First Choice Maximality

First-choice maximality (FCM) focuses on optimizing resource allocation by maximizing the number of agents receiving their top preference, a problem relevant across diverse fields from assignment problems to influence maximization in networks. Current research explores efficient algorithms and model architectures, such as those leveraging neural networks, to achieve FCM while simultaneously considering fairness and efficiency constraints, often employing techniques like Markov decision processes or maximal frequent itemset analysis. These advancements have implications for improving resource allocation in various applications, including computer vision, robotics, and control systems, by providing methods to find optimal solutions that also satisfy other important criteria.