Bibliographic record
Abstract
Expected revenue and allocative efficiency are two of the most important considerations in auction design. The Revenue Equivalence Theorem provides conditions under which many single-object auctions generate the same expected revenue, but theory provides little guidance when more than one object is sold. In such settings, the question of which auction format or pricing rule generates the most expected revenue or the most efficient allocation remains largely an empirical one. In Canada, the central bank (the Bank of Canada) currently uses a multi-unit, sealed-bid auction format in conjunction with a discriminatory pricing rule to invest excess government cash in term-deposits at a select set of financial institutions. Winners at these auctions pay their tendered bids, so participants shave their bids in equilibrium and winning allocations can be inefficient. We consider what would happen to expected revenues and allocational efficiency were the Bank of Canada to switch from the discriminatory pricing rule to the generalized Vickrey auction (GVA) pricing rule. The GVA is an interesting benchmark because, given private values, an equilibrium exists in which bids are truthful and allocations are efficient.
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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 | 0.009 | 0.036 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".