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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.17863 |
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| _version_ | 1866910889684238336 |
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| author | Ramiah, Preetha Hastie, David I. Bunnin, Oliver Liverani, Silvia Smith, James Q. |
| author_facet | Ramiah, Preetha Hastie, David I. Bunnin, Oliver Liverani, Silvia Smith, James Q. |
| contents | In this paper we demonstrate a new advance in causal Bayesian graphical modelling combined with Adversarial Risk Analysis. This research aims to support strategic analyses of various defensive interventions to counter the threat arising from plots of an adversary. These plots are characterised by a sequence of preparatory phases that an adversary must necessarily pass through to achieve their hostile objective. To do this we first define a new general class of plot models. Then we demonstrate that this is a causal graphical family of models - albeit with a hybrid semantic. We show this continues to be so even in this adversarial setting. It follows that this causal graph can be used to guide a Bayesian decision analysis to counter the adversary's plot. We illustrate the causal analysis of a plot with details of a decision analysis designed to frustrate the progress of a planned terrorist attack. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17863 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Causal Analysis of the Plots of Intelligent Adversaries Ramiah, Preetha Hastie, David I. Bunnin, Oliver Liverani, Silvia Smith, James Q. Methodology In this paper we demonstrate a new advance in causal Bayesian graphical modelling combined with Adversarial Risk Analysis. This research aims to support strategic analyses of various defensive interventions to counter the threat arising from plots of an adversary. These plots are characterised by a sequence of preparatory phases that an adversary must necessarily pass through to achieve their hostile objective. To do this we first define a new general class of plot models. Then we demonstrate that this is a causal graphical family of models - albeit with a hybrid semantic. We show this continues to be so even in this adversarial setting. It follows that this causal graph can be used to guide a Bayesian decision analysis to counter the adversary's plot. We illustrate the causal analysis of a plot with details of a decision analysis designed to frustrate the progress of a planned terrorist attack. |
| title | A Causal Analysis of the Plots of Intelligent Adversaries |
| topic | Methodology |
| url | https://arxiv.org/abs/2503.17863 |