M2M Day 355: Overcoming Parkinson’s Law
This post is part of Month to Master, a 12-month accelerated learning project. For October, my goal is to defeat world champion Magnus Carlsen at a game of chess.
Parkinson’s Law states that “work expands so as to fill the time available for its completion”, and I’m definitely experiencing this phenomenon during this final M2M challenge.
In particular, at the beginning of the month, I decided to extend this challenge into early November, rather than keeping it contained within a single month. I did this for a good reason, which I’ll explain soon, but this extra time isn’t exactly helping me.
Instead, I’ve simply adjusted my pace as to fit the work to the extended timeline.
As a result, in the past week, especially since I’m currently visiting family, I’ve found it challenging to make focused progress for more than a few minutes each day.
So, in order to combat this slowing pace and start building momentum, I’ve decided to set an interim deadline: By Sunday, October 29, I must finish creating my chess algorithm and shift my focus fully to learning and practicing the algorithm.
This gives me one week to 1. Build the machine learning model, 2. Finish creating the full dataset, 3. Testing the trained model, 4. Making further optimizations, 5. Testing the strength of the chess algorithm, and, in general, 6. Validating or invalidating the approach.
Hopefully, with this interim deadline in place, I feel a greater sense of urgency and can overcome the friction of Parkinson’s Law.
Read the next post. Read the previous post.
