Regret Dynamic

Regret dynamics study how agents learn and adapt their strategies in repeated interactions, aiming to minimize past decision-making regrets. Current research focuses on developing and analyzing efficient algorithms, such as counterfactual regret minimization and optimistic gradient descent, within various game-theoretic frameworks including Bayesian games, Markov Decision Processes, and Stackelberg games. These advancements improve the understanding of equilibrium convergence in both static and time-varying settings, with implications for algorithmic decision-making, robust reinforcement learning, and the design of fair and efficient systems. The insights gained are relevant to diverse fields, from urban planning and resource allocation to the development of more sophisticated AI agents.