Egalitarian Fairness

Egalitarian fairness, a subfield of fair allocation and machine learning, focuses on distributing resources or outcomes to minimize the disparity between individuals or groups, prioritizing the worst-off. Current research investigates the computational complexity of maximizing egalitarian welfare in various settings, including sequential decision-making, cooperative learning, and resource allocation, often comparing it to utilitarian approaches that maximize overall welfare. This research is significant because it addresses fundamental questions of fairness in resource distribution and algorithm design, impacting fields ranging from social choice theory to federated learning and potentially leading to more equitable outcomes in practical applications.