Integrating buyer and supplier objectives in an iterative combinatorial auction for procurement
Bibliographic record
Abstract
Combinatorial auctions face a trade-off between allocative efficiency and practicality. While achieving allocative efficiency relies on strong economic assumptions that may be difficult for bidders to meet, compromising it for practicality can raise concerns about the auctioneer’s commitment to bidders’ profit maximisation goals. Several evidence-based scenarios report that inter-organisational relationships are adversely affected due to the bidders’ increasing suspicions of the auctioneer’s opportunism. In this paper, we design an iterative procurement combinatorial auction that ensures a steady increase in the winning bidders’ profits throughout the auction process. We analytically prove the convergence of this auction and identify its termination conditions. Complementing our theoretical findings, extensive numerical experiments evaluate the convergence rate, computational costs, and the dynamic interplay between costs and profits within this auction framework. Overall, our results show a substantial increase in the winning bidders’ average profits at the cost of a marginal rise in procurement costs. We illustrate that heightened competitiveness among bidders correlates with improved profit outcomes and convergence rates. The increased profits for bidders, validated both analytically and computationally, signal the auctioneer’s commitment to their welfare which helps enhance the overall appeal of the auction and promote the auctioneer’s resilience against potential supply chain disruptions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".