A competitive bidding decision-making model considering correlation
Bibliographic record
Abstract
<p>Many general contractors obtain a majority of projects based on the low-bid award system. A major objective of a competitive bidding model is to determine an optimum markup size so as to maximize the contractor’s long term profit. The new bidding model with explicit consideration of correlation is proposed since this important parameter is not considered in existing bidding models. In this study, the existence of a positive correlation coefficient between any two competitors’ bid ratios was demonstrated. After that, a new competitive bidding model was proposed, and a statistical method in a Bayesian framework was developed. The significance of correlation on probability of winning and optimum markup decisions was investigated. For an illustration purpose, a case study of a bidding Data Set from actual projects was conducted. It has been found that as correlation increases, the probability of winning will increase, and hence an increased optimum markup can be used. In comparison with Friedman and Gates models, the proposed model with consideration of correlation coefficient derives different value of optimum markup which is closer to the real situation of the construction market since the correlation coefficient among competitors’ bid ratios is considered.</p>
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; a candidate call from one teacher head, 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".