Managing Project Failure Risk Through Contingent Contracts in Procurement Auctions
Bibliographic record
Abstract
Procurement auctions are sometimes plagued with a chosen supplier's failing to accomplish a project successfully. The risk of project failure is considerable, especially when the buyer has inadequate information about suppliers ex ante and the project can only be evaluated at the end. To manage such uncertainty, a model of competitive procurement and contracting for a project is presented in this paper. We study a setting in which suppliers differ in both the costs to fulfill the project and the types reflecting their success probabilities. To screen suppliers, the buyer invites suppliers to specify a two-dimensional bid composed of the proposed cost and a penalty payment if the delivered project fails to meet the requirements. We find that a quasi-linear scoring rule can effectively separate suppliers regarding their types. We then study the efficient and optimal design of the scoring rule. The efficient design internalizes the inferred information on suppliers' type and essentially ranks suppliers based on the expected total cost to the buyer. In the optimal design, the buyer may or may not under-reward suppliers' high success probability, depending on the balance between suppliers' success probabilities and the associated cost distributions. Interestingly, it is always optimal for the buyer to possibly award the project to suppliers with low success probability to promote the competition, even when the difference in suppliers' success probabilities is huge. We show that, compared to standard auctions, the procurement auctions with contingent contracts can significantly improve both social welfare and the buyer's payoff.
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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 | 0.026 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".