A correlated bidding model for markup size decisions
Why this work is in the frame
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Bibliographic record
Abstract
Whereas competitive bidding models have been studied for more than five decades with many factors being considered and statistical methods proposed, the correlation among bids of different companies and its effects on markup decisions have not been explored. Through a multivariate competitive bidding model, the significance of the correlation is investigated in this paper. Mechanistic arguments and probabilistic analysis based on a breakdown of cost estimates show that bid ratios are positively correlated to one another. This fact is then incorporated as a priori information into a Bayesian statistical method to estimate the correlation coefficients from historical data with missing values. The effectiveness of the proposed Bayesian method has been demonstrated through a case study. The proposed bidding model has a flexible mathematical structure, which allows one to better characterize actual varying bidding patterns. It also includes the Friedman and Carr models as its special cases. Moreover, through the use of the streamlined Bayesian method, the new model can be implemented easily in practice.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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