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Augmented probability simulation methods for sequential games
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DOI：10.1016/j.ejor.2022.06.042.png)
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Abstract
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We present a robust framework with computational algorithms to support decision makers in sequential games. Our framework includes methods to solve games with complete information, assess the robust-ness of such solutions and, finally, approximate adversarial risk analysis solutions when lacking complete information. Existing simulation based approaches can be inefficient when dealing with large sets of fea-sible decisions; the game of interest may not even be solvable to the desired precision for continuous decisions. Hence, we provide a novel alternative solution method based on the use of augmented prob-ability simulation. While the proposed framework conceptually applies to multi-stage sequential games, the discussion focuses on two-stage sequential defend-attack problems.(c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Decision analysis
Sequential games
Augmented probability simulation
Adversarial risk analysis
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