Can numbers (the Look then Leap Rule OR the Gittins Index) be used to help a person decide when to stop looking for the most suitable career path and LEAP into it instead or is the career situation too complicated for that?
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Details:
Mathematical answers to the question of optimal stopping in general (When you should stop looking and leap)?
Gittins Index , Feynman's restaurant problem (not discussed in details)
Look then Leap Rule (secretary problem, fiancé problem): (√n , n/e , 37%)
How do apply this rule to career choice?
1- Potential ways of application:
A- n is Time .
Like what Michael Trick did https://goo.gl/9hSJT1 . Michael Trick A CMU Operations Research professor who applied this to his decide the best time for his marriage proposal., though he seems to think that this is a failed approach.
In our case, should we do it by age 20-70= 50 years --- 38 years old is where you stop looking for example? Or Should we multiply 37% by 80,000 hours to get a total of 29600 hours of career "looking"?
B- n is the number of available options. Like the secretary problem.
If we have 100 viable job options, we just look into the first 37? If we have 10, we just look into the first 4? If we are still in a stage of our lives where we have thousands of career paths?
2- Why the situation is more complicated in the career choice situation:
A- You can want a career and pursue it and then fail at it.
B- You can mix career paths. If you take option c, it can help you later on with option G. for example, if I went as an IRS, the irs will help me later on if I decide to become a writer so there's overlap between the options and a more dynamic relationship. Also the option you choose in selection #1 will influence the likelihood of choosing other options in Selection 2 (For example, if in 2018 I choose to work at an NGO, that will influence my options if I want to do a career transition in 2023 since that will limit my possibility of entering the corporate world in 2023).
C- You need to be making money so "looking" that does not generate money is seriously costly.
D- The choice is neither strictly sequential nor strictly simultaneous.
E- Looking and leaping alternates over a lifetime not like the example where you keep looking then leap once.
Is there a practical way to measure how the probability of switching back and forth between our career options affects the optimal exploration percentage?
F- There is something between looking and leaping, which is testing the waters. Let me explain. "Looking" here doesn't just mean "thinking" or "self-reflection" without action. It could also mean trying out a field to see if you're suited for it. So we can divide looking into "experimentation looking" and "thinking looking". And what separates looking from leaping is commitment and being settled. There's a trial period.
How does this affect our job/career options example since we can theoretically "look" at all 100 viable job positions without having to formally reject the position? Or does this rule apply to scenarios where looking entails commitment?
G- * You can return to a career that you rejected in the past. Once you leap, you can look again.
"But if you have the option to go back, say by apologizing to the first applicant and begging them to come work with you, and you have a 50% chance of your apology being accepted, then the optimal explore percentage rises all the way to 61%." https://80000hours.org/podcast/episodes/brian-christian-algorithms-to-live-by/
*3- A Real-life Example:
Here are some of my major potential career paths:
1- Behavioural Change Communications Company 2- Soft-Skills Training Company, 3- Consulting Company, 4-Blogger 5- Internet Research Specialist 6- Academic 7- Writer (Malcolm Gladwell Style; Popularization of psychology) 8- NGOs
As you can see the options here overlap to a great degree. So with these options, should I just say "ok the root of 8 is about 3" so pick 3 of those and try them for a year each and then stick with whatever comes next and is better?!!
I think its a problem about local and global minima of cost function and time deficit.
You can jump into local minimum really fast.
DNA has solved this problem optimally. See my explanation of N versus PN problem as solved by evolution.
As far as your dillema in real life just use your instinct and you will be done.
Emotion based decisions are optimal one ... its a metter of selfpreservation instinct... we are not far from animals ... are we ????
Jerzy the prof of emotional decisions
Dear Hashem,
I suggest you to see links and attached file on subject.
-The Dating Problem – Intriguing Algorithms – Medium
https://medium.com/.../thirty-seven-percent-rule-b13c41d83588
-The Secretary Problem Explained: Dating Mathematically - rs.io
rs.io/the-secretary-problem-explained-dating/
-Strategic dating: The 37% rule | plus.maths.org
https://plus.maths.org/content/mathematical-dating
-SHOUT IT! - Jacob Bender
benderj.com/other/phptests/shoutit/index.php
-Full text of "Isaac Newton : a biography" - Internet Archive
https://archive.org/stream/b29977800/b29977800_djvu.txt
-ahern cecelia love rosie where rainbow ends | Veronika Nikolaevna ...
www.academia.edu/27405975/ahern_cecelia_love_rosie_where_rainbow_ends
Best regards
An alternative phrasing is: exploration-exploitation. As for your general question, I would point at the popular book "Algoriths to live by: the computer science of human decisions" by B Christian and T. Griffiths where thet precisely discuss issues that you raise with your question.
You raise several good questions here, and not all of them have a solution from a algorithmic point of view. Let me just highlight your point C. In real live, there is always a cost associated with continuing the search, due to spending excess time. Christian and Griffith also make this point in Chapter 1 of their book ("Algorithms to live by"). They also mention that when researchers did lab experiments they found that people commit to early, at around 31%, instead of the optimal 37% rule, which is in line with this reasoning.
Further reading:
https://medium.com/galleys/optimal-stopping-45c54da6d8d0
https://criticaldensity.blogspot.com/2017/07/optimal-stopping.html
Samuel Flender "They also mention that when researchers did lab experiments they found that people commit to early, at around 31%, instead of the optimal 37% rule, which is in line with this reasoning"
I recall that they related experimental studies indicating a too long exploration phase with an average of 68% dedicated to exploring. Could it be that your 31% was the overshooting, or am I rememebering wrongly?
Hey Kenneth Carling , I got this number from page 29 in their book (Always Be Stopping, Chapter 1). They quote research results from Seale & Rapoport (1997) who found that on average their subjects leapt at 31% when given the secretary problem - they say that most people leapt too soon. They also say that there are more studies ("about a dozen") with the same result, which makes it more credible in my view.
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