Augmented Probability Simulation Methods for Non-cooperative Games
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We present a comprehensive robust decision support framework with novel computational algorithms for decision makers in a non-cooperative sequential setup. Existing simulation based approaches such as Monte Carlo methods can be inefficient under certain conditions like in presence of a high number of decision alternatives and uncertain outcomes. Hence, we provide a novel augmented probability simulation alternative to solve non-cooperative sequential games. We cover approaches to approximate subgame perfect equilibria under common knowledge conditions, assess the robustness of such solutions and, finally, approximate adversarial risk analysis solutions when lacking common knowledge. The proposed approach can be especially beneficial in application domains such as cybersecurity and counter-terrorism.
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