Nash Bargaining

Nash bargaining, a game-theoretic framework, seeks to find mutually beneficial agreements between self-interested agents by identifying solutions that are both Pareto efficient and fair. Current research focuses on extending Nash bargaining to diverse applications, including collaborative machine learning (where data sharing and task allocation are optimized), negotiation simulations using large language models (to understand human-like behavior and biases), and multi-task learning (to resolve conflicting gradient updates). These advancements improve the efficiency and fairness of resource allocation in various settings, impacting fields ranging from data science and artificial intelligence to economics and social sciences.