Algorithms to Live By: 8 key ideas
The core ideas of Algorithms to Live By, each in under a minute, written in our own words. Want the full depth? Get the book.
- IDEA 1 OF 8
Computer science can guide daily choices
Christian and Griffiths argue that many everyday problems, such as finding a flat or organising a cupboard, resemble problems computer scientists have studied.
Algorithms developed for computers can offer surprising advice for human life.
- IDEA 2 OF 8
Know when to stop looking
The authors explain optimal stopping, such as deciding when to stop searching for a flat.
One rule suggests looking at about the first thirty-seven percent of options, then choosing the next one better than all before.
- IDEA 3 OF 8
Balance exploring and exploiting
The authors describe the explore-exploit trade-off: trying new options versus sticking with known favourites.
Young people may explore more, while older people may exploit. The right balance, they say, shifts with how much time you have left to enjoy what you find.
- IDEA 4 OF 8
Sorting is not always worth it
The authors discuss sorting, noting that it can be costly.
Sometimes it is better not to sort everything. If you rarely search for things, the effort of keeping everything in order may cost more than it saves.
- IDEA 5 OF 8
Keep what you use near
The authors discuss caching, keeping frequently used items close at hand.
A pile of recently used papers can be efficient. That is because the things used most recently are the ones you are most likely to need again soon.
- IDEA 6 OF 8
Schedule wisely
The authors discuss scheduling strategies, such as doing short tasks first or prioritising important ones.
Different goals require different rules. For example, the best rule depends on whether you want to finish the most tasks or avoid the biggest delays.
- IDEA 7 OF 8
Don't overfit
The authors warn against overfitting, making decisions based on too many details.
Simpler rules often work better. A model that matches every small detail of the past may fail to predict the future, because it mistakes chance for a pattern.
- IDEA 8 OF 8
Game theory explains interactions
The authors discuss how game theory explains situations where people's choices depend on others.
Designing better rules can help. In some situations everyone is better off if the rules change, even when no single person can change the outcome alone.
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