The impact of supplier numbers and bid decrements on reverse auction outcomes
Why this work is in the frame
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Bibliographic record
Abstract
Reverse auctions are becoming popular for purchasers as a means of lowering acquisition costs. The challenge for purchasers is to assess which approach is best suited to their business situation. In cases where a reverse auction process is chosen, it is also important to identify the structural characteristics of the reverse auction to achieve the best results. This paper provides some insight into the reverse auction dynamics. While some theoretical insight is available in the literature, there has not been any work that explicitly incorporates the bidding process into a reverse auction model. We develop a simulation model that follows the bidding process to determine expected auction outcomes and present the results. We discuss some strategic elements that purchasers should consider in making a reverse auction decision and suggest some reverse auction specifications which might help lower acquisition costs under certain conditions – notably when the number of participants is small.
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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 | 0.007 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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