Factor

A factor is one of the things you consider when judging an option: a car’s price, color, or safety; a pet’s cuteness, size, or lifespan. In ordinary decision making you score each factor, weight it, and sum the results into an overall evaluation. CF argues this is mathematically broken. Factors live in different dimensions (price, weight, cuteness), and you cannot add quantities of unlike kinds any more than you can add acres to grams. Schemes that try to do so secretly convert every factor into a made-up “goodness” dimension whose conversion weights are arbitrary, so the answer just reproduces whatever conclusion you already favored.

CF’s key reframe: a factor is really a sub-goal. You only care about a factor because doing well on it serves your overall goal. So the right question is not “how much goodness does this factor score?” but “is the option good enough at this factor to avoid failure?” That converts each factor into a binary, pass/fail sub goal tied to a breakpoint. Binary factors can be combined by multiplication: multiply the 1s and 0s, and a single failure (0) sinks the whole option, exactly like a logical AND. No amount of excess goodness elsewhere cancels a dealbreaker.

This grounds multi-factor decision making in yes-or-no thinking: a passing option has no decisive criticism across its factors. It also imports Theory of Constraints: most factors carry excess capacity and need only be adequate, not maximized. CF thus opposes the maximizer habit of optimizing every factor and the weighted-factor and pro/con-list methods that sum incommensurable factors.