Multi-Agent Systems: Traffic Management and Pricing Strategies

This lecture presents various projects focused on multi-agent systems, particularly in traffic management and pricing strategies. The first project addresses the redirection of skiers to avoid congestion on slopes and ski lifts, utilizing mathematical modeling to optimize travel time. The assumptions include uniform skiing ability among users and a one-shot game scenario. The second project explores dynamic pricing strategies in ticket sales, analyzing how fluctuating prices affect consumer behavior and social welfare. Game theory is employed to simulate decision-making processes, considering factors like ticket availability and buyer preferences. The third project investigates the application of multi-agent reinforcement learning in energy management, emphasizing the need for efficient resource allocation in smart grids. Lastly, the lecture discusses the optimization of traffic light systems to reduce waiting times at intersections, highlighting the importance of strategic decision-making in urban traffic management. Each project showcases the application of game theory and multi-agent learning to solve complex real-world problems.