Fair Resource Allocation

Fair resource allocation aims to distribute resources equitably among multiple agents while maintaining efficiency. Current research focuses on developing online algorithms, often incorporating concepts like α-fairness and regret minimization, to handle dynamic environments and unknown utility functions; approaches include active learning, bilevel optimization, and primal-dual methods. These advancements are crucial for optimizing resource use in diverse applications, from energy grids and communication networks to multi-task learning and human-robot collaboration, improving both system performance and user experience.