Algorithms to Live By (Brian and Tom) - Practical Life Lessons from Computer Science Algorithms

TL;DR: "Algorithms to Live By" shows how computer science concepts can optimize everyday life. Key takeaways include the 37% rule for optimal stopping, balancing exploration and exploitation, using sorting for prioritization, caching for quick access to frequently used resources, and algorithmic scheduling for effective time management. By applying these principles, technologists can enhance decision-making and efficiency in both personal and professional spheres.

As technologists, we understand algorithm’s power to solve complex problems and optimize systems. But what if we could apply algorithmic thinking to our daily lives? That's the fascinating premise of "Algorithms to Live By: The Computer Science of Human Decisions" by Brian Christian and Tom Griffiths.

Let's dive into some key algorithms from the book and explore how they can help us make better decisions and optimize our lives.

1. Optimal Stopping - The 37% Rule

The 37% rule, also known as the "secretary problem" or "optimal stopping theory," is a decision-making strategy that can be applied to various situations, including finding the best parking space in a parking lot. This rule suggests that the optimal time to decide is after considering approximately 37% of the available options.

Example: Finding the Best Parking Space

Imagine driving into a long, straight parking lot with a single row of spaces. You want to find the best possible spot, which is closest to the entrance of the building you're visiting. However, you can only move through the lot and not back up or return to a previous space once you've passed it.

The 37% rule is a decision-making strategy for finding the best option, such as the optimal parking space, when you have limited choices and cannot return to a previous option. To apply this rule:

1. Calculate 37% of the options and observe this initial portion to set a quality benchmark.

2. After passing the first 37%, choose the option that exceeds the benchmark.

3. Select the last available option if no option surpasses the benchmark.

This rule balances the trade-off between exploring options and the risk of passing up a good choice. Observing the first 37% allows you to gather information to set a reasonable standard. Then, by choosing the first option that exceeds this standard, you maximize your chances of selecting the best possible choice while minimizing the risk of missing it. Although it doesn't guarantee to find the absolute best option, the 37% rule provides a simple and effective decision-making strategy in various situations with limited options and no ability to return.

2. Explore/Exploit - Balancing Novel and Familiar

The explore-exploit trade-off is a fundamental concept in decision-making that involves balancing the choice between novel options (exploration) and familiar options with known outcomes (exploitation). This dilemma arises in various contexts, from foraging for food to making financial investments or deciding which restaurant to visit.

Exploration vs. Exploitation: Finding the Right Balance

When making decisions, we face a trade-off between exploring new options and exploiting familiar ones. Exploration involves seeking novel alternatives to gather information, while exploitation means sticking with known options to maximize immediate rewards.

Key Factors Influencing the Balance

1. Uncertainty: Higher uncertainty encourages exploration to reduce it.

2. Temporal stability: Stable environments favor exploitation while changing environments require exploration.

3. Prior knowledge: More knowledge leads to exploitation, and less knowledge encourages exploration.

4. Potential gains and losses: Perceived gains from exploration and losses from foregoing immediate rewards affect the balance.

Striking the right balance is crucial for optimal decision-making. It allows gathering enough information to make informed choices while maximizing rewards over time. The ideal balance varies depending on context, goals, and individual preferences.

3. Sorting - Organizing Your Life

Sorting is a fundamental concept in computer science that involves arranging items in a specific order based on a comparison criterion. In everyday life, sorting can be applied to organize various aspects, such as tasks, emails, or physical spaces. By prioritizing and categorizing items, you can first tackle the most critical or time-sensitive matters, ensuring efficient progress.

One popular sorting algorithm is the "Eisenhower Matrix," which categorizes tasks into four quadrants based on their urgency and importance:

1. Urgent and Important: Tasks that require immediate attention and have significant consequences if not completed.

2. Not Urgent but Important: Tasks that contribute to long-term goals should be scheduled for completion.

3. Urgent but Not Important: Tasks that demand attention but do not contribute to overall objectives. These should be delegated or minimized.

4. Not Urgent and Not Important: Low-priority tasks can be postponed or eliminated.

Applying this sorting method to your to-do list lets you focus on the most critical tasks and avoid getting bogged down by less important ones.

4. Caching - Leveraging Frequently Used Resources

Caching is a technique used in computer systems to store frequently accessed data in a quickly accessible location, reducing the time and resources needed to retrieve it. This concept can be applied to optimize physical and digital spaces in daily life.

In the physical world, caching can be implemented by organizing your workspace to keep frequently used tools, documents, or reference materials within easy reach. This reduces the time and effort spent searching for these items when needed.

Similarly, in the digital realm, you can leverage caching by:

- Using bookmarks or favorites in your web browser to quickly access frequently visited websites.

- Creating shortcuts or aliases for your computer's commonly used files or folders.

- Utilizing a password manager to store and quickly retrieve login information for various accounts securely.

By implementing caching techniques, you can streamline your workflows and boost productivity.

5. Scheduling - Making the Most of Your Time

Efficient scheduling is essential for managing time and resources effectively. Algorithmic approaches, such as the "Earliest Deadline First" or the "Shortest Processing Time" methods, can help prioritize tasks and optimize productivity.

The "Earliest Deadline First" algorithm prioritizes tasks based on their due dates, tackling the most time-sensitive items. This ensures that important deadlines are met and helps maintain steady progress.

The "Shortest Processing Time" algorithm focuses on completing tasks that require the least amount of time or effort first. By knocking out quick tasks, you can build momentum and create a sense of accomplishment, motivating you to tackle larger, more complex tasks.

In practice, you can combine these scheduling algorithms by using a priority queue system:

1. List all your tasks and assign each a priority based on urgency and importance.

2. Begin with the highest-priority tasks that have the nearest deadlines or can be completed quickly.

3. As new tasks arise, insert them into the queue based on their priority.

4. Continuously update and re-evaluate the queue to ensure you always work on the most pressing tasks.

By applying these algorithmic scheduling techniques, you can effectively manage your time, meet deadlines, and maintain a strong sense of progress in your personal and professional life.

Conclusion

"Algorithms to Live By" shows how algorithmic principles can optimize decision-making and problem-solving in everyday life. Technologists can leverage these insights to boost efficiency and effectiveness personally and professionally.