Market composition and experience in common-value auctions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This study investigates whether market composition affects individual bidding and the aggregate market in first-price sealed-bid common-value auctions. It compares all-inexperienced markets with only inexperienced bidders, all-experienced markets with only experienced bidders, and mixed markets with both types. On average, there is no market-composition effect for both experienced and inexperienced bidders. When controlling for gender, a market-composition effect appears for inexperienced subjects: Men bid more aggressively in mixed than in all-inexperienced markets, and women bid more aggressively in all-inexperienced markets. On the aggregate level, the all-inexperienced market is the most aggressive with highest winning bids; the all-experienced market is the least aggressive. The mixed market is in between: Both experienced and inexperienced win auctions in this market, but experienced bidders win less auctions than they should.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it