Decision-Making Under Uncertainty: Foundations and Risk Analysis

In a world of unpredictable markets and incomplete data, making sound choices requires more than just intuition. This written course introduces you to the structured mathematical and analytical frameworks used to evaluate risks and make optimal choices under uncertainty. By studying these core methodologies, you will transition from guessing to using formal decision-making models, learning to quantify risks, evaluate trade-offs, and apply probabilistic thinking to business, engineering, and strategic challenges. What you'll learn: - Understand foundational probability concepts and how they apply to uncertain outcomes - Build and analyze decision trees to map out complex choice pathways and payoffs - Apply utility theory to account for different risk preferences and attitudes - Perform basic Monte Carlo simulations and scenario analyses to model future variability - Utilize Bayesian updating to refine decisions as new data and information become available - Evaluate real-world scenarios of risk management and strategic planning This course starts with key terminology, basic probability concepts, and foundational definitions before guiding you through structured modeling techniques. You will progress through written explanations, step-by-step analytical breakdowns, and practical decision-making scenarios. Designed for beginners, aspiring analysts, managers, and students, this course requires no prior advanced statistical training. Start building your framework for confident, data-driven decision-making today.