Forecasting Adversarial Actions Using Judgment Decomposition-Recomposition
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911771442282496 |
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| author | Gomez, Yolanda Rios, Jesus Insua, David Rios Vila, Jose |
| author_facet | Gomez, Yolanda Rios, Jesus Insua, David Rios Vila, Jose |
| contents | In domains such as homeland security, cybersecurity and competitive marketing, it is frequently the case that analysts need to forecast adversarial actions that impact the problem of interest. Standard structured expert judgement elicitation techniques may fall short as they do not explicitly take into account intentionality. We present a decomposition technique based on adversarial risk analysis followed by a behavioral recomposition using discrete choice models that facilitate such elicitation process and illustrate its performance through behavioral experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03538 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Forecasting Adversarial Actions Using Judgment Decomposition-Recomposition Gomez, Yolanda Rios, Jesus Insua, David Rios Vila, Jose Methodology In domains such as homeland security, cybersecurity and competitive marketing, it is frequently the case that analysts need to forecast adversarial actions that impact the problem of interest. Standard structured expert judgement elicitation techniques may fall short as they do not explicitly take into account intentionality. We present a decomposition technique based on adversarial risk analysis followed by a behavioral recomposition using discrete choice models that facilitate such elicitation process and illustrate its performance through behavioral experiments. |
| title | Forecasting Adversarial Actions Using Judgment Decomposition-Recomposition |
| topic | Methodology |
| url | https://arxiv.org/abs/2402.03538 |