The idea
Real problems are defined partly by deadlines, latency, resources, incomplete data and other constraints. A theoretically imperfect approximation may solve the actual problem better.
Worked example · illustrative
Moving out by Sunday
Situation. You need to leave your flat empty on Sunday. The moving van arrives that morning, and you have two evenings left to pack. You’d like to sell the furniture you’re not taking and get a good price for each piece.
The tempting conclusion. I’ll list everything separately and wait for decent offers. There’s no point giving away things I could sell.
What changes the view. Taking photos, answering messages, and arranging collections all take time—and a buyer might not turn up. Selling every piece for a good price could leave you with furniture still in the flat on Sunday. You might have time to sell the most valuable items and arrange a confirmed donation collection for the rest, if one is available before you leave.
What this supports. Getting less money for some things may be worth it if it means the flat is empty on time. A sale only helps if the buyer can collect before you leave. If you can get a few extra days or have somewhere to store things, you may have more room to wait for better offers.
Useful when
Watch for
“Pragmatic” shouldn't become an excuse for ignoring requirements that genuinely determine success. Sometimes complexity really is necessary.
Where this came from
Pommerman, Competitions, Applied machine learning