How Miser Score Works

Dimensions, not one number

Each tool defines a small set of scoring dimensions specific to that decision — for Buy vs Lease, that's monthly effective cost, ownership equity, flexibility, reliability, and mileage fit. Each dimension is calculated deterministically from your inputs, never estimated by AI.

Normalized against a fixed range, not just the other option

Every continuous dimension is compared against a fixed, realistic domain — a monthly cost is scaled against a $0–$3,000 range, for example — rather than only against the option you're comparing it to. This matters: without a fixed range, a $10 difference and a $10,000 difference between two options can end up looking identical, which would make the score meaningless for genuinely close comparisons. Ordinal ratings (like a 1–5 quality score) keep their own fixed meaning too — a 4/5 stays a 4/5 regardless of what the other option scores.

Weighted, and adjustable by your priorities

Each dimension carries a default weight (financial cost is usually weighted highest, but never so high that nothing else can matter), and you can rank what matters most to you — that ranking shifts the weights within a bounded range, so your priorities can meaningfully change a close result without letting a small preference override a large financial gap.

AI's role: zero, for the score itself

AI never calculates a dimension value, never sets a weight, and never determines a winner. The entire scoring pipeline — from your inputs to the final number — is deterministic code. AI's only role anywhere in the product is explaining a result that's already been calculated, using only the numbers the calculation produced.

See Miser Score applied to a decision you define yourself.