The Usefulness of Imperfection

ImperfectToday Tallys Yunes shared his thoughts about Theory versus Practice in creating real-world decision models. A few quotes: “models aren’t perfect, and that’s perfectly OK. There’s a reason why business analytics is known as “the science of better” rather than “the science of provably optimal.” More often than not, it is impossible to capture all nuances of a real-life problem into a mathematical model. Therefore, solutions produced by such a model are to be taken with a grain of salt and cautious optimism.

Imperfect models create imperfect solutions that can still be useful. Having a solution in the ballpark of good answers is better than looking at a blank slate and not knowing where to begin. Solving a model many times with a range of input values improves your understanding of how certain numbers relate to others. As your understanding of the problem improves, that (initially) counter-intuitive solution starts to make sense. Improved understanding leads to revising your initial assumptions and even realizing that what you thought mattered is secondary.

You are learning about your problem in the process of trying to solve your problem.

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