The Partial Control Fallacy

by Darmani 3y14th Dec 20162 comments

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This is a cross-post from http://pathsensitive.blogspot.com/2016/06/the-partial-control-fallacy.html

 

Around the time I started grad school, I applied for a few prestigious fellowships. Winning is determined by several factors. Some are just an application, while some have a follow-up interview, but the applications all get scored on a rubric that looks roughly like this:
  • 50%: Past research
  • 30%: Letters of recommendation
  • 10%: Transcript
  • 10%: Personal Essays
Naturally, I proceeded to pour massive amounts of time into the essays, letting it consume much of my free time for the month of October.

Getting that Fellowship will really help me have a successful graduate career. Writing better essays will help me get the Fellowship. Therefore, to the extent that I care about having a successful graduate career, I should be willing to work hard on those essays.

But if the real goal is a successful graduate career, then at some point shouldn’t I put those essays down and do something else, like reading a few papers or practicing public speaking?

This, I dub the Partial Control Fallacy. It’s where, if there’s some outcome you want, and you only control a couple factors that affect that outcome, you decide how much to try to improve those factors as if you were actually improving the entire outcome. It’s closely connected to the 80/20 principle: it’s when you only have control over that last 20%, but you pretend it’s the whole thing and work on it accordingly. It’s when the 80/20 principle would suggest doing nothing at all.

Here are some more examples:
  • Trying to get any competitive award that’s judged mostly by your past. The best college application is stellar grades and some good awards, the best resume is a great network and lots of success stories, and the best pitch to VCs is a rock-solid business.
  • Thinking really hard about what to say to that cute guy or girl across the room. Most of what happens is determined before you open your mouth by what they’re looking for and whether they’re attracted to you.
  • Worrying about small optimizations when writing code, like avoiding copying small objects. Most of good performance comes from the high-level design of the system.
I think I’ve been guilty of all three of these at one point or another. I don’t want to think about how much time I spent on my Thiel Fellowship application and preparing for my YCombinator interview. Meanwhile, most people who get into either don’t spend much time at all.

In parallel computing, there’s a concept called Amdahl’s law. If your program takes tsteps to run, and you can make s steps faster by a factor of f (say, by splitting them across multiple processors), then the new speed is t-s+s/f, for a speedup of t/(t-s+s/f). Therefore, if you optimize those s steps extremely hard and split them across infinite cores, the best speedup you’ll get is t/(t-s).

Applying that to the above, and you can see that, if I worked infinitely hard on my essays, I could only make my apps 11% better versus not doing anything at all. (At least to the extent that it really does follow that rubric, even if I submit a blank essay.)

Sometimes, that outcome is all you care about it, in which case you’re perfectly justified in trying to eke out every advantage you can get. If you’re in a massively competitive field, like sports or finance, where there’s a really big difference between being #1 and being #2 at some narrow thing, then, by all means, go get that last 1%. Wake up early, get that 7th computer monitor, rinse your cottage cheese. But if you’re putting this kind of effort into something because it’s your terminal goal — well, you’re not doing this for anything else, are you?

I think the solution to this fallacy is always to think past the immediate goal. Instead of asking “How can I get this Fellowship,” ask “How can I improve my research career.” When you see the road ahead of you as just a path to your larger mission, something that once seemed like your only hope now becomes one option among many.

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