Optimization Is a Terrible Life Strategy
What engineers know about trade-offs that self-help doesn’t
I spent three hours trying to find the perfect dinner spot last weekend.
Read reviews. Compared menus. Checked distances. Calculated wait times. Built a mental spreadsheet of variables and weights and trade-offs.
And by the time I’d narrowed it down to the optimal choice, I wasn’t hungry anymore. I’d optimized myself out of breakfast entirely.
This is what happens when you treat life like an engineering problem. When you try to maximize everything. When good enough stops being an option because you’re convinced there’s a perfect out there if you just search hard enough.
In optimization theory, there’s this concept called local versus global maxima. A local maximum is the best option in a given area. A global maximum is the best option everywhere.
And here’s the problem: when you’re searching for the global maximum, you often have to leave the local maximum. You have to abandon something good to see if there’s something better out there.
Which means you spend a lot of time in worse places, searching for perfect, while good sits behind you getting cold.
This is fine if you’re optimizing a machine. If you’re trying to design the most efficient engine or the fastest algorithm. You can test everything. Map the entire space. Find the actual global maximum.
But you can’t do that with life. The search space is too big. The variables change too fast. By the time you’ve found the “optimal” choice, the conditions have shifted and it’s not optimal anymore.
You’re always chasing. Always searching. Never arriving. Because the global maximum is a moving target and you’re running an algorithm that assumes it stays still.
Engineers know this. They know that optimization has a cost. That searching for the best takes time and resources. That sometimes good enough is actually better than perfect because you can have it now instead of eventually.
They have a term for this: satisficing. It means picking the first option that meets your criteria instead of searching for the optimal one. It means accepting good enough and moving on.
It sounds like settling. Like giving up. Like you’re not trying hard enough.
But it’s not. It’s strategy. It’s understanding that the cost of optimization can exceed the benefit. That the search itself has a price.
And sometimes that price is your life. Your time. Your ability to enjoy the breakfast you could be eating instead of researching.
Self-help tells you to maximize happiness. To optimize every area of your life. To never settle for less than you deserve, less than the best, less than perfect.
But happiness isn’t a function you can maximize. It’s not a single variable you can optimize. It’s a complex, multi-dimensional, constantly shifting thing that depends on context and trade-offs and a thousand factors you can’t control.
When you try to maximize it, you end up like me with breakfast. Paralyzed by options. Analyzing instead of experiencing. So focused on finding the best that you miss the good that’s right in front of you.
Because here’s what optimization doesn’t tell you: every choice has trade-offs. Every gain comes with a loss. You can’t maximize everything simultaneously.
In engineering, they call this the cost function. It’s the thing you’re trying to minimize or maximize. And the trick is, you can only optimize for one cost function at a time.
You can optimize for speed or efficiency or cost or durability. But not all of them. Because they conflict. Because improving one makes another worse.
And life is the same. You can optimize for career success or free time or deep relationships or creative fulfillment or financial security. But not all of them. Not perfectly. Not simultaneously.
Every choice you make is trading off one thing for another. And when you try to optimize everything, you end up optimizing nothing. You end up stuck. Searching. Never committing. Never satisfied.
Because there’s always a better option somewhere. Always a way you could be doing more, being more, achieving more.
The global maximum is always out there. Just out of reach. Taunting you.
But the local maximum. The good enough. The thing that works right now, in this context, with these constraints. That’s available. That’s real. That’s something you can actually have.
And I’m learning that having something good is better than endlessly searching for something perfect.
That satisfaction isn’t about finding the optimal choice. It’s about making a choice and being satisfied with it. About deciding that this is enough. That this works. That this is good.
Not perfect. Not optimal. Not the global maximum.
Just enough.
Enough is a concept that optimization can’t handle. Because enough isn’t maximum. Enough is a threshold. A line you draw that says: this meets my needs. This is sufficient. I can stop searching now.
And self-help hates this. Hates the idea of enough. Tells you that enough is settling. That you should always want more, reach higher, optimize further.
But wanting more is exhausting. Reaching higher is endless. Optimizing further is a trap.
Because there’s no end to optimization. There’s always another local maximum to abandon. Another search to conduct. Another variable to improve.
You never arrive. You just search. Forever. Convinced that the global maximum exists and you just haven’t found it yet.
Meanwhile, the local maximum. The good enough. The breakfast spot that’s not perfect but is perfectly fine. That’s sitting there. Available. Waiting for you to just choose it and move on with your life.
Engineers know when to stop optimizing. They know when the improvement is marginal and the cost is high. They know when good enough is actually optimal once you factor in the cost of the search.
They know that optimization is a tool. Not a life strategy.
And I think we need to learn this. Need to accept that good enough is sometimes better than best. That satisficing is sometimes wiser than maximizing. That the local maximum is good, actually, and you don’t need to abandon it to search for something you’re not even sure exists.
This doesn’t mean stop trying. Doesn’t mean accept mediocrity. Doesn’t mean never improve anything.
It means know your cost function. Know what you’re actually optimizing for. Know when the search is costing more than the improvement is worth.
It means draw a line called enough. And when you reach it, stop. Rest. Enjoy what you have instead of endlessly seeking what you don’t.
It means understand that life isn’t an optimization problem with a global maximum solution. It’s a series of local choices, each with trade-offs, each good enough in context.
And good enough is enough. Really. Actually. Truly enough.
Not because you can’t do better. But because the cost of doing better, the endless search, the perpetual dissatisfaction, that’s not worth it. That’s not optimal when you factor in the price.
The optimal strategy is knowing when to stop optimizing. And choosing satisfaction over perfection. And eating breakfast instead of researching it.
That’s what engineers know that self-help doesn’t. That optimization is a tool. That enough is a strategy. That sometimes the best choice is to stop choosing and just live with what’s good.
I had cereal in the end. At home. It was fine. Not optimal. Just enough.
And I was satisfied. Not because it was perfect. But because I stopped searching. Drew my line. Decided this was good enough.
And it was. It really was.
Enough.
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