The field of decision making under uncertainty is moving towards the development of more sophisticated risk-aware approaches. Researchers are exploring new frameworks and algorithms that can efficiently balance competing objectives, such as maximizing expected reward and minimizing risk. One of the key challenges is to develop methods that can handle high-stakes decision-making scenarios, where the consequences of suboptimal decisions can be significant. To address this challenge, researchers are proposing novel algorithms and frameworks that can adapt to complex environments and provide more accurate and reliable decision-making. Notable papers in this area include: Risk-Averse Total-Reward Reinforcement Learning, which proposes a Q-learning algorithm for risk-averse total-reward Markov Decision Processes. Interactive Multi-Objective Probabilistic Preference Learning with Soft and Hard Bounds, which presents an interactive framework for guiding users in high-stakes decision-making scenarios. Risk-Averse Best Arm Set Identification with Fixed Budget and Fixed Confidence, which introduces a novel problem setting in stochastic bandit optimization that jointly addresses maximizing expected reward and minimizing associated uncertainty. Best Agent Identification for General Game Playing, which proposes an efficient procedure to accurately identify the best performing algorithm for each sub-task in a multi-problem domain.