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“Forecasting bias” as a book category encompasses works that examine why predictions go wrong and how human and organizational biases distort our view of the future. These books blend psychology, behavioral economics, and data science to unpack errors such as overconfidence, optimism bias, the planning fallacy, confirmation and hindsight biases, survivorship and selection effects, and model risk. They analyze forecasting in domains like business, finance, policy, technology, and epidemiology, showing how judgment, incentives, culture, and flawed methods skew estimates and scenarios. The category also highlights tools to reduce error—calibration, base rates and reference class forecasting, premortems, scenario planning, probabilistic reasoning, Brier scoring, aggregation, and rigorous feedback loops. Readers can expect both cautionary case studies and practical playbooks for building better forecasts, improving decision quality, and designing organizations that learn from predictive failure.

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