The field of financial decision making and portfolio optimization is moving towards the development of more sophisticated and risk-aware models. Researchers are incorporating concepts from prospect theory and irrationality to better capture real-world decision-making behaviors. The use of machine learning and reinforcement learning techniques is also becoming increasingly popular, allowing for more accurate predictions and adaptations to changing market conditions. Noteworthy papers in this area include: Risk-aware black-box portfolio construction using Bayesian optimization with adaptive weighted Lagrangian estimator, which proposed a novel Bayesian optimization framework to optimize black-box portfolio management models. Seeing Through Risk: A Symbolic Approximation of Prospect Theory, which introduced a novel symbolic modeling framework for decision-making under risk that merges interpretability with the core insights of Prospect Theory. Bridging Econometrics and AI: VaR Estimation via Reinforcement Learning and GARCH Models, which proposed a hybrid framework for Value-at-Risk estimation, combining GARCH volatility models with deep reinforcement learning.