Optimization-Based Methods for Improving the Accuracy and Outcome of Learning in Electronic Procurement Negotiations
Why this work is in the frame
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Bibliographic record
Abstract
Empirical observations as well as theoretical analysis suggest that negotiation outcome in buyer-supplier situations can be improved by having accurate knowledge about the behavior of one's counterpart (i.e., negotiation partner). Yet, there is a paucity of research works dealing with the incorporation of learning methods into electronic procurement technologies, especially methods that can work with small amounts of information. This paper presents an application of nonlinear optimization for learning the parameters of a common negotiation decision function. Then, to show the usefulness of learning in a procurement-negotiation interaction, we outline a reaction algorithm that seeks to improve outcome. Detailed computational results with both the learning and reaction algorithms are conducted to demonstrate the viability of our approach.
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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 | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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