Project Networks and Reallocation Externalities
Bibliographic record
Abstract
A project involves several participants—including clients, contractors, and subcontractors—that work concurrently on multiple projects and allocate resources among them. This interdependency creates a network of otherwise-unrelated projects. We map the network of U.S. government projects involving over 150,000 participants. We show that a seemingly localized disruption, affecting only one project site, eventually causes delays across unrelated projects. This is because participants opportunistically reallocate resources into disrupted projects, at the expense of other projects, triggering a domino effect of further reallocations in the network. Thus, the costs of on-site disruptions end up being shared by multiple participants in the network, rather than being fully absorbed by the affected project. Performance-based incentives, which reward contractors for timeliness, exacerbate these externalities by encouraging self-interested resource reallocation. This paper was accepted by Karan Girotra, operations management. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) [Grant RGPIN-2017-04523]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01574 .
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".