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That Not-To-Mathy Explanation of Algorithms You've Been Dreaming About

it would be a mistake to assume a priori that there is an effective mechanized and quantitative process by which good decisions can be made with regard to any particular matter. If someone cannot describe in detail how they would evaluate a teacher, or make a credit decision or a hiring decision or a parole decision, then it’s hard to imagine how they would devise an algorithm that would reliably perform the function in their stead

While sports enthusiasts love to debate the issue, this method of evaluating talent is now widely embraced across baseball, and gaining traction in other sports as well. The Moneyball model works, O’Neil says, for a few simple reasons. First, it is relatively transparent: Anyone with basic math skills can grasp the inputs and outputs. Second, its objectives (more wins) are clear, and appropriately quantifiable. Third, there is a self-correcting feedback mechanism: a constant stream of new inputs and outputs by which the model can be honed and refined. Where models go wrong, the author argues, all three healthy attributes are often lacking. The calculations are opaque; the objectives attempt to quantify that which perhaps should not be; and feedback loops, far from being self-correcting, serve only to reinforce faulty assumptions.

In fact, I walked around for a day and a half (in order to get the most out of my one expensive overnight room) asking people if they’d seen my vagina anywhere. The reactions were mixed and were not always good. In fact once or twice the stares I got were so weird and intense that I was forced to blurt out, “Oh, here it is! In my purse! Just where I left it.” True story.

Vacation in Utrecht | mathbabe