On simulating the resilience of military hub and spoke networks
Bibliographic record
Abstract
Hub and spoke networks, while highly efficient, are fragile to targeted attacks: removal of the central hub destroys connectivity of the network. This fragility has led to the assertion that these networks are not suited to military distribution systems. However, military supply chains have redundancy induced by heterogeneous transportation modes (e.g., road, marine, and air) leading to enriched connectivity over a pure hub and spoke structure. In this paper a global military (hierarchical) hub and spoke network model is developed; the topological resilience of such networks are probed by stochastically sampling an ensemble of networks and simulating both random and targeted edge knockout, and the network properties relevant to resilience measured. It is found that such networks are resilient to continual attack and loss (network erosion), performing well relative to preferential (scale free) and random network benchmarks. This regime of network erosion is descriptive of modern asymmetric warfare. 1
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".