due to the exposure problem. Moreover, the FPSB exhibits superior price discovery and almost always results in competitive (“core”) prices unlike the SMRA, which frequently produces prices that are too low because of demand-reduction or too high because of the exposure problem. We demonstrate the robustness of our findings by considering two-stage variants of the FPSB and SMRA as well as environments in which bidders know their own values but not the distributions from which values are drawn.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".