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“Approximation” gathers books that explore the art of being usefully inexact—how we model, estimate, and decide with partial information. It includes popular science on statistics, uncertainty, numerical methods, forecasting, and AI; histories of measurement, maps, and standards; essays on heuristics, trade-offs, and “good enough” design in engineering, economics, and policy; and fiction that leans into ambiguity through unreliable narrators, fragmentary structures, and layered truths. These works examine the gap between the world and its representations, foregrounding error bars, rules of thumb, and iterative refinement. At its core, the category celebrates pragmatic wisdom over perfect certainty, showing how simplification, analogy, and compression help us navigate complexity—and why “close enough” can be a powerful way of knowing.

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