Fair Allocation

Fair allocation studies the equitable distribution of resources, whether divisible goods (like cake) or indivisible items (like jobs or healthcare resources), among individuals or groups with potentially differing needs or entitlements. Current research focuses on developing algorithms and mechanisms that balance fairness with efficiency, often employing techniques from optimization, game theory (e.g., Nash equilibria), and machine learning (e.g., reinforcement learning, Lyapunov optimization). These advancements have implications for diverse applications, including resource allocation in healthcare, fairness in algorithmic decision-making, and equitable distribution of goods in crisis situations, improving both efficiency and social equity.