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Algorithms to Live By
The Computer Science of Human Decisions
Algorithms to Live By (2016) is a practical and useful guide that shows how algorithms have much more to do with day-to-day life than you might think. And not just that; they can also lead to a better life by helping you solve problems, make decisions and get more things done.
- Busy people who want to improve their time management
- Anyone interested in what algorithms do
- People who want to get more organized
Brian Christian is a best-selling author and renowned public speaker who specializes in cognitive science. His work has won numerous awards and he’s been honored by the Academy of American Poets. He’s also a contributor to The New Yorker, Wired, The Guardian and many scientific journals.
Tom Griffiths, an award-winning professor of cognitive science and psychology, leads the Computational Cognitive Science Lab at the University of Berkeley. He has published more than 150 scientific papers.
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Algorithms to Live By
The Computer Science of Human Decisions
- Read in 18 minutes
- Audio & text available
- Contains 11 key ideas
Synopsis
Algorithms to Live By (2016) is a practical and useful guide that shows how algorithms have much more to do with day-to-day life than you might think. And not just that; they can also lead to a better life by helping you solve problems, make decisions and get more things done.
Key idea 1 of 11
Algorithms help both humans and computers to solve problems.
If you’re someone who tries to stay abreast of today’s technology, you probably already know that computers use algorithms all the time to solve problems. But you may have asked yourself, “What exactly is an algorithm?”
The word actually dates back to the ninth century, when it was first used by the Persian mathematician Muhammad al-Khwarizmi. But the use of algorithms can be traced roughly four thousand years back, to the Sumerian civilization.
Simply put, an algorithm is a finite series of steps that help solve a problem – and it’s a technique we use all the time.
Even a recipe can be thought of as an algorithm: you follow a series of instructions to get the desired result, a delicious meal. The same can be said for the pattern you follow to knit a scarf or put together some Ikea furniture.
And when you’re putting together a list of pros and cons to decide whether or not to accept a job offer or to make a big decision, you’re also following what’s known as an intuitive algorithm.
By their very nature, the intuitive algorithms that humans use aren’t precise. We use them in times of uncertainty to make the best decision we can, like weighing the potential benefits against the risks of jumping into a new business investment.
Therefore, these intuitive algorithms may seem rather subjective and random compared to the mathematical algorithms a computer uses, though they basically provide the same solution.
Take the unpleasant task of apartment hunting. Most people go into this process with a set of criteria in mind: a minimum amount of space, a certain distance from school or work, a maximum amount of rent. When these conditions are met, that’s when you take the next step and sign the lease.
This is essentially the same method that computer algorithms use, and in the next blinks we’ll explore how these methods can work for you.
Key ideas in this title
- Algorithms help both humans and computers to solve problems.
- Most of the time, algorithms can tell us when to stop pressing our luck.
- Mathematical algorithms can help you decide when it’s time to explore something new.
- You don’t always need help sorting your files, but if you do, algorithms can help.
- When it comes to organizing data, there’s a lot you can learn from computers.
- Algorithms can help us schedule our lives, but they also have their limits.
- The right algorithms can help you predict the future.
- Algorithms help us to exchange messages and handle data overload.
- There are algorithms to help figure out what people will do and guide them when making decisions.
- It’s important to know that algorithms have their limits.
- Final summary
