Where do you go for most of your answers these days? Google. And it’s no surprise that Google’s a company full of engineers. Engineers solve problems. That’s what they do.
And computer software engineers have developed methods — algorithms — to solve some of the most insanely complex problems out there. So what if we turned that cold, clinical science toward the warmest and most human of problems?
Turns out you can get some amazing solutions. No, you don’t need to understand calculus and you don’t need a mind that can bend spoons. We’re going to make it simple to apply advanced computer science to the big decisions in life and the everyday struggles that plague us all.
Okay, time to update the software in your brain. Let’s get to it…
Computer scientists often use a framework called “explore/exploit.” Exploring is when you gather information and exploiting is when you put it to use.
In life, exploration minimizes regret. You get to try lots of options. But exploitation maximizes happiness. You do what you know will work, and get results you know you’ll like.
Exploring is fun. We all like novelty. But if you never do anything with what you learn, you don’t get very far.
And exploiting what you’ve learned can provide big returns. But too much of that and you never learn anything new, and can’t solve problems you’ve never seen before. So you need a bit of both. Which creates a problem: How do you strike the right balance?
No need to do heavy math. But the key thing you want to think about here is time. How much time do you have to exploit the results of your exploration?
When balancing favorite experiences and new ones, nothing matters as much as the interval over which we plan to enjoy them.
So if you’ve just moved to a new city, try a different restaurant every night for a while. If you’re about to move out of a city, stick to your favorites. And you can apply this principle to many different areas of life from jobs to meeting new people.
“Explore/Exploit” also helps explain some of the seemingly crazy behavior of human beings because, to a degree, it’s programmed into us.
Alison Gopnik, a leading researcher on children, explains this is why kids have such short attention spans and do so many crazy things — they need to explore this new world of ours.
And it also explains why older people can be so set in their ways. They’ve had a long time to explore. They know what makes them happy. So they stick to it — and more often than not, it works.
…exploration necessarily leads to being let down on most occasions. Shifting the bulk of one’s attention to one’s favorite things should increase quality of life. And it seems like it does: Carstensen has found that older people are generally more satisfied with their social networks, and often report levels of emotional well-being that are higher than those of younger adults.
(To learn more tips on living an awesome life, check out my book here.)
Alright, so the science of high tech can help you be happy. But can it help you get your act together?
Computer scientists would refer to this as a “sorting” problem. That’s what Google does — sorts information.
Trigger warning for neat freaks: you’re not going to like this. (And sloppy people — rejoice!)
Turns out that in many areas of life, the time you spend searching beats constant attempts to sort. Keeping your books in “that perfect order” takes more time than having to do a little digging on the rare occasion when you need a specific one.
So when it comes to organization, computer science says “err on the side of messiness.”
The basic principle is this: the effort expended on sorting materials is just a preemptive strike against the effort it’ll take to search through them later. What the precise balance should be depends on the exact parameters of the situation, but thinking about sorting as valuable only to support future search tells us something surprising: Err on the side of messiness. Sorting something that you will never search is a complete waste; searching something you never sorted is merely inefficient.
Okay, but with the things you use frequently, you need to be able to find them. No argument here. What’s the best way to organize that stuff?
Well, believe it or not, computer science and Martha Stewart agree. (Add that to the list of “sentences you never thought you’d hear.”)
One of the guiding principles Martha recommends is to think about, “When was the last time I wore it or used it?” If it’s not often, get rid of it, or stuff it in the garage. Things you use frequently deserve priority.
And computer systems almost all use “caching” — giving frequently used data a special area of memory that makes it more accessible.
So stuff that gets used a lot needs to be nearby and easy to locate. What’s a pretty good system to implement this principle? It’s one you already use but probably beat yourself up about: piles.
Don’t feel guilty when you pile stuff up on your desk that you use frequently. Computer science says that’s a very efficient system.
…the big pile of papers on your desk, far from being a guilt-inducing fester of chaos, is actually one of the most well-designed and efficient structures available. What might appear to others to be an unorganized mess is, in fact, a self-organizing mess. Tossing things back on the top of the pile is the very best you can do, shy of knowing the future.
(To learn how to stop being lazy and get more done, click here.)
Alright, we’ve engineered happiness and organization. But what does computer science have to say about powering down your brain when it’s wasting too many cycles on worrying?
You’re worrying about something. You need to make a decision. But you want to consider more possibilities. You feel with enough time you can crack this.
In computer modeling, they refer to the problem as “overfitting.” In trying to create the perfect model, they consider too many factors and end up making something that provides predictions that are worse, not better.
So one of the deepest truths of machine learning is that, in fact, it’s not always better to use a more complex model, one that takes a greater number of factors into account. And the issue is not just that the extra factors might offer diminishing returns— performing better than a simpler model, but not enough to justify the added complexity. Rather, they might make our predictions dramatically worse.
More time thinking doesn’t necessarily mean better results. Sometimes you get too far out in the weeds and just confuse yourself further. So what’s the solution?
Since underthinking and overthinking can both produce lousy results, boundaries are essential. When good information is scarce and you have a high degree of uncertainty, use “early stopping.”
Set a time limit on how much you’re going to think about a problem and when that expires, pull the trigger. Just make the best decision you can.
If you have high uncertainty and limited data, then do stop early by all means. If you don’t have a clear read on how your work will be evaluated, and by whom, then it’s not worth the extra time to make it perfect with respect to your own (or anyone else’s) idiosyncratic guess at what perfection might be. The greater the uncertainty, the bigger the gap between what you can measure and what matters, the more you should watch out for overfitting— that is, the more you should prefer simplicity, and the earlier you should stop. When you’re truly in the dark, the best-laid plans will be the simplest.
(To learn the five secrets to how mindfulness can make you happy, click here.)
So you have the engineering solution to overthinking. But life isn’t all in your head. How can thinking like a programmer lead to you finding an awesome place to live?
You want the best. But you can’t search forever. This problem appears in many, many areas of life. So how many options should you consider before choosing one?
The problem is that the “best” doesn’t usually have a label on it that you can trust. But computer scientists have thought abour this one too: it’s called an “optimal stopping problem.”
So if you’re looking for that perfect apartment, ask yourself how long you’re willing to search. Now take 37% of that time to look at options (roughly a third — I said, I’d make the math easy.) And forget every place you visited — except the best one.
Then keep looking. First apartment that beats that “best” one you found in your initial scouting, take it. Science says this will deliver the best results, given the length of your search.
If you want the best odds of getting the best apartment, spend 37% of your apartment hunt (eleven days, if you’ve given yourself a month for the search) noncommittally exploring options. Leave the checkbook at home; you’re just calibrating. But after that point, be prepared to immediately commit— deposit and all— to the very first place you see that beats whatever you’ve already seen. This is not merely an intuitively satisfying compromise between looking and leaping. It is the provably optimal solution.
(To learn the four rituals neuroscience says will make you happy, click here.)
Okay, let’s round this up — and learn how what computer science says is the optimal way to find your soulmate…
Here’s how computer science can solve the most human of problems:
So how do you find your soulmate? Once again, that’s an optimal stopping problem…
How many people (roughly) are you willing to date? What’s 37% of that number? Go out on that many dates, and politely tell those people, “No, thanks.” But remember the best of the bunch. Then keep dating until you meet someone better than that “best” one. And that’s the person you want to focus on. But…
Sounds kinda cold, callous and terribly unromantic, doesn’t it? You’re probably right. Computer science can’t solve all of our human problems — and nor should we expect it to. I certainly don’t.
My college girlfriend didn’t know it, but she was probably using the “optimal stopping” algorithm. I was one of the first guys she met on campus. And let me tell you, it’s no fun being in the initial 37% that gets the “No, thanks.”
But I’m guessing I was the “best” of her 37%. And it’s safe to say subsequent dating didn’t reveal a better candidate…
So she circled back. And that was the best thing for both of us. The truly “optimal” algorithm.
Computer science has some pretty good solutions we can learn from. But sometimes the math doesn’t work. Sometimes you need to go with your gut. Or with your heart.
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