Good Enough

“Good enough” names the judgment at the heart of CF decision-making: a factor is good enough when its value is sufficient for the goal, with room to spare, so that improving it further changes nothing that matters. CF asks not “how much goodness can I get?” but “how much is enough, and where is the breakpoint?”

This reframes evaluation. Most factors in any working system have excess capacity — they already exceed the minimum needed, like a wall far stronger than any load it bears. Pushing such a factor higher wastes effort on a local optimum without raising overall throughput. Conversely, a factor far short of the threshold has excess failure: small gains there change nothing either, because it still fails.

Because most factors sit safely past their threshold with a margin of error, CF maps a continuous quantity to a binary pass/fail: is this good enough to avoid causing failure, or not? Multiply the passes together for a combined verdict. This makes conclusions resilient — many input values map to the same answer, so small data changes don’t flip it.

CF opposes this to weighted-factor analysis and Bayesian credence-updating, which reward any improvement to any factor and so push toward perfectionist over-optimization of irrelevant details. CF aligns instead with the satisficer stance: set a criterion for what suffices, then accept any option meeting it. The point is qualitative — clearing a bar, not maximizing a number.