Try the Opposite

We avoid local maxima by branching out.

February 28, 2018 • 2 min read

If you’ve been doing the same thing for a while, it’s time to try the opposite.

As you age, you tend to double down on what you like – or at least, what you think you like. However, sticking with what you know is the short-term play. The more conscious you are of that, the richer your world gets.

For example, I like deep games. Building a team, a story, or a whole civilization is satisfying. After completing a particularly egregiously long story-driven RPG, I knew it was time for the opposite: Fortnite Battle Royale, a chaotic 100-player free-for-all. It’s not what I usually like, but it was time to shake things up. Turns out, abject chaos is a crapton of fun. Not only did I get a change of pace, I discovered a whole new genre to enjoy.

Our habits tend to fall into local maxima. We choose well compared to similar alternatives, but ignore options that are totally different yet possibly better. Machine learning algorithms avoid local maxima by occasionally testing random permutations, with techniques like simulated annealing. People can do the same thing by periodically trying something way outside their norm.

With this in mind, I propose a rule of thumb: for every 10 times you do what you know, what you like, or what you’re good at, give the opposite a chance.

Mostly stick to rock music? Listen through a Taylor Swift album. “Know” you prefer writing UI code? Pick up a low-level networking task. Always order the burger? This time, take a chance on the Zanzibari Mackerel with House Special Sauce.

Typically write long articles? Try a short one. 🤷‍♀️

It might not work – but maybe it will. Either way, you learn.

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© Allen Pike. 👋🏼 You can contact me, or check out Steamclock.