Reducing the risk of intentional domino effects in process plants: A risk‐based minimax strategy
Bibliographic record
Abstract
Abstract Compared with safety assessment, security risk assessment in chemical and process plants is more challenging. On top of uncertain environmental and operational parameters and interdependent failures, which are common in the safety risk assessment of complex systems and infrastructures, there are other uncertain parameters such as the likelihood of attack scenarios and attackers' expected outcomes. As such, the application of probabilistic risk assessment (PRA) techniques, which have long been applied to safety risk assessment and management, to security risk management may result in nonoptimal or suboptimal decisions. In the present study, we will demonstrate how a combination of PRA and game theory may outperform PRA and lead to a more cost‐effective allocation of security measures. For this purpose, the outcome of a dynamic Bayesian network—as a PRA technique—is used as input to the minimax strategy—as a game theoretic strategy—for security risk management of a tank terminal under attacks with a homemade bomb. The proposed risk‐based minimax strategy alleviates the need for estimation of attack likelihoods or attacker payoffs, which would have otherwise been too challenging to estimate if the analyst solely depended on a PRA technique.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".