Auction Based

Auction-based mechanisms are increasingly used to optimize resource allocation in diverse settings, primarily aiming to achieve efficient and fair distribution while incentivizing truthful participation. Current research focuses on developing sophisticated auction designs, often incorporating machine learning techniques like deep reinforcement learning and Bayesian optimization, to handle complex constraints and dynamic environments, such as those found in federated learning and multi-robot systems. These advancements have significant implications for various fields, improving resource utilization in areas ranging from online advertising and cloud computing to autonomous systems and the burgeoning metaverse.