MULTI-UNIT, SEALED-BID, DISCRIMINATORY-PRICE AUCTIONS ∗
Bibliographic record
Abstract
As the fiscal agent of the Canadian Federal government, the Bank of Canada uses a sequence of multi-unit, sealed-bid, discriminatory-price auctions to manage excess cash reserves and to sterilize the effects of fiscal operations, thus ensuring that its monetary-policy goals are met. These auctions, which form an important part of the short-term loan market in Canada, provide a unique laboratory within which to investigate bidder behaviour. Within the conditionally-independent, private-values paradigm, admitting bidder asymmetries, we construct a theoretical model of bidder behaviour at multi-unit, sealed-bid, discriminatory-price auctions. Subsequently, we use the notion of best-response to identify sets of bidder values, but the most novel feature of the paper is the development of a framework within which to measure best-response violations. Finally, we apply this framework to bid data from Receiver General auctions, examining whether the observed bids are consistent with bestresponse. In cases when equilibrium best-response does not hold, we measure the expected profits foregone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".