# Algorithms to Live By - Brian Christian Created: [[2023_08_19]] 15:36 Tags: [[Technology]] [[Advice]] [[Model]] The book explores how computer science concepts can be applied to everyday life and human decision-making. Here's a summary of the main ideas and algorithms discussed in the book: 1. **Optimal Stopping**: When should you stop searching and make a decision? The 37% Rule suggests that you should spend 37% of your time looking and then pick the next best option. This is applicable in contexts like apartment hunting or seeking a life partner. 2. **Explore/Exploit**: When should you try something new (explore) versus stick with what you know (exploit)? The Multi-armed Bandit Problem provides strategies to balance the need for new experiences with the benefits of sticking to known options. 3. **Sorting**: How do we order and organize information efficiently? Algorithms like QuickSort or BubbleSort demonstrate principles that can be applied to decision-making processes like setting priorities. 4. **Caching**: How do we remember what's important and forget what isn't? Using the Least Recently Used (LRU) cache principle, we can decide what information or memories to retain and which ones to discard. 5. **Scheduling**: How can we best manage our time? The Earliest Due Date First and the Moore's Algorithm show how to optimize time and reduce the chances of tasks or deadlines being missed. 6. **Bayes’ Rule**: How should we update our beliefs in light of new evidence? Bayes’ Rule helps us make decisions under uncertainty by allowing us to update our initial beliefs as new data comes in. 7. **Overfitting**: How do we avoid drawing overly specific conclusions from limited data? Overfitting in machine learning warns us against making hasty generalizations based on limited experience. 8. **Relaxation**: How can we get a good-enough solution when the best is unattainable? Relaxation techniques in optimization problems teach us that sometimes, approximating can lead to quicker, yet still satisfactory, answers. 9. **Randomness**: Sometimes, introducing randomness can lead to better decisions, as seen in techniques like Random Sampling or Simulated Annealing. 10. **Networking**: How can we effectively communicate and work within networks? Insights from the Transmission Control Protocol (TCP) in computer networking provide guidance on handling congestion and ensuring reliable communication. 11. **Game Theory**: How do we make decisions in a competitive environment? Concepts like the Nash Equilibrium explain how individuals make choices based on the predicted actions of others. Overall, "Algorithms to Live By" showcases how seemingly complex computer science algorithms can provide profound insights into human behavior and everyday decision-making. It bridges the gap between the digital and human worlds, offering strategies to navigate life's challenges more effectively. ## References - [[37% Rule]]