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Algorithms to live by
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