Options

Options are the alternatives a decision picks among: the cars, pets, plans, or chess moves laid out as the rows of a decision chart, with factors as the columns. The standard question “which option is best overall?” assumes options can be ranked on a single combined score. CF argues this assumption fails: scoring each option requires combining factors from different dimensions into one number, but you cannot add unlike dimensions (price plus cuteness is meaningless), and multiplying them yields useless multi-dimensional units. Weighted scoring smuggles in arbitrary conversions to a made-up “goodness” dimension, so the ranking just reflects pre-existing intuitions.

CF’s alternative reconceives how options relate to the goal. Rather than asking how much goodness each option scores, you ask whether each option is good enough on every factor, treating each factor as a binary sub goal (pass or fail). Multiplying the binaries — equivalent to logical AND — gives an overall pass only if no factor fails. A single failure (decisive criticism) sinks the option no matter how strong it is elsewhere; no number of passes cancels one fail.

So CF does not seek the highest-scoring option but a non-refuted one: an option with no known errors, expected to succeed at the goal. Usually several options pass, and the difference between them is below the margin of error. The rare case where one option beats another on every factor is strict superiority; otherwise options embody tradeoffs across dimensions that no ranking can honestly collapse. This makes choices resilient: small data changes rarely flip a pass to a fail.