My Good Grief

1. Optimal Stopping: Know When to Commit

The 37% Rule defines a simple series of steps—what computer scientists call an "algorithm"—for solving these problems.

The 37% Rule. This rule suggests that when faced with a series of options and needing to choose the best one, you should look at 37% of the options to establish a baseline, then choose the next option that's better than all you've seen before. This applies to various life decisions:

• Job hunting: Interview for about 37% of your search period without accepting offers, then take the next job that's better than all previous ones.
• House hunting: Look at 37% of available houses, then choose the next one that's better than all you've seen.
• Dating: If you plan to date between ages 18 and 40, start getting serious about commitment at age 26 (37% of the way through).

The 37% Rule balances the risk of stopping too early (missing out on better options) and stopping too late (having passed up the best option already).

2. Explore vs. Exploit: Balance New Experiences and Favorites

There is no life so rich and rare / But one more friend could enter there.

The exploration-exploitation tradeoff. This concept addresses the balance between trying new things (exploring) and sticking with what you know works (exploiting). The optimal strategy changes over time:

• Early in life: Emphasize exploration to gather information and experiences.
• Mid-life: Gradually shift towards exploitation as you identify preferences.
• Late life: Focus more on exploitation, enjoying favorite experiences.

Examples of this tradeoff:

• Restaurants: Try new places vs. return to favorites
• Music: Listen to new artists vs. replay favorite albums
• Career: Learn new skills vs. leverage existing expertise

The key is to find the right balance that maximizes overall satisfaction and success over time.

3. Sorting: Organize Information Efficiently

Scale hurts.

Efficient organization matters. Sorting is fundamental to how computers and humans process information. Key insights include:

• The difficulty of sorting increases dramatically with the number of items.
• Different sorting algorithms are optimal for different situations:
  • Bubble Sort: Simple but inefficient for large datasets
  • Merge Sort: Efficient for large datasets, uses divide-and-conquer
  • Bucket Sort: Fast for certain types of data

Real-world applications:

• Libraries: Organizing books for easy retrieval
• Sports rankings: Determining team standings
• Search engines: Presenting relevant results quickly

Understanding sorting principles can help in personal organization, from managing emails to arranging physical spaces.

4. Caching: Prioritize Readily Accessible Data

A buffer that's operating permanently full gives you the worst of both worlds: all the latency and none of the give.

Optimize accessibility. Caching involves storing frequently used information in a readily accessible place. This concept applies to both computers and human behavior:

• Computer caching: Storing frequently accessed data in fast memory
• Human caching: Keeping important items close at hand

Key principles:

• Least Recently Used (LRU): Remove the item that hasn't been used for the longest time
• Temporal locality: Recently used items are likely to be used again soon

Applications:

• Digital: Web browsers caching frequently visited pages
• Physical: Organizing your desk with most-used items within reach
• Mental: Keeping relevant information "top of mind"

Effective caching can significantly improve efficiency in various aspects of life and work.

5. Scheduling: Optimize Task Management

You must not get off the train.

Strategic task ordering. Scheduling involves deciding the order in which to complete tasks. Key algorithms include:

• Earliest Due Date: Complete tasks in order of their deadlines
• Shortest Processing Time: Do quick tasks first to reduce the number of pending items
• Moore's Algorithm: Maximize the number of tasks completed before their deadlines

Considerations:

• Preemption: Ability to interrupt tasks
• Priority: Importance of each task
• Dependencies: Tasks that must be completed before others

Real-world applications:

• Project management: Organizing workflows
• Personal productivity: Managing daily to-do lists
• Manufacturing: Optimizing production lines

Effective scheduling can significantly improve productivity and reduce stress.

6. Bayes's Rule: Update Beliefs with New Information

Small data is big data in disguise.

Rational belief updating. Bayes's Rule provides a framework for updating beliefs based on new evidence. Key concepts:

• Prior probability: Initial belief before new evidence
• Likelihood: Probability of the evidence given the belief
• Posterior probability: Updated belief after considering new evidence

Applications:

• Medical diagnosis: Updating disease probability based on test results
• Spam filtering: Classifying emails based on content
• Predictions: Estimating probabilities of future events

Bayes's Rule encourages rational thinking by quantifying how much we should change our beliefs in light of new information.

7. Overfitting: Avoid Overthinking and Overcomplicating

Sometimes "good enough" really is good enough.

Simplicity over complexity. Overfitting occurs when a model or decision process becomes too complex, fitting noise rather than underlying patterns. Key ideas:

• Bias-variance tradeoff: Balancing simplicity and accuracy
• Cross-validation: Testing models on unseen data
• Regularization: Penalizing complexity in models

Real-world applications:

• Decision making: Avoiding analysis paralysis
• Relationships: Not overanalyzing every interaction
• Business strategy: Keeping plans flexible and adaptable

Recognizing overfitting can lead to more robust decisions and strategies in various aspects of life.

8. Relaxation: Simplify Complex Problems

Relaxations offer us a number of advantages.

Strategic simplification. Relaxation in problem-solving involves temporarily removing or simplifying constraints to gain insights. Techniques include:

• Constraint relaxation: Removing some problem constraints
• Continuous relaxation: Treating discrete variables as continuous
• Lagrangian relaxation: Incorporating constraints into the objective function

Benefits:

• Provides bounds on optimal solutions
• Offers starting points for more complex problem-solving
• Reveals key problem structures

Applications range from operations research to personal decision-making, where temporarily ignoring certain constraints can lead to creative solutions.

9. Randomness: Harness Chance for Better Solutions

Sometimes the best solution to a problem is to turn to chance rather than trying to fully reason out an answer.

Embrace controlled randomness. Randomized algorithms can often outperform deterministic ones, especially for complex problems. Key concepts:

• Monte Carlo methods: Using random sampling to solve problems
• Simulated annealing: Mimicking physical processes to find optimal solutions
• Genetic algorithms: Evolving solutions through random mutations

Applications:

• Cryptography: Generating secure keys
• Optimization: Finding approximate solutions to hard problems
• Creative problem-solving: Introducing random elements to spark new ideas

Randomness can break us out of local optima and lead to unexpectedly good solutions.

10. Networking: Understand How We Connect

There are no connections in the Internet. Talking about a connection in the Internet is like talking about a connection in the US Mail system.

Connection illusions. Understanding how networks function can provide insights into human interactions and information flow. Key concepts:

• Packet switching: Breaking messages into small, independent units
• Protocols: Rules governing communication
• Bufferbloat: Issues arising from excessive buffering in networks

Implications:

• Communication: Understanding delays and misunderstandings
• Information spread: How ideas and trends propagate
• Resilience: Building robust systems and relationships

Networking principles can inform strategies for effective communication and community building.

11. Game Theory: Navigate Strategic Interactions

Hell is other people.

Strategic thinking. Game theory provides frameworks for understanding and navigating situations where outcomes depend on the actions of multiple parties. Key concepts:

• Nash equilibrium: A state where no player can unilaterally improve their position
• Prisoner's dilemma: A scenario illustrating the conflict between individual and collective interests
• Mechanism design: Creating systems that encourage desired behaviors

Applications:

• Economics: Understanding market dynamics
• Politics: Analyzing international relations
• Personal relationships: Navigating conflicts and cooperation

Game theory offers insights into how to approach complex social and strategic situations.

12. Computational Kindness: Design for Cognitive Ease

We can be "computationally kind" to others by framing issues in terms that make the underlying computational problem easier.

Cognitive consideration. Computational kindness involves designing interactions and systems that minimize cognitive load. Principles include:

• Reducing options: Limiting choices to ease decision-making
• Clear defaults: Providing sensible default options
• Structured information: Presenting data in easily digestible formats

Applications:

• User interface design: Creating intuitive software interfaces
• Communication: Framing requests and information clearly
• Social norms: Developing conventions that reduce cognitive overhead

By applying computational kindness, we can create more efficient and pleasant interactions in both digital and social realms.

Last updated: August 14, 2024