Decision-making under uncertainty

Practical models and frameworks to navigate ambiguity, reduce blind spots, and make confident high-stakes decisions.

Description

Uncertainty is a constant in modern decision-making. Markets shift without warning, competitors move unpredictably, and the data we rely on is often incomplete or even contradictory. Yet leaders and professionals are expected to choose a course of action, and the cost of a poor decision can be significant.

This course provides a practical toolkit for navigating those moments with clarity and confidence. You will learn to distinguish between risk, where probabilities are known, and uncertainty, where outcomes are unclear. Most real-world choices fall into the latter category, and understanding this distinction is the first step toward better decisions.

You will then explore powerful mental models such as expected value, inversion, second-order thinking, and Bayesian updating. These tools will help you weigh trade-offs, identify hidden risks, and refine your thinking as new evidence emerges. The course also introduces practical frameworks including the OODA Loop and decision trees with scenario planning. These structured approaches show you how to act quickly, adapt to changing conditions, and prepare for multiple outcomes.

Through real-world case studies—including a product launch and Netflix’s content bets—you will see how these methods work in practice. By the end, you will have a disciplined, repeatable process for making smarter choices under uncertainty in business, strategy, and beyond.

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