Elimination Voting

Elimination voting, a process of sequentially removing candidates based on various criteria, is a subject of ongoing research across diverse fields. Current work focuses on improving the efficiency and accuracy of elimination processes, exploring techniques like uncertainty quantification to refine candidate selection and employing machine learning models, including Tsetlin Machines and deep reinforcement learning, to optimize elimination strategies in contexts ranging from machine learning model training to tournament design. These advancements aim to enhance the effectiveness and fairness of elimination-based decision-making in various applications, from data cleaning and model optimization to resource allocation and competition structures.