Augmented Probability Simulation Methods for Sequential Games

TL;DR

This paper introduces a new augmented probability simulation framework to efficiently solve and analyze large-scale, multi-stage sequential games with incomplete information, improving computational robustness and approximation accuracy.

Contribution

The paper develops a novel augmented probability simulation method tailored for large, complex sequential games, especially under incomplete information conditions.

Findings

Efficiently solves large-scale sequential games

Assesses robustness of game solutions

Provides approximate adversarial risk analysis

Abstract

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 robustness 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 feasible 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 probability simulation. While the proposed framework conceptually applies to multi-stage sequential games, the discussion focuses on two-stage sequential defend-attack problems.