However I also do frequently spend more time on close decisions. I think this can be good praxis. It is wasteful in the moment, but going into detail on close decisions is a great way to learn how to make better decisions. So in any decision where it would be great to improve your algorithm, if it is very close, you might want to overthink things for that reason.
In my experience, the more effective way to learn from close decisions is to just pick one alternative and then study the outcome and overthink the choice, rather than deliberate harder before choosing. This is related to what Cedric Chin describes in Action Produces Information: by going faster through close decisions, we both have more information about the consequences revealed to us, and we can run more experiments in parallel.
That said, I am very hardcore about coinflipping even not-so-close decisions, and made a tool for it.
In my experience, the more effective way to learn from close decisions is to just pick one alternative and then study the outcome and overthink the choice, rather than deliberate harder before choosing. This is related to what Cedric Chin describes in Action Produces Information: by going faster through close decisions, we both have more information about the consequences revealed to us, and we can run more experiments in parallel.
That said, I am very hardcore about coinflipping even not-so-close decisions, and made a tool for it.