Differentiated Bidders and Bidding Behavior in Procurement Auctions
Bibliographic record
Abstract
Why do bidders in buyer-determined procurement auctions often bid above the lowest observed bid over the course of the auction? Are such bidding patterns meaningful? In this research, the authors propose that because bidders are differentiated in their value to the buyer and competition in these auctions is anonymous, bidders infer their potential quality advantage or disadvantage through their observation of competitive bids and incorporate this information into their responses and price bids. Using point-by-point bid data from two industrial procurement auctions, the authors show that bidders appear to be making inferences about their own implied quality differentials and adjust their bidding strategies and bidding aggression accordingly. Specifically, they find that high-quality bidders tend to be more aggressive in bidding against potentially higher-quality competition and less aggressive when bidding against potentially lower-quality competition. In contrast, low-quality bidders appear aggressive regardless of their implied quality in relation to the competition. The authors conclude with a discussion of implications for management and auction design.
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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 | 0.050 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".