Abstract
Adversarial risk analysis (ARA) provides decision-theoretic arguments to manage uncertainty in competitive decision-making environments. This paper introduces efficient algorithmic approaches to approximate ARA solutions in multi-stage games, covering both sequential and simultaneous settings, through augmented probability simulation. Two examples concerning international piracy and air combat illustrate the proposed methodology.
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Acknowledgments
EU’s Horizon 2020 project No. 101021797(STARLIGHT), the AMALFI FBBVA project, AFOSR award FA-9550-21-1-0239, AFOSR-EOARD award FA8655-21-1-7042, and the Spanish Ministry of Science program PID2021-124662OB-I00. DRI supported by the AXA-ICMAT Chair. JMC supported by a fellowship from “la Caixa” Foundation (ID100010434), whose code is LCF/BQ/DI21/11860063.
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Camacho, J.M., Naveiro, R., Ríos Insua, D. (2025). Algorithmic Decision Analysis for Multi-stage Games with Incomplete Information. In: Freeman, R., Mattei, N. (eds) Algorithmic Decision Theory. ADT 2024. Lecture Notes in Computer Science(), vol 15248. Springer, Cham. https://doi.org/10.1007/978-3-031-73903-3_7
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