3 ways to make better decisions -- by thinking like a computer | Tom Griffiths

Summary

In this talk, the speaker explores how computer science concepts, like the '37% rule' and the 'explore-exploit trade-off,' can inform and improve human decision-making. Using examples such as finding a home, choosing a restaurant, and organizing a wardrobe, the speaker illustrates how applying computational strategies can simplify complex problems. The talk emphasizes that while we may not always achieve perfect outcomes, focusing on efficient processes and taking calculated risks can lead to better decisions, highlighting the importance of rational approaches in everyday life.

Takeaways

• 😀 The 37% rule suggests you should look at 37% of the available options before making a decision to maximize your chances of finding the best choice.
• 😀 The 'optimal stopping problem' helps us understand decision-making strategies in situations like searching for a home, which involves knowing when to stop exploring and start choosing.
• 😀 Human decision-making often involves balancing exploration (trying new things) and exploitation (sticking with what we know). This is known as the explore/exploit trade-off.
• 😀 When deciding what restaurant to go to, consider how much time you have: if you’re staying somewhere short-term, exploit your existing knowledge; for longer stays, explore new options.
• 😀 Babies engage in exploration by trying new things, and their behavior is a natural part of their life phase, which aligns with computer science’s exploration phase in decision-making.
• 😀 The explore/exploit trade-off applies to decisions throughout life, from daily choices like where to eat to larger life decisions like relationships and career paths.
• 😀 When organizing your wardrobe, consider the 'least recently used' principle, a strategy used in computer memory systems, to help decide which items to keep and which to discard.
• 😀 The 'least recently used' principle can also apply to managing physical spaces like your desk or home, making them more efficient and helping you access what’s most important.
• 😀 Martha Stewart's four questions about decluttering (how long you’ve had an item, if it still functions, if it’s a duplicate, and when it was last used) can be informed by memory management strategies in computing.
• 😀 Decision-making processes can be improved with computer science strategies, such as using randomness, simplifying problems, and allowing approximations to find good-enough solutions.
• 😀 Following the 37% rule for home buying or similar decisions doesn’t guarantee the best outcome but optimizes the chances of making a well-informed decision.

Q & A

What is the 37 percent rule mentioned in the script?

The 37 percent rule is a strategy for maximizing the chances of finding the best option in situations where you have a limited amount of time or options. It suggests looking at 37 percent of the available options, and then choosing the next one that is better than any you've seen so far.

How does the 37 percent rule apply to finding a home?

When searching for a home, if you look at 37 percent of the available listings and then make an offer on the next place that seems better than anything you've seen, you maximize the chances of finding the best place. This is based on the concept of optimal stopping problems in decision theory.

What does the speaker mean by 'optimal stopping problems'?

Optimal stopping problems are scenarios where you must decide when to stop looking for better options and settle for a good enough option. This concept is studied by mathematicians and computer scientists to understand how decisions can be made optimally given limited time or resources.

What is the 'explore-exploit trade-off' in decision-making?

The explore-exploit trade-off refers to the decision of whether to try something new (explore) or stick with something you already know works (exploit). The trade-off is common in various decisions, from choosing a restaurant to technology companies deciding which ad to show.

How does computer science help with human decision-making?

Computer science provides frameworks and strategies for decision-making, such as understanding optimal stopping rules, the explore-exploit trade-off, and other computational strategies that help in making decisions more efficiently and rationally.

Why do babies tend to try new things, according to the script?

Babies are in the 'explore' phase of their lives, which is why they engage in behaviors like trying new things, such as putting objects in their mouths. This is an essential part of gathering information to understand the world around them.

How does the concept of 'exploration' relate to adults' decision-making?

Adults, like babies, also engage in exploration when facing decisions. If they have more time or opportunities to use the information they gain, it is worth exploring new options, even if they might not be perfect, because the information can help with future decisions.

How can the least recently used principle apply to everyday decisions?

The least recently used principle, commonly used in computer memory management, can be applied to decision-making by helping prioritize things you are most likely to need. For example, in organizing your wardrobe, you might want to keep items you are most likely to use and remove items that you haven’t used in a while.

What can Martha Stewart's wardrobe advice tell us about organizing our space?

Martha Stewart suggests organizing items in your wardrobe by asking four questions to determine if they are worth keeping. However, the computer science approach would prioritize removing the least recently used items, making it easier to access the things you need most.

What role do computer scientists play in solving everyday problems, according to the speaker?

Computer scientists study problems and develop strategies, like optimal stopping rules and memory management techniques, that can be applied to everyday human decisions. Their work helps simplify complex decisions and make them more efficient, offering insights into human behavior and decision-making.