Chapter 3 is about Recognition. This is an important skill in real-world practices of data analytics. It is to recognize the same abstracted form embedded in different real-world problems. No matter how different the problem looks, we hope to leverage existing models and solutions that have been proven effective for the forms that are recognizable in the problem. This is why we say the same model/theory could be applied in multiple areas51 Another practical metaphor is: a model is a hammer, and applications are nails..
This is not to say that a real-world problem is equivalent to an abstracted problem. A dialectic thinking is needed here to understand the relationship between a real-world problem and its reduced form, an abstracted formulation. On one hand, for a real-world problem to be real-world, it always has something that exceeds the boundary of a reduced form. On the other hand, for a real-world problem to be solvable, it has to have some kinds of forms.
Many operations researchers believe that being able to recognize these abstracted forms holds the key to solve real-world problems effectively52 Some said, formulation is an art; and a good formulation contributes more than \(50\%\) in solving the problem.. For some abstracted forms, indeed we have studied them well and are confident to provide a sense of “closure.” It takes a sense of closure to conclude that we have solved a real-world problem, or at least we have reached the best solution as far as our knowledge permits. And we have established criteria to evaluate how well we have solved these abstract forms. Those are the territories where we have surveyed in detail and in depth. If to solve a real-world problem is to battle a dragon in its lair, recognition is all about paving the way for the dragon to follow the bread crumbs so that we can battle it in a familiar battlefield.