Decision Theoretic

Decision theory provides a framework for making optimal choices under uncertainty, aiming to maximize expected utility or minimize risk. Current research focuses on extending decision-theoretic principles to complex scenarios, such as collaborative learning, large-scale A/B testing, and human-AI interaction, often employing Bayesian methods, empirical Bayes solutions, and minimax approaches. These advancements are improving the design and evaluation of algorithms in diverse fields, from machine learning and robotics to human-computer interaction, by providing principled methods for comparing and optimizing decision-making processes.