Probabilistic Risk Quantification for Complex Decision Environments

Design a sophisticated decision support framework that can quantify uncertainty and risk across multi-dimensional decision spaces. Develop a probabilistic modeling approach that integrates Bayesian inference, Monte Carlo simulations, and machine learning techniques to generate nuanced risk assessments with clear confidence intervals and interpretable uncertainty metrics.