Contingency Game

Contingency games analyze decision-making under uncertainty, aiming to develop strategies robust to unforeseen events or "contingencies." Current research focuses on improving the efficiency and reliability of contingency screening methods, often employing machine learning techniques like neural networks and reinforcement learning to handle high-dimensional problems and generate realistic, adversarial scenarios. These advancements have significant implications for various fields, including power grid management, autonomous systems, and even legal contract analysis, by enabling more efficient and reliable planning in the face of uncertainty.