Beyond Dynamic Pricing: Dynamic Product Configuration with Auction/Negotiation Mechanisms
Bibliographic record
Abstract
Reverse auctions are one of the standard exchange mechanisms used in procurement. In many situations heterogeneous products and services that are auctioned require multi-attribute auctions. Often these goods are produced and delivered by the winning bidder after the conclusion of the auction. In such cases the price and other attributes are interrelated. This means that the key assumption of auction theory that the buyers and the sellers have quasi-linear utilities does not hold. The relationship between the price and other attributes is illustrated here with a simple exchange in which the buyer's utility is linear and the sellers’ utilities are Cobb-Douglass production functions with increasing returns to scale. Even in this case, the contract curve is a convex function so the auction does not maximize social welfare. This means that a reverse auction is an inefficient mechanism. Moreover, efficient winning bids can be improved in cases when side-payments are possible. The decrease in the buyer's utility when another efficient solution is selected may be offset by the seller's side-payment. The search for such an alternative and side-payments requires that the buyer and the sellers engage in post-auction negotiations.
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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 | 0.005 | 0.011 |
| 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 | 0.004 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".