Augmented Probability Simulation Methods for Non-cooperative Games

We present a robust decision support framework with computational algorithms for decision makers in non-cooperative sequential setups. Existing simulation based approaches can be inefficient when there is a large number of feasible decisions and uncertain outcomes. Hence, we provide a novel alternative to solve non-cooperative sequential games based on augmented probability simulation. We propose approaches to approximate subgame perfect equilibria under complete information, assess the robustness of such solutions and, finally, approximate adversarial risk analysis solutions when lacking complete information. This framework could be especially beneficial in application domains such as cybersecurity and counter-terrorism