Congestion Game

Congestion games model scenarios where multiple agents compete for shared resources, aiming to minimize individual costs while acknowledging the impact of others' choices. Current research focuses on developing efficient algorithms to find optimal or near-optimal solutions, particularly exploring tax mechanisms and informational interventions to improve social welfare and address equity concerns in settings like traffic management. This involves employing diverse model architectures, including Markov decision processes and bilevel programming, along with algorithms like exponential weights and gradient descent methods. The field's impact spans theoretical advancements in game theory and algorithm design, with practical applications in areas such as traffic flow optimization, resource allocation, and multi-agent systems.