Adrian's Reviews > Algorithms to Live By: The Computer Science of Human Decisions
Algorithms to Live By: The Computer Science of Human Decisions
by
by
![99910490](https://images.gr-assets.com/users/1670997349p2/99910490.jpg)
** spoiler alert **
Really awesome read bridging the gap between computer science and daily life , focusing on the complexity of life and how we make decisions based on intuition or common sense which for the most part turn out to be the optimal ones .
The book also presents some amazing mathematical solutions to problems we encounter in daily life such as :
- choosing a new secretary , buying a new condo : look then leap approach (37% rule)
- choosing a parking lot , cashing out : optimal stopping
- choosing a new restaurant ( multi armed robber / gittins index)
- creating a travel plan : Lagrangian Relaxation of constraints
- solving an intractable problem (like solving chemical composition dependencies or even placing relatives to tables at a wedding) , one that has so many variables it is impossible to compute in a reasonable time : applying a Monte Carlo simulation , sampling solutions and taking the best one
One of the most interesting parts was the one regarding Exponential Backoff?
How do you deal with a [drug/alcohol..etc] addict friend or relative that needs your help (money/shelter) but distresses you constantly?
You can't turn him down without feeling guilty but you can not constantly be there for him.
After helping him every time you will let him now he can use your help the next time in a period two times longer than the previous one.Thus you apply an exponential backoff.
Some countries have started applying this method in the prison system regarding probationary periods , jail time etc...
A really great and fun read that admiringly is accesible to anyone (not just computer science people).
The book also presents some amazing mathematical solutions to problems we encounter in daily life such as :
- choosing a new secretary , buying a new condo : look then leap approach (37% rule)
- choosing a parking lot , cashing out : optimal stopping
- choosing a new restaurant ( multi armed robber / gittins index)
- creating a travel plan : Lagrangian Relaxation of constraints
- solving an intractable problem (like solving chemical composition dependencies or even placing relatives to tables at a wedding) , one that has so many variables it is impossible to compute in a reasonable time : applying a Monte Carlo simulation , sampling solutions and taking the best one
One of the most interesting parts was the one regarding Exponential Backoff?
How do you deal with a [drug/alcohol..etc] addict friend or relative that needs your help (money/shelter) but distresses you constantly?
You can't turn him down without feeling guilty but you can not constantly be there for him.
After helping him every time you will let him now he can use your help the next time in a period two times longer than the previous one.Thus you apply an exponential backoff.
Some countries have started applying this method in the prison system regarding probationary periods , jail time etc...
A really great and fun read that admiringly is accesible to anyone (not just computer science people).
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Reading Progress
July 11, 2020
– Shelved
July 11, 2020
– Shelved as:
to-read
October 2, 2020
–
Started Reading
October 2, 2020
– Shelved as:
tech
October 2, 2020
– Shelved as:
sci
October 2, 2020
– Shelved as:
psichology
October 2, 2020
– Shelved as:
2020
October 6, 2020
–
10.0%
October 12, 2020
–
15.0%
October 16, 2020
–
30.0%
October 22, 2020
–
50.0%
October 25, 2020
–
Finished Reading