Algorithms to Live By Summary (7/10)

The 37% Rule

The 37% rule defines a time to stop searching when looking for an apartment, or a partner. The idea here is to figure out how much time you have and stop your search when 37% of that time has elapsed and commit to a choice.

The Explore/Exploit Trade-off

This tells us about when we should try new things and when we should abandon our pursuit and – instead enjoy our favorite things. For example, if you’re conflicted with having to choose which restaurant you want to go to – the best thing to do would be to figure out how much time you have left in the city you’re in. If you’re new to the city and will be spending a lot of time there, it’s best to explore as many new options as possible. But, if you’re about to leave a city you’ve spent a long time in – it’s best to go to your favorite restaurants and savour your favorite choices instead.

LRU (last recently used) beats FIFO (First in First Out)

Imagine you’re trying to organize your house and trying to live like a minimalist. So, you want to figure out what to throw out and what to keep. The LRU rules stipulates that you should throw out the items you’ve used the least recently and doing so would yield better results than throwing out your oldest items (FIFO).

In their efforts to find computer science metaphors for the human mind, the authors go on to compare our memory to caches. When we miss or forget something, it’s akin to a cache miss. Other ideas include over fitting – which suggests that simplicity is often superior to complexity when interpreting results – and other things. They give the example of fencing. The purpose of fencing was to learn to defend yourself in a duel, but now, because of the complex scoring systems, the game has lost its purpose and has become about scoring points. The techniques that used to be good in duels are not necessarily the techniques that would be good in a fencing competition.

Another concept is buffer-bloat. A crepe kiosk with too many customers waiting is better off creating a cut-off point that stops serving customers after a certain number, the same way it’s better to have a Skype call live with a shaky signal to perfect sound with a 10 second delay. It’s better to have less delay and lower capacity than more delay and higher capacity.

“Algorithms to Live By” was an enjoyable read – although I suspect I would I have enjoyed it a lot more if I was more knowledgeable about computer science, since the premise of the book is to draw interesting comparisons between solving problems in computer science and the real world. It also offers an impressive list of concepts on decision making, sorting, and planning.

Because the authors constantly transition through innumerable concepts, you can’t but feel a lack of cohesion and proper narrative flow. Nonetheless a good read with many important lessons to consider, and interesting multi-disciplinary parallels that yield interesting insights into how we behave and how we try to behave better.