Fair Resource Allocation
Overview
The field studies how to divide scarce or indivisible resources among people, groups, or services while balancing equitable outcomes with overall utility. For goods, common approaches include envy-freeness up to one item (EF1), envy-freeness up to any item (EFX), and maximin-share guarantees; for online settings, researchers use primal-dual methods, bandits, and reinforcement learning. These methods are applied to settings such as healthcare, energy distribution, wireless networks, and shared computing capacity, where arrivals, demand, and resource limits may change over time. A central challenge is that fairness guarantees can reduce efficiency or become computationally difficult, and results depend on which fairness measure and operational constraints are chosen.
Latest papers
59 papers11 papers in the last four weeks, up 120% on the four weeks before. 0.1% of all new papers.