The Art of Good Enough
People chase perfection all the time: the perfect plan, the perfect choice, the perfect result. So when I think about something “being enough”, my gut response is to recoil just a bit. But good enough is not about giving up or accepting second-rate work. It’s about refusing to waste effort on details that don’t change the outcome or on options that simply aren’t possible with the time and information we have. That choice fits with aiming higher. You move forward instead of staying stuck. “Perfect is the enemy of the good” (Voltaire) and most of us have felt that truth when a project drags on, a decision never gets made, or exhaustion sets in.
Some problems make this especially clear. Scheduling a large group with conflicting needs, finding the shortest route through dozens of stops, or assigning limited resources across competing projects — these are the kinds of hard, combinatorial problems where exact solutions take too much time or computation to be practical. For those, loosening rules or adding controlled chance isn’t a shortcut; it’s often the only reasonable way to get a useful answer.(e.g. Travelling Salesman Problem).
Here’s the encouraging part: we already use these ideas every day without naming them. A parent skips the full bedtime routine one night for a movie with the kids. A manager hires a solid candidate instead of waiting for the mythical perfect one. You pick a dinner spot because it feels right rather than reading every review. We plan to break the rule because we can live with the worst case–it’s a penalty, but it’s not the end of the world. Have you ever broken the speed limit “with cause”? We do these things intuitively every day. Computer science has studied them, measured the results, and shown they often beat rigid approaches — especially on the tough problems. That gives comfort: your gut choice, if you pay attention to what happens next and adjust, is a strong place to start. And on the really intractable problems, though, these methods are required, not just convenient.
You may recall we were doing a flooring renovation recently. With the thousands of flooring options, I knew we wouldn't find the best flooring that was available, but we could find something that was good enough. I'll share my "good enough" experience with you as we move along.
Think of decision-making like painting. An artist doesn’t try for a perfect image on the first pass. As I look at it, I see three invaluable tools in their artists kit. The same tools help with everyday choices. There are risks if you push good enough too far, but paying attention to results keeps it honest.
The Pencil – Simplifying to Start
Most paintings begin with quick pencil lines that set the basic shape. The lines are light and easy to change. The artist isn’t locked in yet In decisions, this means loosening rules that stop you from starting (similar to constraint relaxation, continuous relaxation where exact choices turn fluid for a moment, or Lagrangian relaxation that treats rules as costs instead of absolute limits). Essentially, if you can’t solve the problem in front of you, solve one like it to be or one you can solve and use those results as a “this is my worst case scenario”. If a new exercise habit feels impossible with a fixed daily schedule, sketch it lighter: aim for movement most days in whatever form fits. You’ve likely done something close already, like bending a budget for a purchase. You’d like to buy a pick-up truck, but they’re pricey. You don’t have the cash, but you’re open to the idea of paying an interest “penalty” for a loan. How about not owning it, but renting one when you need it? Or borrowing one, for the cost of cashing in a favor from a friend? Worst case, you pay the penalty; best case, you find the cash, the patience, an alternative solution, or a better deal. Change the rules, consider the cost of breaking them to establish an initial benchmark. Make it easier to find a starting point.
In practice, our flooring selection: We solved for the problem we wanted. Finding the right style, color, type, quality, durability, and price was challenging. So we relaxed on the price to help quickly narrow things down. When we were willing to look at flooring that was above and below our targets, we narrowed down on style, color, and quality quite quickly. From there, we re-introduced our price range and we had very specific things to ask of our vendors and installer.
You gain speed and flexibility. Energy stays available for things like family or work that counts more. The danger is drifting without direction. When it comes time to execute, safeguard the drift by keeping one or two firm points clear from the beginning, where certain rules can be “bent” in finding the worst-case, but can’t be bent in practice.
Embracing Happy Little Accidents - Just Do Something!
Once the sketch exists, the artist starts adding paint, mixing colors on the canvas, scraping parts away, and revisiting other parts. But sometimes we’re past the point of just scraping and starting over – the work has started and we’re past the point of no return. Bob Ross, the famous PBS painter, puts it like this – “We don’t make mistakes, we have happy accidents…when you get over that fear of making a mistake, then it becomes fun.” (https://www.youtube.com/shorts/KKN-qE2bRuU)
When it comes to making decisions, though, we can add a bit of controlled chance to get past familiar, expected patterns. How effective is that drug? Who will win the election? One technique is drawing from “Monte Carlo methods” that sample randomly to estimate answers. There are also randomized algorithms that use probability for quick results, or ways to escape local optima. Can’t decide on the next book? Close your eyes and grab one off the shelf. Need fresh ideas for a project? List ten options fast without judging, then pick one at random to test first. We already do versions of this, like flipping a coin to settle a small choice or taking a different route home. On a side note, I’ve discovered the coin-toss or blind-selection definitely reveals my bias/preference – “No, I don’t want to do that first!”. Interesting. Research shows it frequently leads to better outcomes than pure logic alone. Or, if you’re willing to risk a bit of error, you can trade that small risk for huge gains. If you have children, consider how you can tell if the chore was done well by the time spent on it. You can check on it to be sure, or you can point out that it took 5 minutes to do a 20 minute chore. You risk a false negative. Or, on a website with millions of users, when you go to create a username for your account, it could do a search for the string you enter and find a match – there are decent algorithms for that. Or, they could convert your full string to a numeric representation and do a much faster search and same space in the process. The risk is false positive – you might be told that your username is already taken when it really isn’t, but that might be okay. These are examples of what are called “Bloom Filters”, where you trade tiny errors for huge gains.
In practice, our flooring selection: We introduced randomness. We had ideas about what we liked and didn't like, wanted and didn't want. But that could still have left us making additional trips back and forth between stores getting samples. So we introduced some randomness and brought home some samples that were strategically random -- they could be anything that didn't look like what we'd already tried or held in our hands. With the additional "data" at home, we were able to narrow down what we thought looked good and what didn't.
With randomness you get new ideas, less bias, and often unexpected wins. The downside is wasted time if chance runs wild. Keep experiments small and review what you learn afterward.
Stepping Back – Gaining Perspective to Know When It’s Enough
Experienced artists walk away from the canvas for a while, then look again from across the room or the next day. They see the overall effect and decide whether more work helps or hurts.
We might not have the 100% optimal soolution, but is our approach sufficient to guarantee it will be good enough? If we had 10 stops to make on an errand run, we might not have the absolute optimal route, but if you plot a course, take a look at it, is it good enough? Is it worth the time and effort to try another sequence? Or, you’re preparing for a presentation – does it already cover the bases, presented in a meaningful way? Oftentimes too much reworking only muddies the end product. How many times have I got a better response from a presentation or report I squeezed out at the last minute with fewer opportunities to rework it? My changes are usually for my benefit, not the benefit of the audience. In fact, Christian and Griffiths are pretty clear that measurements confirm that extra polishing rarely adds much real value.
We should save effort for bigger goals and build trust in your own judgment. While there is a risk of quitting too soon and missing an easy improvement, a quick check with someone else or a short break usually catches that.
In practice, our flooring selection: We stepped back numerous times. With a wide array of samples in-hand, we eliminated some options very quickly and could focus on a few in a slightly more extended way. With 3 "finalists" in hand, we place samples though the areas we'd be reflooring and let them sit there for a couple days. Voila. We knew the one we liked best.
It’s Your Process, It’s Your Masterpiece
You have your process and I’m not likely going to suggest you find a new way, but these are steps that you’re likely using intuitively anyway. They connect naturally and can be repeated as needed: sketch lightly, try something, step back and adjust while learning from each round. Why not plan to use them strategically and confidently; don’t consider them as a failing, use them as tools to get to “good enough” from either direction – too perfect or to liaise faire. So if you’re stuck on an intractable problem or are planning a new side project try to outline the basics loosely, test a few approaches, review progress, tweak. Your first instinct gives the starting mark; results tell you what to change next.
- Were there literally thousands of options to choose from? Yes.
- Was it an exhaustive search? No.
- Did we make use of intuition and science-backed research to narrow the field of options down quickly? Yes.
- Is our flooring choice the very best flooring that could be purchased and installed? Not guaranteed.
- Did we weigh options we would not have thought to consider, otherwise? Yes.
- Did we land on solid decision that we're very pleased with and is good enough? Definitely!
Good enough frees you. Your everyday instincts are often right, and now you know that science has looked into it and can explain why. Drop the chase for perfection and use this straightforward process, pay attention to outcomes, and you’ll have more time and energy for what actually matters. Try it on one choice today and see what happens.
Thanks for reading and keep Aiming Up!
For Further Study/Information
If you want to dig into the computer science side from Algorithms to Live By (Chapters 8-9):
- Constraint relaxation: Temporarily ignore or soften rules to find workable answers.
- Continuous relaxation: Allow fractional choices briefly, then round to whole ones.
- Lagrangian relaxation: Turn strict rules into penalties for balanced trade-offs.
- Monte Carlo methods: Use random samples to approximate hard problems.
- Randomized algorithms: Add chance for faster, reliable results.
- Escaping local optima: Random jumps to avoid getting stuck on merely okay solutions.
- Bloom filters: Fast lookups that accept small error chances for major speed gains.