Maximizers and satisficers use approximately the same approach with a different threshold for the amount of the possibility space they are willing to search thr

Maximizers and satisficers use approximately the same approach with a different threshold for the amount of the possibility space they are willing to search through (let's call it p). Let's eliminate the extremes: p = 0 is a blob who just doesn't do anything. p = 1.0 only ever keeps looking at new possible options. Since the space is almost always infinite, this approach never terminates. p = 0.01 just does the first thing that comes to mind p = 0.99 spends an very, very long amount of time evaluating options People tend towards different thresholds. But the right p value depends on the context. Sometimes the first thing that comes to mind--the naive approach, is in fact the right thing to do. If you're building something lower level that will be built upon later and difficult to change, more caution is often warranted. The approach through the possibility space also matters--identifying the places to explore that might reveal the most risky aspects of the problem quickly is a skill developers build over time (often via past mistakes they later regretted).