Getting Unstuck from Local Optimum
Imagine pulling up Google Maps and asking where to find the best slice of pizza. This is already a loaded question because “best” can be highly subjective based on personal tastes and preferences. But let’s imagine that we could systematically take each one of these inputs, insert into an algorithm that adds it all up, and assign a single value to hundreds of thousands of restaurants that may sell pizza.
Now Google Maps is smart in that it will start by using location to give you answers that are ranked first and foremost by how close they are towards you. Live in a beautiful town like I do (Yay for Longmont Colorado!) and search from your office (Yay for Contact Mapping!), and the first result you’ll get is Little Caesars. Despite the seemingly positive 3.7 out of 5-star rating, I would venture to guess it’s NOT the best slice of pizza in town, although it is undoubtedly the closest. Zoom the map out to observe the entire time, and you’ll find almost 20 options. In this broader territory, you’ll find over 20 options. Some of them being are awful (I’m looking at your Little Caesars). Some being amazing (e.g., Rosalee’s on Main street).
But let’s go back to the original question. Where can I find THE BEST slice of pizza? Longmont has a lot of excellent options, but I’m sure it’s not the best in the state. To find that I would have to widen the net further. AND HERE’S THE KICKER… initially, as I enlarge the search radius, I’m not going to find a better result. As I expand into smaller neighboring towns, I might find some absolute dumps along the way. But it’s only in widening this net that I will eventually find a restaurant that has the best pizza in the city. Or the best pizza in the state. Or the best pizza in the US. Or the best pizza in the entire world.
In our search for the best, we have to leave the current winner and search through a lot of losers. Rosalee’s in Longmont is probably better than anything you can find in Nebraska (I would guess). However, keep driving along and you’ll hit Chicago, which is famous for its deep-dish style pizzas. And as great as those options are in Chicago, you may have to slog through a lot of terrible options in rural Pennsylvania before you get to NYC, which could either rival or better the options in Chicago. Of course, if the true invention of pizza is in Italy, you have to continue to search much further before you would find the best slice in Sicily.
Why does this matter?
In our pursuit for truth and excellence, it’s easy to get caught in the illusion that we’ve already found it. We look around our immediate vicinity, and we might discover the best of what’s right in front of us. We then look a little further and see no better options. We then either conclude that we’ve found the best solution or simply stay put rather than expend the energy to search for something greater. Especially if the options along the way appear worse than what we already have.
In mathematics, this is called a local optimum. It’s the best solution in the most immediate area. However, unless you explore every possible inch of the answer space, there may be an unlimited number of better options. The key is to both recognize that you may be stuck in one of those areas and have the willingness to search in the dark trying to find a better solution. Put another way, you may have to eat a ton of shitty pizza across the country before you can honestly know if you’ve found the best slice.
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About Rick Manelius
Quick Stats: Chief Product Officer of DRUD Tech. Author of Winning the Lottery Within. Graduated from MIT in '03 (BS) and '09 (PhD). Life hacker and peak performance enthusiast. This blog is my experiment in creative writing, self-expression, and sharing what I've learned along my journey. For more information, read my full bio here or contact me.