Managing Interdependence-Induced Systemic Risks in Infrastructure Projects
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
Complex by nature, infrastructure megaprojects rarely meet stakeholders’ expectations. A key characteristic of such complexity is the interdependence among different project stakeholders (e.g., contractors) where disruption of one contractor’s work may instigate (system-level) systemic risks, resulting in poor key performance indicators (KPIs) of the whole project. Attributed to the lack of appropriate analysis and quantification tools, managing the systemic risks resulting from such interdependence remains challenging. The current study fills this knowledge gap through proactive systemic risk management (i.e., early identification, analysis, mitigation, and continuous monitoring). The study enhanced and operationalized a previously developed conceptual framework, in which interdependence was quantified through employing complex network theoretic measures. Specifically, the current study correlated contractor interdependence to project KPIs in order to assess associated project systemic risks. Subsequently, a metaheuristic optimization technique was employed to reorganize the project’s schedule—effectively managing interdependence-induced risks. To demonstrate the utility of the developed methodology, a power infrastructure project was considered. Finally, the study provides valuable insights to improve the performance of complex infrastructure projects through proactive systemic risk management, ultimately enhancing the overall project’s hyper resilience (i.e., to interdependence-induced disruptions).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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