Asymmetric Information and Economies of Scale in Service Contracting
Bibliographic record
Abstract
We consider outsourcing in two important service settings: call center and order fulfillment operations. An important factor in both is the inherent economies of scale. Therefore, we advance a unifying model covering both applications and study the associated contracting problem under information asymmetry. At the time of contracting, the outsourcing firm, “the originator,” faces uncertainty regarding the demand volume but has private information about its probability distribution. The true demand is quickly observed once the service commences. The service provider invests in capacity before the start of the operation and offers a menu of contracts to screen different types of the originator. Adopting a mechanism design approach, we prove that a menu of two-part tariffs achieves the full-information solution. Hence, it is optimal among all possible contracts (in both settings) because of economies of scale and contractibility of realized demand.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".