Stability of Interdiction Strategies in Quickest Flow Networks
Bibliographic record
Abstract
ABSTRACT Many, if not all, societies have experienced natural disasters, public protests, traffic congestion, and the smuggling of dangerous goods, among other similar events. Such decision‐making and managerial problems can be studied using a game‐theoretic approach on specific networks. This paper studies a type of Stackelberg game known as the Quickest Flow Network Interdiction Problem (QFNIP). This problem considers a predefined volume of source flow, a limited interdiction budget, a network with two distinguished nodes (source and sink), two competing operators, the user, and the interdictor. The user aims to speed the flow from source tosink, while using the available interdiction budget, the interdictor tries to slow down the user's flow as much as possible by removing (interdicting) some network links. A set of removed links constitutes an interdiction strategy. This paper investigates the optimal interdiction strategies and their dependency on the source flow volume. It shows that optimal interdiction strategies depend on the volume of the source flow. This dependency holds up to a sufficiently large threshold of source flow volume, and the optimal strategies remain unchanged when the source flow exceeds that value. An interdiction strategy, independent of the source flow volume, is referred to as stable network interdiction. This paper proposes a practical method to find these stable interdiction strategies, applies it to the Canada Freedom Convoy, and examines the proposed method in both real‐world and randomly generated grid networks.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".