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“Model Assumptions” features books that surface, explain, and scrutinize the simplifying premises that make models possible. Spanning statistics, machine learning, economics, epidemiology, climate science, and risk, these works clarify what assumptions are—idealizations, structural constraints, priors—and how they shape inference, prediction, and policy. The category blends primers, methodological texts, and case studies on identifying, testing, and managing assumptions (e.g., independence, linearity, stationarity, equilibrium, ignorability) using tools like robustness checks, sensitivity analysis, validation, stress testing, and out-of-sample evaluation. Authors probe the consequences of misspecification and model risk, and offer strategies for relaxing assumptions, comparing models, and communicating limitations. Many titles also tackle philosophical and ethical dimensions—bias, fairness, uncertainty, and the limits of generalization. Intended for anyone who builds, evaluates, or relies on models, this genre illuminates the trade-offs that make models useful and the boundaries where they break.

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