Multiattribute Procurement Auctions: Efficiency and Social Welfare in Theory and Practice
Bibliographic record
Abstract
One of the standard assumptions in auction theory is that preferences can be represented with quasilinear utility. This assumption is of particular significance in reverse auctions, which are used in procurement. This paper presents an analysis of quasilinear utilities and their implications. Building on observations of scholars in economics and decision sciences, who note that quasilinear preferences may be rare in practice, the paper shows that in the procurement of goods and services, price is often interrelated with costs. When preferences can be represented with convex or concave utilities, the alternatives in which the buyer's surplus is maximized are different from those that maximize social welfare. The result is that reverse auctions may cause a significant loss of social welfare, which is of particular significance for public organizations. The analysis of concave efficient frontiers in the utility space shows that it is possible to determine deals that yield greater social welfare than the winning bids. If the winning seller is willing to share the increase in utility with the buyer who faces a loss, then these alternatives can produce, for both the buyer and the seller, utility values that are higher than the utility values produced by the winning bid.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".