The Analytic Hierarchy Process, developed by Thomas Saaty in the 1970s, extended paired comparison to multi-criteria decision-making. Today, pairwise methods underpin everything from product recommendation algorithms to political polling — and the training of AI models. The core insight — that relative judgement is more reliable than absolute judgement — has proven robust across a century of research.

An item that ranks first for half the group and last for the other half produces a very different signal than one that consistently ranks in the middle — even if the average score is similar. The pairwise method surfaces the structure of disagreement explicitly, making it the basis for productive conversation rather than circular debate.