Fair Division

Fair division studies the allocation of resources among multiple agents in a way that is both efficient and fair, aiming to maximize overall welfare while satisfying fairness constraints like envy-freeness or proportionality. Current research focuses on developing algorithms for online fair division, particularly using contextual bandit models to handle uncertainty in agent preferences and resource values, and exploring the use of Shapley values for equitable reward allocation in collaborative settings. These advancements have implications for resource allocation in various domains, including online platforms, collaborative projects involving AI, and the division of goods and tasks, improving fairness and efficiency in practical applications.