The field of decision-making under uncertainty is rapidly advancing, with a focus on developing innovative methods to improve the accuracy and efficiency of decision-making processes. Researchers are exploring new approaches to address complex problems, such as multi-objective optimization, bandit optimization, and fair division. Notably, there is a growing interest in developing algorithms that can handle uncertain disruptions, adversarial attacks, and biased data. These advancements have significant implications for real-world applications, including online advertising, recommendation systems, and resource allocation. Some noteworthy papers in this area include: EGA, which proposes a unified end-to-end generative framework for industrial advertising systems, demonstrating its effectiveness in offline and online experiments. Best Arm Identification with Possibly Biased Offline Data, which introduces the LUCB-H algorithm to address the challenge of biased offline data in best arm identification problems, showing improved performance over standard LUCB algorithms.