Satisfice to Save Time and Effort
It often takes 90% of the time to get the last 10% of achieving your very best. But you can save time and effort by satisficing. Find out more here.
When you’re making a decision, it can be tempting to go for the Perfect Possible Decision. After all, we’re smart people with amazing skills, and we want to do the best we possibly can. Unfortunately, it often takes 90% of the time to get the last 10% of achieving our Very Very Best.
This sad fact of life means that you can be making fine progress on your projects and dreams right up until the point where you’re almost done. At that moment, somehow that remaining 10% balloons into the 90% and deadlines slip, drastically. But all it takes to fix that is a small tweak to your decision-making process.
When you are striving for perfection, you declare the project done when you ask yourself: “Is it done?” and answer yes. But being a perfectionist, you’ll only answer “yes” if “done” means “perfect.” People who study decision-making call this “maximizing.” We’re trying to get the best possible outcome.
The alternative, which will save you tons of work, is satisficing. When you satisfice, instead of asking “Is it done?” you ask “Is it good enough? Will it work at its current point of completion?” If the answer is yes, then you declare everything finished and move on to the next task.
Choose something in your life that’s incomplete and ask yourself whether you’re maximizing or satisfying. Then explicitly satisfice. Ask yourself, “If I were to stop working now, would it be good enough?” If so, try stopping now and see what happens. You just may discover that you have a lot more free time and are still accomplishing everything you want.
To read my entire episode about maximizing and satisficing, click here.
Blue pie chart from Shutterstock
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About the Author
Stever Robbins was the host of the podcast Get-it-Done Guy from 2007 to 2019. He is a graduate of W. Edward Deming’s Total Quality Management training program and a Certified Master Trainer Elite of NLP. He holds an MBA from the Harvard Business School and a BS in Computer Sciences from MIT.
