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Career Calculus

Created by dave. Last edited by dave, 14 years and 48 days ago. Viewed 2,011 times. #2
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Thoughts About Career Calculus

Although I wouldn't know him from a hole in the ground, I respect a lot of Eric Sink's writing. I've referred to his writing before, for example the (thus far) excellent and (thus far) incomplete discussion on source code control, a topic dear to my former-CM heart.

Yesterday I found a link to an older article of his, a discussion on what he refers to as >>Career Calculus.

Go read it, it is important to the following discussion. I'll wait.

While he speaks of a software developer, it is obvious to anyone that this can be described in slightly more general terms to reflect any kind of career. In practically any career, the maxim learn or stagnate applies.

Mr. Sink describes the general theory of cluefulness:

C=G+LT

where

  • C is your effective cluefulness
  • G is your natural level of giftedness in the field
  • L is learning you do
  • T is time.
The thesis of this argument is that the only factor you can control is the amount of learning you do. You are stuck with your natural gifts (or lack thereof); and time passes the same for everyone. The only way to get ahead is to keep learning.

While being both profound and stunningly obvious (which in my view reinforces the profoundness of the observation), it is also too simple because it doesn't reflect the inevitable decay in your skills over time either due to lack of use and/or obsolecsence.

In the first case, if you don't use a skill for a while, you forget pieces of it, and your clue-level drops accordingly. In my case, I have a terrible memory and if I don't exercise an infrequently-used skill, I lose it and have to re-learn it. My comment about my post-secondary education is not that I learned anything worthwhile (which I admit to be a gross oversimplification); but more that I learned how to recognize a good book and how to use it to learn the information it contains.

Therefore, there needs to be a factor in our equation which relates to our memory loss. This won't be linear, and it has to have a local factor which reflects the fact that most people remember things better than I do.

The re-learning exercise is interesting because it draws a question about the original formula. Does re-learning count as "learning" if you have forgotten? I re-learn things all the time. I don't know the answer to this question.

The second case I mentioned is skills obsolecense. I mean, it could be great that someone knows Pascal inside and out. But there isn't much call for that today, and chances are that there isn't much specific in the knowledge of Pascal that helps you with other situations. Chances are that the specific lessons you learned while learning Pascal itself, about program structure, scoping rules, program flow, whatever, those are the skils that are applicable to your current assignments.

Therefore we also needs to be a factor in our equation which reflects the relentless forward progress society is making with technologies in our field. Again, this won't be linear.

One thing which occured to me while writing this is that while we can affect the Cluefulness through learning, our Effective Cluefulness (ie how clueful we actually end up being) is very dependant on the situation. For example, my employer considers me a fairly clueful dude (current license-audit-monky-work not withstanding); yet if someone asks me a question about a Microsoft product, chances are I won't know how to deal with it.

So I make our new formula as looking like:

C=S*( (G + LT) - (F{T} +O{T}) )

where

  • C is our cluefulness
  • S is the situational modifier
  • G is our giftedness in the field
  • L is the amout of learning we do
  • T is time
  • F{T} is a function which reflects us forgetting more over time
  • O{T} is a function which reflects our skills getting obsolete
I don't think any of this contradicts Mr. Sink's thesis that learning is the only way you have to improve your level of cluefulness. If anying, it tells you that you have to keep learning just to stay on level ground.
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