Forecasting Adversarial Actions Using Judgment Decomposition-Recomposition
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 i...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/347929 |
| Acceso en línea: | http://hdl.handle.net/10261/347929 http://arxiv.org/abs/2402.03538v1 |
| Access Level: | acceso abierto |
| Palabra clave: | Statistics - Methodology |
| Sumario: | 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. |
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