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"Choice under uncertainty" is a cross-disciplinary book category that explores how individuals, organizations, and algorithms make decisions when outcomes are unknown or probabilistic. Titles in this category cover the theory and practice of risk and ambiguity — from expected utility and Bayesian inference to prospect theory, heuristics, and robust or adaptive strategies — and apply these ideas in domains such as economics, finance, public policy, management, health, and AI. Books range from rigorous mathematical and statistical treatments of optimal decision rules to experimental and behavioral studies of how real people deviate from normative models, plus practical guides to risk assessment, forecasting, and decision support tools. Overall, this category helps readers understand the trade-offs, models, and methods for choosing effectively when certainty is unattainable.

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