Developed
Mechanism Established
Human Behaviour
Most people do not know exactly what they want until they see it. Asking them to describe it first produces a best guess, not a real preference. Systems built around that guess stall the decision rather than drive it.
Mechanism
Instead of leading with a question, the system leads with a proposal . It shows a set of options, consisting of confident recommendations, popular alternatives, and deliberate contrasts. The set acts more as a diagnostic than a shortlist. Each response, whether acceptance, rejection, or passing interest, narrows what comes next without asking the user to explain why. People know what they do not want when they see it, even when they cannot say what they do want. Across a few turns, the right answer becomes obvious rather than chosen.
What makes it distinct
Decision Compression is not a filtering model. The user does not arrive with declared constraints: they emerge from the conversation. The nearest adjacent model, Elimination Sequencing, starts from the opposite assumption: the user already knows their limits and needs help applying them. One model induces preference. The other extracts it.
Where it breaks
The model fails when inventory is too thin to support more than one or two rounds — the system runs out of fresh options before the user finds their footing. It breaks where users already know what they want and find the back-and-forth slower than a direct search. It also breaks when users game it, pushing back not because the options are wrong but to see what else surfaces.