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Algorithms to live by

  1. Learnings fromm the book
  2. Actionables

The book is about how you can learn from computer algorithms and apply them in your daily life.

Learnings fromm the book

  • Optimal Stopping: Stop after 37% (time or quantity). Till 37% try new things, after that grab whatever’s the best you find ASAP. Look then leap rule.
    • Parking is not just a optimization problem of spots. It’s a complete process of fuel, energy, time and generates congestion.
  • Explore vs Exploit: Explore when you will have time to use the resulting knowledge, exploit when you’re ready to cash in.
    • Exploration in itself has value since it gives you new information
    • Select possible adventures based on their potential to be good, not factoring in their potential to be bad
  • Sorting: The search sort trade off suggests that its often more efficient to leave a mess
    • Don’t waste time sorting when you don’t search often
  • Caching: What things to keep?
    • Ask yourself: How long have I had it, Does it still function, Is it a duplicate of something I already own, When was the last time I wore it or used it?
    • LRU principle: Simply put an item back at the very front of the list — then the total amount of time you spend searching will never be more than twice as long as if you’d known the future
    • Best guess of the future is the mirror image of the past
  • Scheduling: Before having a plan, think what should be the optimizing metric
    • Plan by earlier due date, or minimizing the number of items late
    • Every time a new work comes in, divide it’s importance by the amount of time it will take. If it’s greater than the current work, drop it and take this
  • Know what type of distribution you are against and then predict
    • Power Law: Multiplicative, Normal: Average, Erlang: Additive
  • If the factors we come up with first are likely to be the most important ones, then beyond a certain point thinking more about a problem is not only going to be a waste of time and effort — it will lead us to worse solutions
  • If you can’t solve a hard problem, solve an easier version and use that as a starting point
  • Acknowledge others when they are speaking, presenting. Be sure to give regular feedback
  • Game theory: Be wary of the cases where
    • public information > private information
    • you know more about what people are doing instead of why they are doing it
      • you are more concerned about fitting the consensus than fitting the facts

Actionables

  • Explore vs Exploit principle, take risks, find the upper confidence level
  • Don’t sort what you don’t search
  • Batch things
  • Let go of things