Allocate Your Studies Wisely

Studies photo

There’s a danger that lurks for those of us who are curious about lots of things and love learning, and it is that our “learning efforts” (of which there is a scarce supply) end up being allocated by external factors rather than by internal priorities. These outside forces bring us somewhere – and it might seem like a good place to be – but if we had initially asked ourselves where we wanted to go, it probably would’ve been somewhere else.

That might not be very clear, so allow me to demonstrate what I mean with three real-world examples:

Whole Brain Emulation
Earlier this year, during a trip to Detroit, I read a paper by Anders Sandberg and Nick Bostrom titled: Whole Brain Emulation: A Roadmap.

Going in, I knew that my goal was only to get a good idea of what was currently possible and where things were headed with whole brain emulation (WBE). I didn’t understand most of the paper (a lot of it is very technical), but the ~10-15% that made sense to me was enough to reach my goal, so I accepted that a lot of it was over my head.

To get to a level of comprehension significantly higher than the one I had would’ve required a massive amount of efforts, and that would have been disproportionate in relation to my target (my goal was not to become a brain scientist, but rather to understand the challenges and opportunities of WBE specifically).

Not long ago, I got Judea Pearl’s Causality (a book I’ve been meaning to read for years).

As he recommends in the preface, I started by reading the epilogue, which is a speech that he gave in 1996. That was fine. Then I moved on to the first chapter and found I was having difficulty with most of the math because I wasn’t familiar enough with probability theory.

Since my goal is to truly understand the work (as part of my larger goal of better understanding the math behind probability theory and Bayesian statistics), I decided that I had to take a step back to fill in holes in my knowledge.

I put Causality on the back-burner and dived into a statistics textbook* (Les Statistiques: Une Approche Nouvelle, 2e édition. Started with chapter 5, on probability theory). I really want to understand this stuff – not just have an idea of what’s going on – so I need to take the long way. This extra effort wouldn’t have been fulfilling with the Sanberg/Bostrom paper, but it is in this case. Different goals.

As mentioned in Discipline for my Information Diet, I have a tendency to do what’s easy for me – like water flowing through the path of least resistance – and spend a lot of my learning time on news & politics. It’s so much easier to just pick up a periodical than to dive into a textbook or technical paper.

The Economist arrives every week, it’s well-written, contains lots of interesting facts, and best of all, it’s predictable. Much easier to go to that familiar, comfortable place than to face the unknown.

The problem is that most of the newsy stuff doesn’t satiate. It doesn’t give the same satisfaction as the things that are higher on my priority list. So I have to make a conscious effort to go to news-type stuff last, when I’m done with the other things.

So far I’ve been successful in removing most newsy stuff from my routine (periodicals, blogs, etc) except The Economist. It’s like mental candy. Very addictive, but lots of empty calories.

This might all seem very obvious, but I think we too often (myself included) don’t have a clear idea of what we’re trying to achieve when we’re learning something, so we give up when we should be putting extra effort (and never reach the promised land), or we waste mental cycles on details that don’t really matter to us (they don’t feed our curiosity or have practical uses).

To avoid this trap, we need to keep in sight why we’re learning something. Otherwise, we risk allocating efforts based on secondary factors like “how motivated you happen to feel that day” or “how easy it is to find sources” rather than the degree of our desire to learn something.

See also:

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*If anyone has a really good statistics textbook to recommend, especially if it covers the Bayesian approach well, please let me know.

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