Simplification is the discipline of removing everything that isn’t earning its place. Most users of a heavy modeling tool only ever touched a fraction of it; most models carry structure no one uses. The simple move — build on your own requirements, reuse only the fraction you actually need, strip the rest — is not laziness, it is precision. What remains is easier to understand, cheaper to maintain, and far harder to get wrong. The bar is Einstein’s: as simple as possible, but not simpler. Simplification is not dumbing down; it is finding the smallest form that still carries the meaning. Cut a necessary distinction and you have oversimplified; keep an unnecessary one and you have hidden the signal in noise. The skill is knowing which is which — and that comes from having decomposed the problem first. Simplification is the twin of decomposition: break the whole into the right parts, then make each part as plain as it can be. Together they are what lets a complex landscape be modeled clearly instead of merely completely.
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