This article is an excerpt from the Shortform book guide to "Algorithms to Live By" by Brian Christian and Tom Griffiths. Shortform has the world's best summaries and analyses of books you should be reading.

Why is it so difficult to make decisions? Do you often regret your decisions, thinking you should have known better?

Decision-making is a cognitively taxing process, especially when the future is uncertain and the stakes are high. But it doesn’t have to be this way. According to Brian Christian and Tom Griffiths, humans already have all the tools to make smart decisions. In their book Algorithms to Live By, they explain how to make better decisions using computer algorithms.

Let’s take a look at four algorithms intended to help you make better decisions.

1. How to Know When to Settle

Christian and Griffiths’s first algorithm is: To choose the best from a series of options, explore without committing for the first 37%, then commit to the next top pick you see. This algorithm is designed to solve something mathematicians call an “optimal stopping problem”—when faced with a series of options, when do you settle down and commit to the opportunity in front of you if you don’t know what opportunities will be available in the future?

For example, imagine you’re looking for a job and know your skills are in high demand. After a couple of days of searching, you receive an offer out of the blue that’s better than any of the available positions you’ve seen so far. However, it doesn’t have everything you’re looking for. Do you take it or keep searching for better options?

According to Christian and Griffiths, statisticians have determined that the optimal way to solve this problem is to initially reject all opportunities, exploring your options to get a sense of what quality looks like. Then, at a certain point, commit to the next option that’s better than any you’ve seen so far. By calculating the probability that you pick the best option available for every possible “pivot point” from exploration to commitment, researchers have determined that you should explore for the first 37% of options, then commit to the next best opportunity.

2. How to Optimize Your Life

Christian and Griffiths’s next algorithm is a broader directive that applies to any area of your life you want to improve: To optimize your life, pursue whatever opportunity has a chance to be the greatest.

The authors frame life as a complex “multi-armed bandit” problem, referring to a model computer scientists use in machine learning. The multi-armed bandit is a theoretical experiment in which a decision-making agent is presented with a row of slot machines (“one-armed bandits”) and must try out different machines, learning from the outcomes to figure out which will pay off the most.

Christian and Griffiths explain that the multi-armed bandit problem’s optimal solutions are called “Upper Confidence Bound” algorithms, which recommend making decisions based on your options’ best-case scenarios. Pursue whatever opportunity in life has the potential to pay out the most, even if you think it’s extremely unlikely, since the only way to know for sure whether or not it’ll pay off is to test it yourself. Then, if you’ve given something a shot and determined that it’s not worth your while, adjust accordingly and shoot for the moon somewhere else.

3. How to Predict the Future

The next algorithm posed by Christian and Griffiths addresses the problem of an unpredictable future: To make better predictions, first, use your prior knowledge of the situation to estimate the chances of something happening, then adjust based on observable data. This strategy of using your prior beliefs to analyze the evidence you have is called “Bayes’s Rule.”

For example, if you want to predict when you’ll receive a raise at work, you might begin by asking a coworker how long it took for them to get a raise, then adjust that estimate based on how you think your boss views your performance.

4. Why You Should Make Less Informed Decisions

Christian and Griffiths’s final algorithm to aid decision-making is as follows: To make better decisions, consider less information.

With this algorithm, the authors address the problem of overfitting. In statistics and machine learning, “overfitting” occurs when a model takes too many variables into account, resulting in faulty understanding. Christian and Griffiths argue that, in the same way, if you consider too many variables when making a decision, you’ll “overfit,” overestimating the impact of insignificant information and underestimating the details that really matter.

According to Christian and Griffiths, the trick to conquering overfitting is to consciously restrict the amount of information you consider when making decisions. Identify one or two factors that matter the most and ignore everything else. For example, you may decide what job to take solely based on how much you expect to enjoy the work.

Make Better Decisions With Computer Algorithms

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Like what you just read? Read the rest of the world's best book summary and analysis of Brian Christian and Tom Griffiths's "Algorithms to Live By" at Shortform .

Here's what you'll find in our full Algorithms to Live By summary :

• How to schedule your to-do list like a computer
• Why making random decisions is sometimes the smartest thing to do
• Why you should reject the first 37% of positions in your job search

Darya Sinusoid

Darya’s love for reading started with fantasy novels (The LOTR trilogy is still her all-time-favorite). Growing up, however, she found herself transitioning to non-fiction, psychological, and self-help books. She has a degree in Psychology and a deep passion for the subject. She likes reading research-informed books that distill the workings of the human brain/mind/consciousness and thinking of ways to apply the insights to her own life. Some of her favorites include Thinking, Fast and Slow, How We Decide, and The Wisdom of the Enneagram.