Hierarchical Game

Hierarchical game theory models complex interactions by structuring decision-making into nested levels, allowing for the analysis of strategic choices at different scales. Current research focuses on applying this framework to diverse areas, including autonomous vehicle coordination, online marketplaces, and traffic optimization, often employing reinforcement learning, multi-agent systems, and game-theoretic algorithms like Stackelberg games and hypergradient methods. These advancements enable more efficient and robust solutions for problems involving multiple interacting agents with varying levels of autonomy and information, impacting fields from transportation to artificial intelligence.