What will I learn about: how powerful computer algorithms can be adapted to human decision-making to help solve difficult everyday problems, from apartment hunting to finding a partner?

Discover how powerful computer algorithms can be adapted to human decision-making to help solve difficult everyday problems, from apartment hunting to finding a partner.

What will I learn about: use the 37 percent rule of optimal stopping to eliminate guesswork and know exactly when to commit to a new apartment, job, or relationship?

Learn how to use the 37 percent rule of optimal stopping to eliminate guesswork and know exactly when to commit to a new apartment, job, or relationship.

What will I learn about: the Upper Confidence Bound algorithm to effectively balance sticking with a familiar option or exploring a new one, whether you are investing or trying to maximize your luck?

Master the Upper Confidence Bound algorithm to effectively balance sticking with a familiar option or exploring a new one, whether you are investing or trying to maximize your luck.

What will I learn about: how adaptive strategies can help you evaluate outcomes in real-time, allowing you to quickly abandon failing paths and exploit new information for better results?

Find out how adaptive strategies can help you evaluate outcomes in real-time, allowing you to quickly abandon failing paths and exploit new information for better results.

What will I learn about: why an untidy desk might actually be highly efficient and when you should stop wasting time and energy on excessive organizing?

Understand why an untidy desk might actually be highly efficient and when you should stop wasting time and energy on excessive organizing.

Algorithms to Live By

The Computer Science of Human Decisions

Brian Christian & Tom Griffiths

★ 3.9 / 5(190 ratings)

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Science Motivation & Inspiration

If You're Curious About These Questions...

You should listen to this audiobook

💡Have you ever wondered if there’s a mathematically proven "perfect time" to stop searching and finally commit to a big life decision?
💡Did you know that computer science holds a secret formula for deciding when to stick with your favorite routines and when to take a risk on something new?
💡What if you could use the same logic that powers high-speed processors to master your daily to-do list and eliminate the stress of choice?

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Key Takeaways from Algorithms to Live By

✓Discover how powerful computer algorithms can be adapted to human decision-making to help solve difficult everyday problems, from apartment hunting to finding a partner.
✓Learn how to use the 37 percent rule of optimal stopping to eliminate guesswork and know exactly when to commit to a new apartment, job, or relationship.
✓Master the Upper Confidence Bound algorithm to effectively balance sticking with a familiar option or exploring a new one, whether you are investing or trying to maximize your luck.
✓Find out how adaptive strategies can help you evaluate outcomes in real-time, allowing you to quickly abandon failing paths and exploit new information for better results.
✓Understand why an untidy desk might actually be highly efficient and when you should stop wasting time and energy on excessive organizing.

Learning Tools

Reinforce what you learned from Algorithms to Live By

Mind Map

Algorithms to Live By

Optimal Stopping+

Explore vs. Exploit+

Sorting & Caching+

Scheduling+

Predicting+

Networking+

Game Theory & Mechanism Design+

Limitations+

Quiz — Test Your Understanding

Question 1 of 10

According to the optimal stopping algorithm, what percentage of options should you evaluate to establish a standard before making a choice?

A. 25%
B. 37%
C. 50%
D. 80%

Question 2 of 10

In the context of the 'multi-armed bandit' problem, how does the Upper Confidence Bound algorithm suggest you play slot machines?

A. Stick with one machine until you experience a single loss, then immediately switch to another.
B. Randomly switch machines every few minutes to maximize your exposure to different jackpots.
C. Play the machine with the highest expected value and switch only if the real outcome continually disappoints you.
D. Always play the machine with the smallest jackpot because it is statistically programmed to pay out more frequently.

Question 3 of 10

Which sorting method is described as the least efficient for organizing a large collection, such as a massive library of books?

A. Merge sort
B. Insertion sort
C. Bubble sort
D. Cache sort

Question 4 of 10

How does the 'Least Recently Used' (LRU) algorithm justify leaving a messy stack of papers on your desk?

A. It proves that visual clutter stimulates creative problem-solving and intuitive algorithms.
B. It demonstrates that recently used and important items naturally stay at the top of the pile for quick access.
C. It shows that sorting papers takes more time than the information on the papers is actually worth.
D. It suggests that hiding older documents at the bottom prevents the phenomenon of priority inversion.

Question 5 of 10

If you are running out of time and realize you cannot complete all your scheduled tasks, what does Moore's Algorithm suggest you do?

A. Start with the task that has the closest deadline.
B. Focus on the task that requires the least amount of time to build momentum.
C. Skip the task that requires the most time so you can complete more tasks overall.
D. Multitask by switching rapidly between all remaining tasks to ensure partial completion.

Question 6 of 10

When trying to predict phenomena like wealth distribution, which mathematical pattern is most appropriate to use?

A. The normal distribution (bell curve)
B. The power-law distribution
C. The exponential backoff method
D. The optimal stopping rule

Question 7 of 10

What is the 'Exponential Backoff' method used for in computer networking?

A. Maximizing the total amount of data a server can hold in its solid-state drive.
B. Handling server overloads by waiting and doubling the wait time after each failed attempt to connect.
C. Continuously resending a message at the same speed until it finally breaks through a firewall.
D. Encrypting messages so they cannot be intercepted by a third party.

Question 8 of 10

How does 'mechanism design' differ from traditional game theory in solving problems like employees not taking enough vacation?

A. It relies on offering larger financial bonuses to persuade employees to take time off.
B. It models the probability of employee burnout using Bayes's logic.
C. It changes the rules of the system to force the desirable behavior, such as making vacations mandatory.
D. It pits employees against each other in a prisoner's dilemma scenario to see who will take time off first.

Question 9 of 10

What happens when a predictive model suffers from 'overfitting'?

A. The model becomes too simplistic and ignores crucial variables like genetics or diet.
B. The model perfectly explains the sample data, including its errors, but loses flexibility and fails on new data.
C. The model relaxes its standards too much, resulting in highly inaccurate predictions that are merely 'good enough'.
D. The algorithm requires too much computing power, causing the system to experience priority inversion.

Question 10 of 10

According to the Shortest Processing Time algorithm, what is the best way to tackle a very long to-do list if your goal is to check off as many items as possible?

A. Alternate between hard and easy tasks to maintain mental energy.
B. Delegate the most time-consuming tasks to someone else.
C. List and complete the tasks in order of what can be done the fastest.
D. Tackle the most important and difficult task first thing in the morning.

Algorithms to Live By — Full Chapter Overview

1Recommendation
21 — Algorithms help humans and computers solve problems.
32 — Algorithms can often tell us when to stop pushing our luck.
43 — Mathematical algorithms can help you know when to try something new.
54 — You may not always need help sorting files, but algorithms can assist when you do.
65 — Computers can teach us a lot about organizing data.
76 — Algorithms can help schedule our lives, though they have limits.
87 — The right algorithms can help predict the future.
98 — Algorithms help us exchange messages and manage data overload.
109 — Algorithms can help predict people’s actions and guide their decisions.
1110 — It’s important to understand the limits of algorithms.
1211 — Final summary

Algorithms to Live By Summary & Overview

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.

Who Should Listen to Algorithms to Live By?

Busy people who want to improve their time management
Anyone interested in what algorithms do
People who want to get more organized

About the Author: Brian Christian & Tom Griffiths

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.

Algorithms to Live By

If You're Curious About These Questions...

Listen to Algorithms to Live By — Free Audiobook

Key Takeaways from Algorithms to Live By

Learning Tools

Mind Map

Quiz — Test Your Understanding

Algorithms to Live By — Full Chapter Overview

Algorithms to Live By Summary & Overview

Who Should Listen to Algorithms to Live By?

About the Author: Brian Christian & Tom Griffiths

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