Funny you mention this as, starting tomorow, I am writing a script that tries to solve this issue.
The idea is to have an sql database that contains everything you want to do. The python script picks 2 entries and ask “If you die in a year from now, wich activity minimized regret the most?”. The answer will probably be a cursor from activity1 <-> neutral <-> activity2. The scores are then calculated using the ELO scoring. This pair comparison algorithm allows to quickly rank your lifegoals by importance.
But that’s only half of it. It will also ask “which activity takes the most time to complete?/If you had one hour, which would be a better investment?” to rank activities by time to complete (also using ELO).
If you do a few comparison a day and don’t add too many entries you can then display a rank by “importance score minus by time score” to see what is the most important thing to you that also takes the less time. Or something like that. I expect some fine tuning for this.
The idea is to have an optimized and dynamic todolist that can help you to jungle between short time goals and life goals.
I’m thinking of managing my “toread” list the same way, but automatically deriving the time score from the length of the article.
What do you guys think? Any idea how to make this better? I just started and plan to learn quite a bit by trying to make this work. Any opinion is much appreciated. I will put this on github at the end. Should I publish it here also?
It’s great to see other people thinking and working on these ideas of efficiently eliciting preferences and very ‘subjective’ data, and building your own long-term decision support system! I’ve been pretty frustrated by the seeming lack of tooling for this.
Inspired partially by Gwern’s Resorter as well, I’ve started experimenting with my own version, except my goal is to end up with random variables for cardinal utilities (at least across various metrics), and I’m having the inputs for comparisons be quickly-drawn probability distributions.
Funny you mention this as, starting tomorow, I am writing a script that tries to solve this issue.
The idea is to have an sql database that contains everything you want to do. The python script picks 2 entries and ask “If you die in a year from now, wich activity minimized regret the most?”. The answer will probably be a cursor from activity1 <-> neutral <-> activity2. The scores are then calculated using the ELO scoring. This pair comparison algorithm allows to quickly rank your lifegoals by importance.
But that’s only half of it. It will also ask “which activity takes the most time to complete?/If you had one hour, which would be a better investment?” to rank activities by time to complete (also using ELO).
If you do a few comparison a day and don’t add too many entries you can then display a rank by “importance score minus by time score” to see what is the most important thing to you that also takes the less time. Or something like that. I expect some fine tuning for this.
The idea is to have an optimized and dynamic todolist that can help you to jungle between short time goals and life goals.
I’m thinking of managing my “toread” list the same way, but automatically deriving the time score from the length of the article.
What do you guys think? Any idea how to make this better? I just started and plan to learn quite a bit by trying to make this work. Any opinion is much appreciated. I will put this on github at the end. Should I publish it here also?
Btw, the idea came to me after reading through this : https://www.gwern.net/Resorter
Thanks gwern!
It’s great to see other people thinking and working on these ideas of efficiently eliciting preferences and very ‘subjective’ data, and building your own long-term decision support system! I’ve been pretty frustrated by the seeming lack of tooling for this. Inspired partially by Gwern’s Resorter as well, I’ve started experimenting with my own version, except my goal is to end up with random variables for cardinal utilities (at least across various metrics), and I’m having the inputs for comparisons be quickly-drawn probability distributions.
Very interesting!
Could you explain the workflow?
Also, do you intend to make the code accessible?