Routing Game

Routing games analyze how self-interested agents choose paths in a network, aiming to optimize individual travel times or costs, often leading to inefficiencies. Current research focuses on developing and evaluating novel algorithms, including neural network approaches and mechanisms based on replicator dynamics, to improve solution quality and efficiency in various routing scenarios, such as vehicle routing and crowdsourced information sharing. These studies are crucial for optimizing resource allocation in real-world systems like transportation networks and for understanding the impact of incentive mechanisms on collective behavior. The development of robust benchmark suites and the exploration of incentive-compatible mechanisms are key areas driving progress in the field.