Managing Project Failure Risk Through Contingent Contracts in Procurement Auctions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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