2008), "Testing for Common Valuation in the Presence of Bidders with Informational Advantage," working paper presented at the Penn State auctions conference
Bibliographic record
Abstract
We develop a test for common values in auctions in which some bidders possess information about rivals ’ bids. Information about rival’s bids causes a bidder to bid differently when she has a private value than when there is a common valuation component or her value depends on rivals ’ information. In a divisible good setting, such as treasury bill auctions, bidders who obtain information about rivals ’ bids in the private values model use this information only to update their prior about the distribution of residual supply. In the model with a common value component, they also update their prior about the value of the good being auctioned. We use these differential updating effects to construct our test. The proposed test displays good performance in Monte Carlo studies. We then apply it to data from Canadian treasury bill market, where some bidders have to route their bids through dealers who also submit bids on their own. We cannot reject the null hypothesis of private values in our data for 3-months treasury bills, but we reject private values for 12-months treasury bills. Furthermore, we use the data to estimate the value to a dealer from obtaining information about non-dealers ’ bids. We find that the extra information leads on average to an increase in payoff equal to 0.5 of a basis point, or 27 % of the expected surplus of dealers from participating in these auctions.
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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 | 0.039 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".