Bad Choices: How Algorithms Can Help You Think Smarter and Live Happier by Almossawi Ali

Author:Almossawi, Ali [Almossawi, Ali]
Language: eng
Format: azw3
Publisher: Hodder & Stoughton
Published: 2017-04-03T16:00:00+00:00

A non-greedy approach is undoubtedly more sophisticated and often leads to better results. We see an interesting analog for such an approach in military maneuvers where an immediate win, like securing one’s own capital city, is abandoned in favor of a greater win later on—think Russia’s handling of Napoleon’s Grande Armée in 1812. A non-greedy approach might therefore be described as playing the long game. The Wall Street Journal published an article recently about the rise of Scrabble champions in Nigeria, and how their winning edge isn’t necessarily due to a better vocabulary but rather to the counterintuitive tactic of opting for shorter words. Rather than playing seven- and eight-letter words, which are worth more points, the Nigerian Scrabble players found that playing four- and five-letter words resulted in a better overall strategy, seeing as the players were able to keep their most useful letters for upcoming rounds and face fewer potentially bad draws from the bag. This excerpt from the article paints a striking image of the beauty of such an approach:

The Brit broke into a lead with AVOUCHED—an eight-letter bingo for 86 points—but spent the next five rounds managing awkward racks, playing words that scored in the low 30s and high 20s. With QUIZ (93), Mr. Jighere popped ahead. At the final score, which was 449 to 432, the winner’s teammates lifted their champion around the room, singing a Nigerian pop tune: “We Done Win.”

Method 3 mitigates one of the drawbacks of Method 2 by focusing on tasks that are actually critical rather than tasks that are perhaps lower impact because they are less involved. We place tasks or events or whatever else we might have into a list and order them by priority. Note that “priority” might in fact be a function of “time to completion,” which is why it can be said that Method 2 orders tasks by priority too. An application where this equivalence makes sense is your printer. If a printer has a queue of ten fifty-page documents followed by a one-page document, it might make more sense for the printer to prioritize that last job rather than have it wait until the very end. Separating the two methods here is to make the point that a priority can rely on properties other than time.

If a new task were to come in, rather than adding it to the end of the list, we might determine that it goes somewhere in the middle because of its priority. You can imagine how adding tasks in the middle of an already-prioritized list might end up taking an increasing amount of time as you erase other tasks to make room for new ones. In chapter 12, we will talk about how a computer might choose to store such a list, which is often referred to as a priority queue, so that inserts can happen fairly quickly. Often in life, this approach is the most fruitful. Here is how those three methods look as a graph:

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